Undergraduate Research at the Swanson School of Engineering
University of Pittsburgh Swanson School of Engineering Undergraduate Research Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA 15261 USA Spring 2022
The image on the cover shows the average rCMRO2 following optogenetic stimulation in a VGAT-ChR2-YFP animal. (See page 68 by Andrew Toader, Departments of Electrical and Computer Engineering.) Please note that neither Ingenium nor the Swanson School of Engineering retains any copyright of the original work produced in this issue. However, the Swanson School does retain the right to nonexclusive use in print and electronic formats of all papers as published in Ingenium.
Ingenium 2022
Table of Contents Message from Dr. Vorp
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Message from Co-Editors-in-Chief
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Graduate Student Review Board – Ingenium 2022
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Assessing the impact of showerheads on effluent drinking water chemistry * Daniel P. Huffman, Sarah Pitell , Paige Moncure, Jill E. Millstone PhD, Sarah-Jane Haig PhD, Leanne M. Gilbertson PhD Department of Civil and Environmental Engineering, Department of Chemistry, Graduate School of Public Health, Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA, USA 7 u Statistical modeling of drug-induced proteomic adaptation to overcome fibroblast-mediated HER2 therapy resistance † Jacob C. McDonald, Ioannis K. Zervantonakisa Tumor Microenvironment Engineering Laboratory, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; UPMC Hillman Cancer Center, Pittsburgh, PA, USA 11
Preliminary development of a hemoadsorption device for removal of cell-free plasma hemoglobin Anna L. Maywar, Ryan A. Orizondoa, Nahmah Kim-Campbell Department of Bioengineering; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA; McGowan Institute for Regenerative Medicine
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Development and testing of a novel biomechatronic balance aid and rehabilitation device Nathaniel Mitrik, Alexandra Delazio, Goeran Fiedler, David Brienza Department of Rehabilitation Science and Technology, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA 57 u Effects of a feedback time delay in a megahertz-frequency, nonlinear resonator Joseph Mockler, Thomas Hinds, Nikhil Bajaj Department of Mechanical Engineering and Material Science, University of Pittsburgh, Pittsburgh, PA, USA 61
FPGA programming and PCB design for quantum dot singlephoton emitter Adnan Alagic, In Hee Lee Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA 15
Difference in excitatory and inhibitory neuron oxygen metabolism elucidated by intrinsic optical imaging and optogenetics in awake and anesthetized mice Andrew E. Toader, Alberto L. Vazquez Departments of Electrical and Computer Engineering, Radiology, Bioengineering, University of Pittsburgh, PA, USA
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Concept methodology and strength evaluation testing of a woven socket and socket fitting system Taylor Brightman, Neharika Chodapaneedi, Zachary Roy, Goeran Fiedler Department of Bioengineering, Department of Rehabilitation Science and Technology, University of Pittsburgh, PA, USA 19
Development of a Graphical User Interface to Facilitate Automated BioModel Selection for Synthetic Biology Gene Circuit Design Kristyn Usilton, Chueh Loo Poh Department of Bioengineering, University of Pittsburgh, PA, USA; Department of Biomedical Engineering, National University of Singapore
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Classification of shallow and deep sleep using electroencephalogram signals in real time Mark F. Ciora, Jijun Yin, Zhi-Hong Mao Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
Development of an infra-red imaging device to detect visceral arteries Oldrich Virag, Mohammad H. Eslami, David A. Vorp, Timothy K. Chung Department of Bioengineering, University of Pittsburgh, Pittsburgh PA; Division of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh PA; Department of Surgery; Department of Cardiothoracic Surgery; Department of Chemical and Petroleum Engineering; McGowan Institute for Regenerative Medicine; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, USA 74
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u Designing metal organic framework-based e-noses to detect lung cancer via volatile organic compounds emitted in breath Spencer C. Conaway, Christopher Wilmer, Brian Day Department of Chemical Engineering, University of Pittsburgh, PA, USA 26 u VOF modeling of annular gas-liquid flow regimes in horizontal pipes Joshua Dewald, Wai Lam Loh Mechanical Engineering Department, University of Pittsburgh, PA, USA Mechanical Engineering Department, National University of Singapore 32
New perspectives on clustered linear regression
Jared Lawrence, Jourdain Lamperski Department of Industrial Engineering, University of Pittsburgh, PA, USA 38
Magnetic guidance of fibrin gel encapsulated adipose-derived mesenchymal stem cells using iron nanoparticles Jason L. Zheng, Ande X. Marini, Timothy K. Chunga, Justin S. Weinbaum, David A. Vorp University of Pittsburgh Departments of Bioengineering, Surgery, Pathology, Cardiothoracic Surgery, Mechanical Engineering & Materials Science, and Chemical & Petroleum Engineering; McGowan Institute for Regenerative Medicine; Center for Vascular Remodeling & Regeneration, Pittsburgh, PA, USA 80
Generation of obese adipose tissues from induced pluripotent stem cells (iPSCs) Katelyn E. Lipa, Hang Lin Department of Bioengineering, Department of Orthopaedic Surgery, University of Pittsburgh, PA, USA 43 Simultaneous local and bulk polymer crystallization analysis using microfluidic dilatometry Ryan J. MacElroy and Sachin S. Velankar Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA, USA 48 * Reviewers’ Choice
†
Editors’ Choice
Category Definitions
u Computational Research—using computational techniques to address a scientific question Device Design—focusing on the development of a product or device Experimental Research—using laboratory methods to achieve a novel overarching experimental aim Methods—developing new techniques and tools for research and design Review—summarizes the current state of knowledge on a particular topic 3
A Message from the Associate Dean for Research As eloquently stated in “Pro Balbo,” delivered in 56 BCE by Marcus Tullius Cicero, one of the first theorists to discuss ingenium, “Constant practice devoted to one subject often outdoes both intelligence and skill (Assiduous usus uni rei deditus et ingenium et artem saepe vincit).” Here at the University of Pittsburgh Swanson School of Engineering we strive to focus on the methods best attuned to reveal and cultivate the unique capabilities of each of our students while helping to guide their ingenium (genius, innate talent) to the greater good.
David A. Vorp, Ph.D.
On behalf of the Swanson School of Engineering and U.S. Steel Dean of Engineering James R. Martin II, I proudly present the eighth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering, a compilation of articles representing the achievements of our exceptional undergraduate students and their 2021 summer research projects.
Despite another year of turbulence and uncertainty, our students once again showed that engineering is at the forefront of leading the way to change, adaptability, and flexibility. Going on more than two years of life during a pandemic, they have continued to thrive and have shown great resilience and perseverance in the face of adversity. They have again risen to the challenging way of life that we have grown to endure and embrace. As with each year and each edition of Ingenium, one thing remains the same– the notable and impressive academic and professional growth and development in our outstanding undergraduate students when given the opportunity to engage in scientific research. As always, our students took their skills, knowledge, resources, and information that they learned in their course work and applied it in a thoughtful way outside of the classroom. These students, the future of both our highly accredited institution and our world, will go on to become engineers, scientists, academics, physicians, or whatever else they set out to accomplish. They will, without a doubt, make incredibly significant impacts in the fields of technology, medicine, travel, space, and communication, just to mention a few. The student authors of the articles in this issue of Ingenium studied mostly under the guidance of faculty mentors in the Swanson School. At the conclusion of the summer research program, students were asked to submit abstracts summarizing the results of their research. The abstracts were reviewed by the Graduate Student Review Board (GSRB), and the authors of the highest-ranking abstracts were invited to submit full manuscripts for peer review by the GSRB for inclusion in this edition of Ingenium. Therefore, Ingenium serves as more than a record of our undergraduate student experience in research; it is also a practical experience for them in scientific writing and in the author’s perspective of the peer-review process. Additionally, it provides graduate students with an opportunity to experience the editorial review process and the reviewer’s perspective of the peer-review process. I would like to acknowledge the hard work and dedication of the coeditors in chief of this issue of Ingenium, Pierangeli Rodriguez De Vecchis and Qianyun ‘Gloria’ Zhang, as well as the design team at AlphaGraphics, the team in the Office of University Communications and Marketing, and Jaime Turek. This issue would not have been possible without the hard work of the graduate student volunteers who constitute the GSRB and who are listed by name in this issue. It is also altogether fitting to thank the faculty mentors and other coauthors of the reports included in this issue. I hope that you enjoy reading this edition of Ingenium and that the many talents of our students inspire the engineers of the future. Hail to Pitt!
David A. Vorp, Ph.D. Associate Dean for Research Swanson School of Engineering University of Pittsburgh
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Ingenium 2022
Message from the Co-Editors-in-Chief Greetings!
We are excited to present the eighth edition of Ingenium: Undergraduate Research at the Swanson School of Engineering. This volume features 17 articles from undergraduate students at the University of Pittsburgh’s Swanson School of Engineering (SSOE). The articles demonstrate how the talents and dedication of these students provide new perspectives in relevant scientific topics that concern our world today. Ingenium provides SSOE undergraduate students with an introduction to the scientific peer-review process and allows them to gain experience in communicating their research through written manuscripts. All manuscripts were reviewed by SSOE graduate students who kindly volunteered to provide detailed and valuable feedback, in a reciprocally rewarding experience of standing in the reviewer’s perspective of the process and helping others with their expertise. Qianyun (Gloria) Zhang
This year’s edition of Ingenium displays a sample of the diverse research that can be found in SSOE labs, and the opportunities undergraduate students are exposed to. We are so proud of all participating students and their hard work. They showed an incredible amount of creativity, critical thinking, dedication, resilience, and commitment to their research, as well as a desire to learn and grow in their abilities. We hope all authors, mentors and reviewers share our excitement and pride and that you enjoy all articles as much as we did! We would like to thank everyone in the production team of this year’s Ingenium volume. We deeply thank Dr. David Vorp, Associate Dean of Research, for his vision and continued commitment to this publication. We are also extremely grateful to Jaime Turek, for her advice, guidance, and continued support throughout the entire year. We also deeply appreciate all the mentors who guided the students’ research and the graduate students on the GSRB, for dedicating so much of their not-so-free time and sharing their knowledge to advice the authors. Finally, we would like to thank everyone in the Office of University Communications and Marketing and the AlphaGraphics team, especially, Rich Cichoski and John Kasunic for their amazing work with the production and design of this Ingenium edition.
Pierangeli Rodriguez De Vecchis
We have learned so much from everyone involved in Ingenium and we are honored to have served as Co-Editors-in-Chief for this year’s edition. It was truly a most rewarding experience to continue this Pitt SSOE tradition and to be part of an impressive research community that invests in students and their academic and personal development. We hope that, as you read this year’s articles, you let yourself be submerged in the wonderful research developments, as well as the passion and hard work shown by the authors. Congratulations to the authors and happy reading!
Qianyun (Gloria) Zhang Co-Editor-In-Chief
Pierangeli Rodriguez De Vecchis Co-Editor-In-Chief
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Graduate Student Review Board – Ingenium 2022 *Co-Editors-in-Chief Name...................................................................................................... Department Abigail Allen............................................................................................... Department of Bioengineering Elizabeth Bentley....................................................................................... Department of Bioengineering Kayla Bohlke.............................................................................................. Department of Bioengineering Ian Eder...................................................................................................... Department of Bioengineering Brittany Egnot............................................................................................ Department of Bioengineering Lily Farmerie.............................................................................................. Department of Bioengineering Dorota Jazwinska...................................................................................... Department of Bioengineering Maria Jantz................................................................................................. Department of Bioengineering Camille Johnson........................................................................................ Department of Bioengineering Nick Lagerman.......................................................................................... Department of Bioengineering Lucy Liang.................................................................................................. Department of Bioengineering Jennifer Mak.............................................................................................. Department of Bioengineering Ande Marini............................................................................................... Department of Bioengineering Katelin Omecinski..................................................................................... Department of Bioengineering Andrew Orenberg..................................................................................... Department of Bioengineering Adiya Rakymzhan...................................................................................... Department of Bioengineering Julie Rekant................................................................................................ Department of Bioengineering Andrea Sajewski........................................................................................ Department of Bioengineering Jordyn Ting................................................................................................. Department of Bioengineering Haoran Li.................................................................................................... Department of Civil and Environmental Engineering Qianyun Zhang *....................................................................................... Department of Civil and Environmental Engineering Yifan Deng.................................................................................................. Department of Chemistry Engineering Monica Shapiro......................................................................................... Department of Chemistry Engineering Zeineb Bouzid............................................................................................ Department of Electrical and Computer Engineering Avery Peiffer.............................................................................................. Department of Electrical and Computer Engineering Anisha Suri................................................................................................. Department of Electrical and Computer Engineering Hanie Eskandari........................................................................................ Department of Industry Engineering Andrew Baker............................................................................................ Department of Mechanical Engineering and Materials Science Manyu Chadha.......................................................................................... Department of Mechanical Engineering and Materials Science Ruikang Ding............................................................................................. Department of Mechanical Engineering and Materials Science Gregory Kinzler......................................................................................... Department of Mechanical Engineering and Materials Science Stephanie Liu............................................................................................. Department of Mechanical Engineering and Materials Science Tyler Paplham........................................................................................... Department of Mechanical Engineering and Materials Science Pierangeli Rodriguez De Vecchis*........................................................... Department of Mechanical Engineering and Materials Science Rafael Rodriguez De Vecchis................................................................... Department of Mechanical Engineering and Materials Science Tate Shorthill............................................................................................. Department of Mechanical Engineering and Materials Science Zeke Villarreal............................................................................................ Department of Mechanical Engineering and Materials Science Dihui Wang................................................................................................ Department of Mechanical Engineering and Materials Science Mo’ath Yousef............................................................................................ Department of Mechanical Engineering and Materials Science
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Ingenium 2022
Assessing the impact of showerheads on effluent drinking water chemistry Daniel P. Huffman a, Sarah Pitell a, Paige Moncure b, Jill E. Millstone b, Sarah-Jane Haig a, c, and Leanne M. Gilbertson a, d a
Department of Civil and Environmental Engineering, Department of Chemistry, c Graduate School of Public Health, d Department of Chemical and Petroleum Engineering
b
Daniel Huffman is a third-year environmental engineering student at the University of Pittsburgh. His interest in water chemistry and his passion for environmental justice led him to pursue research with the Gilbertson Group. Daniel P. Huffman
Dr. Gilbertson is an associate professor of Environmental Engineering. Her research group focuses on designing sustainable solutions to tackle global environmental and public health challenges. Leanne M. Gilbertson
Significance Statement
The findings of this study indicate that effluent water chemistry from three different showerhead designs does not significantly change over time despite explicit design modifications. Ongoing research to expand the dataset will inform efficacy of fixture designs with the intent of minimizing implications to users.
Category: Experimental Research Keywords: drinking water, water chemistry, premise plumbing, silver
ABSTRACT
The design of premise plumbing fixtures such as showerheads can impact effluent drinking water chemistry and quality. Further, microbes may exist as biofilms present in premise plumbing, which can lead to contamination of water. When water exits drinking water fixtures, microbes can detach from the biofilms and become present (in a planktonic phase) in the drinking water. Some microbes (opportunistic pathogens) are particularly concerning to populations with compromised immune systems. The prevalence of opportunistic pathogens in water has prompted the development of modified fixture designs to mitigate exposure. One design includes embedding silver in the showerhead material due to its antimicrobial properties. However, the effects of these silvermodified showerheads on effluent drinking water chemistry are not fully understood. This project assesses the impact of showerhead designs on drinking water chemistry by sampling and analyzing effluent drinking water from a custom full-scale shower rig with three showerhead designs: plastic conventional, metal conventional, and silver-modified plastic showerheads. The results of this study indicate that the type of showerhead impacts the lead concentration in the effluent water. Interestingly, the silver-modified plastic showerhead effluent water samples contained negligible silver concentrations, indicating minimal silver release from the composite material. Further analysis of the microbial concentrations within these water samples and of the fixture biofilms will help to indicate whether the silver present in these showerheads is effective in reducing opportunistic pathogen abundance and will inform the optimal fixture design for minimizing exposure.
I. INTRODUCTION
Premise plumbing (e.g., pipes, meters, heaters, and fixtures) in buildings comprises the final stage of drinking water transport to the consumer. Even though municipally treated drinking water leaving the treatment plant must meet federal water quality guidelines, its path to the consumer can change the water chemistry (e.g., long water stagnation periods due to plumbing lengths, building size, and dead-ends) [1]. One concern is the transmission of opportunistic bacterial waterborne pathogens via shower water. One of the best-known examples of such an opportunistic pathogen (OP) is Legionella pneumophila, the etiological agent of Legionnaires’ disease. Every year, thousands in the United States are hospitalized with this disease, a severe type of pneumonia that most strongly impacts immunocompromised patients [2]. Given the increase in incidence rates of pulmonary infections by OPs such as L. pneumophila in recent years [3, 4], there is an urgent need to develop infection mitigation strategies. Silver-modified showerhead fixtures are a recent technology designed and marketed to protect 7
2.2. Data Analysis
against waterborne microbes [5]. Researchers such as Salvatorelli et al. have concluded (using culturebased methods) that showerhead filters containing silver bacteriostatic agents are effective in reducing concentrations of L. pneumophila and other OPs in drinking water [6]. However, it is unknown whether the silver within these fixtures has unintended consequences on water chemistry. This study examines the effect of showerhead fixture design on effluent drinking water chemistry using a custom full-scale shower laboratory containing three stalls, each with three showerhead fixtures: a conventional plastic showerhead, conventional metal showerhead, and silver-modified plastic showerhead. Biweekly draw samples were taken from each of the nine shower fixtures for a sampling period of 10 weeks and water chemistry parameters were assessed, including metal concentrations, total organic carbon content, temperature, and pH. Due to the presence of silver within the silver-modified plastic showerhead fixtures, we anticipated a non-negligible amount of silver leaching from these showerheads over the sampling period, resulting in water samples with greater silver concentrations compared to the other showerhead samples. We likewise expected that—in accordance with these fixtures’ intended purpose of controlling biofilm growth—the silver-modified showerhead samples would have lower total organic carbon content compared to the other showerhead designs.
1.5 L of first draw (the water exiting the showerhead after turning it on) warm water (mean temperature 24.5 ± 3.0℃) were collected biweekly from each showerhead for a sampling period of 10 weeks. These draw samples were processed for various water chemistry parameters (Table 2) following standard methods for examination of water and wastewater [7]. During the two weeks preceding each sampling event, the showerheads were flushed daily for 8 minutes to simulate the average duration of a shower under real use [8].
2.3. ICP-MS Analysis
Inductively coupled plasma mass spectrometry (ICPMS) analysis was performed using an argon flow with a NexION spectrometer (PerkinElmer). A 5% (by volume) nitric acid matrix solution was prepared with nitric acid (Sigma-Aldrich, > 99.999% trace metal basis) diluted with NANOpure water (Thermo Scientific, ≥18.2 MΩ). Water samples were collected and acidified with nitric acid prior to analysis, following EPA 200.8 methodology. Unknown Ag and Pb concentrations were determined by comparison to a 7-point standard curve with a range of 1-100 ppb (1, 5, 10, 20, 30, 50, and 100 ppb prepared by volume) from a silver standard for ICP (Fluka, TraceCERT 1,001 ± 2 mg/L Ag in HNO3) and a lead standard for ICP (Sigma, TraceCERT 1,001 ± 2 mg/L Pb in HNO3) diluted in the 5% nitric acid matrix. All standards were measured 5 times and averaged, while all unknown samples were measured in triplicate and averaged. A 5-minute flush time with 5% nitric acid matrix was used between all runs, and a blank was analyzed before each unknown sample to confirm removal of all residual metals.
2. METHODS 2.1. Shower Lab Design
Three types of showerheads were tested in triplicate in a full-scale shower laboratory—a conventional acrylonitrile butadiene styrene (ABS) showerhead (hereafter referred to as conventional plastic), a conventional metal showerhead, and an ABS showerhead embedded with silver (hereafter referred to as silver-plastic). The locations of these showerheads were randomized across the three shower stalls to prevent sampling bias and are provided in Table 1.
2.4. Statistical Analysis Box and whisker plots were generated to display the collected data by showerhead type over time. MANOVA and ANOVA tests were employed to evaluate the combined and individual influence of showerhead type, stall number, and sample age on each measured
Table 1. Custom Shower Rig Design Stall 1
Stall 2
Stall 3
Fixture A
Conventional Metal
Conventional Plastic
Conventional Plastic
Fixture B
Silver-Plastic
Conventional Metal
Conventional Metal
Fixture C
Conventional Plastic
Silver-Plastic
Silver-Plastic
Table 2. Methods of Water Chemistry Analysis Parameter(s)
Method
Units of Measurement
Total Organic Carbon (TOC)
Shimadzu TOC-L Analyzer
mg/L
Total Ag and Pb concentrations
Perkin Elmer NexION 300 ICP-MS
ppb
Temperature and pH
Hanna portable temperature and pH meter
°C and pH
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Ingenium 2022
parameter, respectively. Differences in the value of each parameter between every pair of showerhead types were assessed using paired Wilcoxon tests. Plots and statistical tests were produced using the statistical package R, version 4.1.0, and the ggplot2, pacman, tibble, cowplot, and stats libraries. A p-value of ≪0.05 was used as the threshold for significance for all statistical tests.
3. RESULTS
Box and whisker plots were prepared for the total organic carbon (TOC), total silver concentration, and total lead concentration data according to Section 2.3 (Figure 1).
3.1. Data Trends
TOC concentration trends increase over time from weeks 0 to 4 and again from weeks 6 to 10 (Figure 1a). Interestingly, the total concentrations of silver were low for all showerheads, including the silverplastic heads (Figure 1b). The conventional metal showerheads tended to produce the greatest total lead concentrations (Figure 1c).
3.2. Results of Statistical Analysis
Statistical analysis revealed that sample age had a significant influence on TOC, with concentrations tending to increase over time (Figure 1a). Showerhead type and stall number did not have a significant impact on TOC, indicating that the factors influencing TOC concentrations were independent from showerhead type and location within the experimental setup. Showerhead type did significantly influence total lead concentrations, with water samples from the metal showerheads containing significantly higher lead (mean concentration 1.385 ± 1.009 ppb). All silver concentrations were below the limit of quantification (LOQ) of 1 ppb and hence a statistical difference between types of showerheads could not be assessed.
4. DISCUSSION 4.1. Total Organic Carbon
TOC is a measure of the organic matter present in a water sample. Since the reaction of organic compounds with chlorine may produce hazardous disinfection byproducts, drinking water treatment includes processes to remove TOC prior to chlorination [9]. Consequently, increases in TOC within our shower water samples may reflect the growth and sloughing off of biofilm over time within the showerheads and hoses. The lack of a significant relation between showerhead type and TOC may suggest that microbial concentrations were similar across all types of showerheads, but follow-up microbial analysis is needed to verify this.
4.2. Metal Analysis
Metals in drinking water may be present in the source water (e.g., groundwater and surface water) or introduced in various ways during treatment and distribution (e.g., leaching from pipes within the distribution system or premise plumbing). The dissolution and release of metals are influenced by water parameters such as pH and temperature [10]. However, in our system, we found no correlation between these parameters (data not shown) and effluent metal concentrations. This suggests that elevated lead concentrations result from the showerhead itself.
4.2.1. Total Silver Concentration Figure 1. Box-and-whisker plots of chosen water chemistry parameters. Within any sampling week, each colored box corresponds to a type of showerhead. Each box is constructed from three points corresponding to the three replicates of that showerhead type, one located in each stall. Data points are offset horizontally for ease of visualization; all time values are whole integers corresponding to the sampling week (weeks 0, 2, 4, 6, 8, and 10). Horizontal dashed lines depict limits of quantification for ICP-MS analysis of these metals. A. Box-and-whisker plot of TOC concentrations over time in milligrams per liter, TOC = total organic carbon; B. Box-andwhisker plot of total Ag concentrations over time in parts per billion, Ag = silver; C. Box-and-whisker plot of total Pb concentrations over time in parts per billion, Pb = lead.
Since all the total silver concentrations were below the LOQ of 1 ppb, silver leaching from these showerheads was considered negligible. Likewise, silver concentrations across all samples were below the EPA’s secondary standard of 0.10 mg/L (100 pb) for drinking water, further indicating that silver leaching from these fixtures had minimal impact on water quality or appearance [11].
4.2.2. Total Lead Concentration
Total lead concentrations were low overall (ranging from 0.01 ppb to 3.70 ppb). The metal showerhead samples consistently exhibited the greatest concentrations (mean 1.385 ± 1.009 ppb), indicating 9
that lead may have been leaching from these showerheads. While lead concentrations in the metal showerhead samples were above the EPA’s maximum contaminant level goal of zero, the greatest observed lead concentration of 3.70 ppb was still below the action level of 15 ppb provided in the EPA’s Lead and Copper Rule (LCR) [12]. It should however be stressed that the LCR does not apply to this shower system as it only pertains to cold water.
4.2.3. Temperature and pH
Although first draw water temperature fluctuated over the sampling period (ranging from 20.6℃ to 29.9℃, one outlier at 37.2℃), the temperatures across showerhead samples were similar within each week. Likewise, the pH of the water samples largely followed the same trends across showerhead fixtures and varied only with time (ranging from 6.42 to 7.66 in weeks 2-10; a different pH probe was used in week 0). Since there was minimal difference in both temperature and pH across showerhead type within each week, it is unlikely that these parameters were responsible for the significant differences in lead concentration observed across head types.
5. CONCLUSIONS
The collective results indicate that the type of showerhead, namely the type of material, can impact effluent water chemistry. ICP-MS analysis reveals that the leaching of certain metals (e.g., lead) from the conventional metal showerheads may be a likely mechanism by which this type of showerhead can influence water chemistry during regular use. In contrast, the low concentrations of silver present in effluent water samples indicate that the leaching of silver from the silver-plastic heads is negligible. Ongoing research is investigating the concentrations of silver within the silver-plastic showerhead fixtures to confirm its presence and compare biofilm growth across showerhead types. Even though silver was detected in the water samples at concentrations below the LOQ, its presence in the polymer may influence biofilm formation on the internal surface of the showerhead and hose. Additionally, future experiments will include alternate showerhead designs containing greater proportions of silver, which is expected to result in more significant impacts on water chemistry. In conjunction with the water chemistry analysis conducted in this study, knowledge of how these showerhead fixtures interact with opportunistic pathogens will allow for a comprehensive understanding of the impact of showerhead type on effluent water chemistry and inform the optimal showerhead fixture design.
6. ACKNOWLEDGMENTS
This project was conducted under the guidance of Jamie Mastropietro, who collected water samples and performed TOC analysis during sampling events.
Thanks also go to Dr. Janet Stout and Dr. Julianne Baron for their guidance and direction. We thank Dr. Daniel J. Bain for his generosity in using his ICP-MS. We also thank Dr. David Malehorn and Charles Hager for their assistance. Daniel Huffman was supported by the Kennametal Internship Fellowship and through funding from the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. This project was also supported by NSF CBET Award No: 1935378.
REFERENCES [1] National Research Council. “Alternatives for Premise Plumbing.” Drinking Water Distribution Systems: Assessing and Reducing Risks, 2006, 316-340. [2] Fields et al. “Legionella and Legionnaires’ Disease: 25 Years of Investigation. Clinical Microbiology Reviews, 2002, 506-526, doi: 10.1128/CMR.15.3.506-526.2002. [3] Barskey, et al. Legionnaires’ Disease Surveillance Summary Report, U.S. 2016-2017. CDC. [4] Benedict et al. “Surveillance for Waterborne Disease Outbreaks Associated with Drinking Water — United States, 2013-2014.” MMWR Morb Mortal Wkly Rep, 2017, 66. [5] “Are You Showering in Microbes?” Microban, https://www.microban.com/blog/are-youshowering-in-microbes. [6] Salvatorelli et al. “Effectiveness of installing an antibacterial filter at water taps to prevent Legionella infections.” Journal of Hospital Infection, 2005, 270271, doi: 10.1016/j.jhin.2005.04.012. [7] Rice et al., eds. Standard Methods for Examination of Water and Wastewater. 22nd ed., Rice. [8] “UK sustainable shower study.” Unilever, 2011, https://www.unilever.co.uk/news/pressreleases/2011/uk-sustainable-shower-study.html. [9] “TOC Removal in Drinking Water Applications.” Hach, https://www.hach.com/asset-get.download. jsa?id=51350168613. [10] Chowdhury et al. “Heavy metals in drinking water: Occurrences, implications, and future needs in developing countries.” Science of The Total Environment, 476-488, Nov. 2016, doi: 10.1016/j. scitotenv.2016.06.166. [11] “Drinking Water Regulations and Contaminants.” EPA, https://www.epa.gov/sdwa/drinking-waterregulations-and-contaminants. [12] “National Primary Drinking Water Regulations.” EPA, https://www.epa.gov/ground-water-and-drinkingwater/national-primary-drinking-water-regulations.
10 Undergraduate Research at the Swanson School of Engineering
Statistical modeling of drug-induced proteomic adaptation to overcome fibroblast-mediated HER2 therapy resistance Jacob C. McDonalda and Ioannis K. Zervantonakisa, b Tumor Microenvironment Engineering Laboratory, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA a
b
UPMC Hillman Cancer Center, Pittsburgh, PA
Jacob McDonald is a senior bioengineering student at the University of Pittsburgh with a focus in cellular engineering. After graduating, he plans to further his education and bioengineering research by attending graduate school. Jacob McDonald
Ioannis Zervantonakis
Ioannis Zervantonakis (Dr. Z) was born and raised in Heraklion, the capital city of the island of Crete in Greece. Dr. Z’s research interests include developing and applying engineering approaches to understand tumorstroma interactions driving metastasis and drug resistance.
Significance Statement
Interactions between tumor cells and stromal fibroblasts in the tumor microenvironment have been shown to mediate drug resistance in some HER2 overexpressing breast cancers. This study utilizes statistical modeling to identify signaling pathways within tumor cells that are critical to fibroblastmediated drug resistance and rational targets to restore treatment sensitivity.
Category: Computational Research
Keywords: breast cancer, tumor microenvironment, fibroblasts, drug resistance
Ingenium 2022
ABSTRACT
Multiple targeted therapies have been developed for the treatment of HER2 overexpressing (HER2+) breast cancers, such as the dual HER2/EGFR kinase inhibitor lapatinib, but many patients eventually develop resistance to these treatments. One proposed cause of HER2 therapy resistance is the interaction between tumor cells and stromal fibroblasts in the tumor microenvironment, which has been reported to activate signaling pathways in HER2+ tumor cells that lead to a decrease in drug sensitivity. Here, we utilize partial least squares regression (PLSR) modeling to identify proteomic predictors of lapatinib resistance in fibroblast-protected tumor cells. We then validate our model predictions by inhibiting these proteins in combination with lapatinib to assess their efficacy as targets to overcome fibroblast-mediated therapy resistance. Our PLSR model suggests that fibroblasts reduce lapatinib sensitivity in HER2+ breast cancer cells through the activation of alternative receptor tyrosine kinases and cell cycle proteins. Furthermore, we demonstrate that interrupting either RTK signaling via SHP-2 inhibition or cell cycle checkpoints via Chk1 inhibition is sufficient to restore sensitivity to fibroblastprotected tumor cells. Together, our findings suggest that our PLSR model is capable of identifying rational protein targets for combination therapy to overcome fibroblast-mediated lapatinib resistance in HER2+ tumor cells.
1. INTRODUCTION
HER2 overexpressing (HER2+) breast cancer accounts for ~20% of all breast cancer cases [1]. The HER2 receptor is a receptor tyrosine kinase in the epidermal growth factor receptor family (ErbB), and heterodimerization of HER2 with other members of the ErbB family leads to signal transduction through the PI3K/Akt and MAPK survival pathways [2]. As a result, overexpression of HER2 can lead to increased cancer cell survival, proliferation, and invasion [2]. Multiple targeted therapies have been developed to treat HER2+ breast cancer, such as the dual HER2/EGFR kinase inhibitor lapatinib. Lapatinib acts by binding to the kinase domain of the HER2 receptor, preventing its autophosphorylation and therefore its ability to transduce pro-survival signaling [3]. However, even though lapatinib treatment may initially be successful in treating HER2+ breast cancers, many patients will eventually develop resistance to these treatments [3]. Several mechanisms of resistance to lapatinib treatment have been proposed, such as the activation of compensatory signaling pathways through other kinases, mutation of the HER2 kinase domain, or gene amplification [3]. Another proposed cause of HER2 therapy resistance is the interaction between tumor cells and stromal fibroblasts in the tumor microenvironment, which can occur through direct cell-cell contact or through the secretion of soluble
11
factors [4]. These tumor-fibroblast interactions have been reported to activate signaling pathways in HER2+ breast tumor cell lines, such as the mTOR and JAK2/ STAT3 pathways, which leads to a decrease in lapatinib sensitivity [5, 6]. Recent work in identifying fibroblast-mediated lapatinib resistance mechanisms has utilized differential expression analyses and the identification of protein and gene resistance signatures [5, 6]; however, incorporating the use of multivariate regression models can further shed light on the complex proteomic adaption that leads to this observed resistance [7]. Here, we aim to identify proteomic predictors of lapatinib resistance in these fibroblast-protected HER2+ breast cancer cells by performing statistical modeling using partial least squares regression (PLSR). We then validate the model predictions by inhibiting select proteins in combination with lapatinib treatment to evaluate whether lapatinib sensitivity can be restored in fibroblast-protected cancer cells.
2. METHODS
2.1 Cell Culture
A panel of ten different HER2+ breast cancer cell lines was used in this study. The selected cell lines have been shown to exhibit a wide range of different lapatinib sensitivities both alone and when cocultured with AR22 fibroblasts [5]. Cells were grown in 96 well plates in either monoculture, transwell tumor-fibroblast coculture, or with fibroblast conditioned medium and treated with either 0.1µM lapatinib or a dimethyl sulfoxide (DMSO) control for 96 hours. For the tumorfibroblast coculture, AR22 fibroblasts were physically separated from the tumor cells using transwell filters, allowing for the exchange of secreted factors between the fibroblasts and tumor cells without cell-cell interactions due to physical contact. After 48 hours of treatment in each culture condition, protein expression in the tumor cells was quantified using reverse phase protein arrays (RPPA) as previously described [8], and tumor cell viability was tracked using live cell analysis. Data for these experiments was previously collected and further details of the experiments performed are provided in the supplementary methods in [5].
2.2 Building the PLSR Model
To identify proteomic predictors of lapatinib resistance, partial least squares regression of the log2 fold change in cell number vs. the log2 change in expression of each protein after 48 hours of lapatinib treatment was performed using R package pls. Model fit was assessed using the fraction of variance captured by the training data (R2), whereas the predictive ability of the model was evaluated using the fraction of variance predicted through ten-fold cross validation (Q2) [7]. The number of components selected to build the model was chosen in an effort to maximize R2 and Q2 without overfitting the training data.
2.3 Combination Therapy
The most significant predictors of post-treatment viability were identified based on their variable importance in the projection (VIP) scores [9] and selected for follow-up combination therapy experiments. Select protein pathways were inhibited alone and in combination with lapatinib treatment in EFM192 tumor cells cocultured with fibroblasts for 96 hours to assess their ability to restore lapatinib sensitivity in fibroblast-protected tumor cells. The SHP-2 inhibitor SHP099 was used to target RTK signaling, while the Chk1 inhibitor AZD7762 was used to target cell cycle regulation. Inhibitor concentrations of 0.03, 0.3, and 3.0µM were used for both SHP099 and lapatinib single-agent treatments, whereas 0.09, 0.9, and 9.0µM were used for AZD7762 treatment. In combination treatment experiments, a lapatinib dose response (0.03, 0.3, and 3.0µM) was performed with a fixed concentration of either 3.0µM SHP099 or 0.9µM AZD7762. These inhibitor concentrations were selected to effectively block each target based on concentrations reported in the literature [10, 11].
3. RESULTS
A three component PLSR model generated from the protein expression data was accurately able to capture variance in the viability of HER2+ cell lines (R2 = 0.93, Fig 1) and had moderate predictive ability (Q2 = 0.51).
Figure 1: Model Fit – Tumor cell viability predicted by the PLSR model vs. measured viability for monoculture, transwell tumor-fibroblast coculture (transAR22), and fibroblastconditioned medium (AR22CM) culture conditions. The PLSR model is able to accurately capture the variance in viability for all three culture conditions. R2 = 0.93.
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Proteins with the highest VIP scores (Fig 2) consisted of proteins involved in receptor tyrosine kinase (RTK) signal transduction (DDR1, FRS2α, SHP-2, Src) and cell cycle proteins (Cyclins B1 and D3, PLK1, CDK1, etc.). Therefore, these pathways were chosen as targets for follow-up combination therapy experiments in the fibroblast-protected cell line EFM192 as proof of concept.
Figure 2: VIP Scores - Top 40 most significant model predictors based on VIP score. Blue bars indicate proteins whose inhibition positively correlates with a reduction in cell viability, while red bars indicate a negative correlation. The most significant predictors include proteins involved in receptor tyrosine kinase (RTK) signal transduction (DDR1, FRS2α, SHP-2, Src) and cell cycle regulation (Cyclins B1 and D3, PLK1, CDK1, etc.)
Inhibition of RTK signaling through a SHP-2 inhibitor (treatment with SHP099) in combination with lapatinib resulted in a 30% reduction in post-treatment cell number compared to lapatinib alone at the highest treatment dose (Fig 3a, p = 0.013 Student’s t-test), while blockade of the cell cycle Chk1 checkpoint (treatment with AZD7762) resulted in a 22% reduction in cell number (Fig 3b, p = 0.011 Student’s t-test). These results make SHP-2 and Chk1 promising targets for future studies to optimize lapatinib-based combination therapy.
Figure 3: Combination Therapy – Dose response curves for EFM192 tumor-fibroblast coculture treated with inhibitors (A) SHP099 and (B) AZD7762 as single agents and in combination with lapatinib. Both combination treatments result in a reduction in viability at the highest dose compared to lapatinib alone.
4. DISCUSSION
Our PLSR model was able to accurately encompass the variance in viability between 10 different HER2+ cell lines and three different culture conditions, suggesting that both tumor-autonomous and fibroblast-mediated proteomic responses can be represented using a multivariate linear model. More specifically, proteins involved in RTK signal transduction and cell cycle signaling were identified as significant predictors of the lapatinib-induced reduction in tumor cell viability, both of which have been reported to contribute to lapatinib resistance in HER2+ cancers [12]. Experimental validation of the PLSR model predictions through combination treatment revealed that inhibition of RTK signaling through SHP-2 or target the cell cycle checkpoint Chk1 in combination with lapatinib could re-sensitize fibroblast-protected tumor cells to lapatinib treatment. However, these combination treatments were only tested in the tumor cell line EFM192, so further testing on other HER2+ breast cancer cell lines is needed. Nonetheless, these findings are consistent with a recent study by Fernández-Nogueira et al. who found that fibroblast-mediated lapatinib resistance can be conferred through the activation of RTK signaling in HER2+ tumor cells via FGFR2 and Src [13]. Our experimental results in combination with the results of this study validate that our PLSR model is capable of identifying rational protein targets for lapatinib-based combination therapy to overcome fibroblast-mediated lapatinib resistance. However, a limitation of our current PLSR model is that it includes proteomic adaptations for both tumor cell autonomous (e.g. constitutively active MTOR signaling) and fibroblast-mediated signaling changes. In other words, it is not possible to directly determine whether the identified proteomic predictors contribute to tumor-autonomous inherent lapatinib resistance, fibroblast-mediated lapatinib resistance, or both without follow-up experimentation. One way to overcome this limitation would be to instead regress the change in tumor cell viability from monoculture to coculture conditions against the changes in lapatinibtreated protein expression from monoculture to coculture conditions. This would identify which changes in protein expression are most predictive of fibroblastmediated changes in tumor cell viability, making it unique to fibroblast-mediated resistance mechanisms. However, given the heterogeneity between HER2+ tumor cell lines it would be advantageous to employ a larger proteomic dataset (different lapatinib doses and coculture times) to build this proposed model.
5. CONCLUSION
It is evident that interactions between cancer cells and the tumor microenvironment play a significant role in sensitivity to drug treatments. Here, we have shown that our PLSR model is capable of identifying predictors of tumor cell viability that can be targeted
13
to overcome fibroblast-mediated lapatinib resistance, such as SHP-2 in RTK signaling and Chk1 in cell cycle regulation. In the future, we hope to modify the model to be more applicable to fibroblast-specific mechanisms of resistance. This will allow us to identify effective combination therapies to restore lapatinib sensitivity and personalize treatment based on the composition of the tumor microenvironment.
6. ACKNOWLEDGMENTS
Funding was provided by the Department of Bioengineering and the Office of the Provost at the University of Pittsburgh. I would like to thank Matthew Poskus and other fellow lab members for providing helpful discussion and advice during the completion of this project. We thank Dr. Gordon Mills and Dr. Yiling Lu for the RPPA studies.
REFERENCES [1] M. A. Owens, B. C. Horten, and M. M. Da Silva, “HER2 amplification ratios by fluorescence in situ hybridization and correlation with immunohistochemistry in a cohort of 6556 breast cancer tissues,” Clin. Breast Cancer, vol. 5, no. 1, pp. 63–69, 2004, doi: 10.3816/CBC.2004.n.011. [2] N. Iqbal and N. Iqbal, “Human Epidermal Growth Factor Receptor 2 (HER2) in Cancers: Overexpression and Therapeutic Implications,” Mol. Biol. Int., vol. 2014, pp. 1–9, 2014, doi: 10.1155/2014/852748. [3] V. D’Amato et al., “Mechanisms of lapatinib resistance in HER2-driven breast cancer,” Cancer Treatment Reviews, vol. 41, no. 10. W.B. Saunders Ltd, pp. 877–883, Dec. 01, 2015, doi: 10.1016/j. ctrv.2015.08.001.
[8] R. Tibes et al., “Reverse phase protein array: Validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells,” Mol. Cancer Ther., vol. 5, no. 10, pp. 2512–2521, Oct. 2006, doi: 10.1158/1535-7163.MCT-06-0334. [9] I. G. Chong and C. H. Jun, “Performance of some variable selection methods when multicollinearity is present,” Chemom. Intell. Lab. Syst., vol. 78, no. 1–2, pp. 103–112, Jul. 2005, doi: 10.1016/J. CHEMOLAB.2004.12.011. [10] Y. N. P. Chen et al., “Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases,” Nat. 2016 5357610, vol. 535, no. 7610, pp. 148–152, Jun. 2016, doi: 10.1038/ nature18621. [11] S. D. Zabludoff et al., “AZD7762, a novel checkpoint kinase inhibitor, drives checkpoint abrogation and potentiates DNA-targeted therapies,” Mol. Cancer Ther., vol. 7, no. 9, pp. 2955–2966, Sep. 2008, doi: 10.1158/1535-7163.MCT-08-0492. [12] H. Shi, W. Zhang, Q. Zhi, and M. Jiang, “Lapatinib resistance in HER2+ cancers: latest findings and new concepts on molecular mechanisms,” Tumor Biol. 2016 3712, vol. 37, no. 12, pp. 15411–15431, Oct. 2016, doi: 10.1007/S13277-016-5467-2. [13] P. Fernández-Nogueira et al., “Tumor-associated fibroblasts promote HER2-targeted therapy resistance through FGFR2 activation,” Clin. Cancer Res., vol. 26, no. 6, pp. 1432–1448, Mar. 2020, doi: 10.1158/1078-0432.CCR-19-0353.
[4] E. Sahai et al., “A framework for advancing our understanding of cancer-associated fibroblasts,” Nature Reviews Cancer, vol. 20, no. 3. Nature Research, pp. 174–186, Mar. 01, 2020, doi: 10.1038/ s41568-019-0238-1. [5] I. K. Zervantonakis et al., “Fibroblast–tumor cell signaling limits HER2 kinase therapy response via activation of MTOR and antiapoptotic pathways,” Proc. Natl. Acad. Sci. U. S. A., vol. 117, no. 28, pp. 16500–16508, Jul. 2020, doi: 10.1073/ pnas.2000648117. [6] A. Marusyk et al., “Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes,” Cancer Res., vol. 76, no. 22, pp. 6495– 6506, Nov. 2016, doi: 10.1158/0008-5472.CAN-161457. [7] P. K. Kreeger, “Using partial least squares regression to analyze cellular response data,” Sci. Signal., vol. 6, no. 271, Apr. 2013, doi: 10.1126/SCISIGNAL.2003849. 14 Undergraduate Research at the Swanson School of Engineering
Ingenium 2022
FPGA programming and PCB design for quantum dot single-photon emitter Adnan Alagic and In Hee Lee Department of Electrical and Computer Engineering As a senior computer engineering student with a concentration in autonomous systems, Adnan aspires to work in the robotics industry. His long-term goal is to become a control systems theorist. Adnan Alagic
In Hee Lee
Inhee Lee received his PhD degree in electrical engineering from the University of Michigan in 2014 and then worked as an assistant research scientist until 2019. Later that same year, he joined the University of Pittsburgh as an assistant professor. His research interests include energyefficient circuit design and miniature system design.
Significance Statement
Quantum decoherence manipulates qubit information and thus decreases quantum computing reliability. This can be solved through sending qubits to remote destinations using a single-photon source module. As part of this module, this research used the flexibility of FPGAs to design electronic hardware that generates voltage pulses.
Category: Device Design
Keywords: quantum decoherence, voltage pulse, hardware description Abbreviations: quantum dot (QD), single-photon source (SPS), field programmable gate array (FPGA), printed circuit board (PCB), voltage level converter (LC)
ABSTRACT
Quantum decoherence manipulates the information stored within qubits, which in turn may create incorrect results in quantum computer calculations. One method to give qubits protection against quantum decoherence is to send them to remote destinations. More specifically, quantum dots (QDs) can be excited by some module, releasing single photons that carry qubits to destinations less prone to quantum decoherence. This research aimed to create a voltage pulse generator as part of this module. The hardware description of the generator was written to a Field Programmable Gate Array (FPGA) in Verilog. Inputs of the generator include a request to begin generation, as well as inputs specifying the characteristics of the output pulse. A Printed Circuit Board (PCB) was then designed as a hat for the FPGA development board, connecting peripherals that allow for integration with the goal device. The results were a functional FPGA hardware description and a PCB ready for manufacturing and integration testing.
1. INTRODUCTION
Quantum computing is a niche field dedicated to solving problems that would take traditional computing too long. This is due to the structure of their building block: the quantum bit (qubit). Rather than discretely holding a ‘0’ or ‘1’ in the manner of traditional bits, qubits hold a linear combination of both. More specifically, a qubit holds two ‘amplitudes’ that correspond with the probability of it being a ‘0’ or ‘1’. From this, exponentially more information can be stored in a quantum computing system and therefore can solve problems that would otherwise involve too many individual steps [3]. A challenge in quantum computing that may stand in the way of this progress is the unwanted introduction of “quantum decoherence” – which manipulates the information stored in qubits [4]. This can create issues such as undesired results from a quantum computation. A solution to this problem, however, can be to send qubits to remote destinations via single photons [1],[2]. At these remote destinations, the qubits are less likely to be affected by their environment and can then deliver more reliable results. Therefore, a single-photon source (SPS) module is necessary. In the SPS, quantum dots (QDs) are excited by some voltage, releasing entangled photon pairs. Though QDs and qubits share a root word, it should be noted that QDs are not involved in quantum computing and are instead used for photon generation via excitation by voltage. This voltage excitation is the motivation for this research. A module needs to be designed such that it may generate voltage pulses for carrier injection into QDs. Requirements for this device include being able to control the width of the pulses, as well as the time delay before the pulse is generated. Additionally, the 15
minimum resolution for both of these needs to be as small as the technology used allows. To go along with the pulse generator, a counter module is needed such that the functionality of the pulse generator can be verified. Here, the counter should measure the two parameters of the pulse generator module: the pulse width and delay. We seek to use an FPGA development kit (a flexible device that creates digital logic circuits via software) to write the functionality of the pulse generator and counter. Once the FPGA is configured, we need to create a custom PCB to connect peripherals to it – including voltage level converters that allow communication to and from the SPS module.
the pulses coming from another system such as System B. In this case, System B is merely a copy of System A and therefore is only partially shown.
2.2 FPGA Hardware Description
The FPGA chosen for this project was the Alchitry Cu, a lightweight development board with a 100 MHz clock. The clock speed has a minimum 10 ns period, which was used as the resolution for the pulse width and delay. The hardware description language for the board was written in Verilog. Figure 2 illustrates the two functions of the FPGA: generating voltage pulses and counting them.
In short, the final design should take the form of a device that accepts input to generate a pulse – followed by the device generating voltage pulses that follow pulse width and delay parameters. These pulses can then be measured by a counter to check performance. Integrating this module with the SPS module, we seek that it will play its part in pushing quantum computing forward through reducing quantum decoherence and therefore increasing reliability.
2. METHODS
2.1 Pulse Generator & Counter Design
Figure 1: Block diagram showing the inputs and outputs of the FPGA. The blue wire shows the FPGA sending output pulses to the QDs, the red wire accepts requests from an unspecified module to generate pulses, and the green wire counts the pulses coming from another system such as System B. In this case, System B is merely a copy of System A and therefore, is only partially shown.
Figure 2: Hardware description of the pulse generator and counter. When a request is received, the pulse generator creates pulses with specified parameters and the counter counts their width and delay.
In Figure 2, the inputs and outputs are on the outermost left and right respectively. The pulse generator does not produce any pulses unless the “Request” input is made high. If it is high, the “Delay Control” input determines how many clock cycles are run before the request signal is forwarded to the pulse width block (Note: The “No Delay?” block and OR gate are included for the simple function of bypassing the “Pulse Delay” block in the case of “Delay Control” having the value of 0). The “Pulse Width” block then makes the “Pulse Output” high for the number of clock cycles specified by the “Width Control”. The “Delay Control” and “Width Control” inputs are both four bits wide, meaning that the highest number of clock cycles that can be ran for their respective functions is fifteen. These clock cycle amounts grant flexibility after integration with the SPS module. The counter uses the “Request” input and “Pulse Output” as references when counting the pulse delay and width respectively. The performance of this hardware description was verified using a combination of the FPGA board’s LEDs, an oscilloscope, and the counter.
Looking at System A, Figure 1 shows LCs (voltage level converters) allowing communication between an FPGA and external modules despite varying voltages. The blue wire shows the FPGA sending output pulses to the QDs, the red wire accepts requests from an unspecified module to generate pulses, and the green wire counts
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Figure 3: Test bench of the pulse generator - the switches correspond with the control parameters and the button is the request input.
2.3 PCB Design
A PCB was needed to connect the FPGA to components that assist in integration. The first decision was whether to have the PCB house the FPGA chip itself, or to connect it to the entire development board. For our application, it became clear that it was more time and cost effective to create a PCB that acts as a ‘hat’ to the development board than to work through incorporating the FPGA chip separately.
Figure 5: Circuit Schematic of the PCB hat on Altium Designer. The female headers (ports) are featured in the center with the Level Converters (LCs) on the perimeter. These work together to communicate with the FPGA and other external devices.
With the block diagram and the structure of the PCB in mind, the circuit schematic was drafted in Altium Designer. Two components were necessary in the design: The LCs and female headers. With the LCs explained in section 2.1, the female headers serve the function of connecting the FPGA board, LCs, and input/ output modules together through external wires.
Figure 4: Rough sketch of the PCB hat concept showing how the PCB may attach on top of the FPGA development kit and be connected with external wires.
As sketched in Figure 4, the hat attaches to the outermost posts of the development board with external wires connecting the two together.
Figure 6: PCB hat design rendering using Oshpark’s GERBER viewer. The smaller footprints are for the Level Converters (LCs) and the larger footprints are for the female headers (ports).
When translating the schematic to a PCB layout, the components were laid out such that the width of the board would be minimized. This was done to allow the board to be attached to the FPGA board where it should remain relatively flush and not cause a shift in weight.
17
3. RESULTS
may be necessary to use an FPGA with a faster clock. Assuming integration between the PCB and the FPGA board is successful, then the pulse generator would be integrated into the SPS module and excite quantum dots as described in the introduction.
5. CONCLUSIONS
QDs within the SPS module require excitement incurred by some voltage. This creates demand for a voltage pulse generator module. Using block diagrams highlighting functionality, the flexibility of FPGAs, and careful PCB design, we have created the core features of this module. Assuming successful integration between the FPGA board and the PCB hat, this module will serve to improve the reliability of quantum computing.
6. ACKNOWLEDGEMENTS Figure 7: Example output read by an oscilloscope resulting from a delay input of two clock cycles and a pulse width input of three clock cycles. The yellow waveform is the request input and the green waveform is the resulting output pulse.
The core of the voltage pulse generator was successfully created – meaning the FPGA functionality was tested and verified. Figure 7 demonstrates an example output resulting from inputting a delay of two clock cycles and a pulse width of three clock cycles. The yellow and green waveforms represent the input and output respectively, where the test input is one clock cycle long. As it can be seen, the green waveform arrives two clock cycles away from the input and lasts for three clock cycles. This is only one of the 256 possible outputs derived from the pulse width and delay inputs having four bits each. Based on testing random inputs and edge cases, all combinations of these inputs are assumed to create the expected output. The PCB’s GERBER files, or the files necessary for manufacturing, were generated and rendered in Figure 6.
4. DISCUSSION
Integration between the PCB with the FPGA board did not yet occur, which draws caution to two potential issues. The first is ensuring that the PCB connections function as intended. The worst-case scenario would be needing to draw new connections on the PCB and placing another order. The second potential issue is ensuring functionality on a higher clock speed. Figure 7 demonstrates the functionality of the device, but the clock speed was reduced to produce rigid, legible square waves. When the clock speed is increased, the square waves become less legible and more curved in shape. Therefore, performance at full speed would need be determined upon integration. If there is a clear performance issue related to the clock speed, then it
Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. Thank you to Dr. Lee and Yuyang Li for mentoring me, along with Kennametal for selecting me to be an Internship Fellow.
REFERENCES [1] I. Aharonovich, D. Englund, and M. Toth, “Solid-state single-photon emitters,” Nature Photonics, vol. 10, no. 10. pp. 631-641, Oct. 2016. [2] Z. Yuan, B. E. Kardynal1, R. M. Stevenson, A. J. Shields, C. J. Lobo, K. Cooper, N. S. Beattie, D. A. Ritchie, and M. Pepper, “Electrically driven singlephoton source,” Science, vol. 295, no. 5552, pp. 102105, Jan. 2002. [3] M. P. Andersson, M. N Jones, K. V. Mikkelsen, F. You, S. S. Mansouri, “Quantum computing for chemical and biomolecular product design,” Current Opinion in Chemical Engineering, vol. 36, 2022 [4] M. Schlosshauer, “Quantum decoherence,“ Physics Reports vol. 831, pp. 1-57, 2019
18 Undergraduate Research at the Swanson School of Engineering
Concept methodology and strength evaluation testing of a woven socket and socket fitting system Taylor Brightmana, Neharika Chodapaneedia, Zachary Roya, Goeran Fiedlerb Department of Bioengineering, bDepartment of Rehabilitation Science and Technology a
Taylor Brightman is a senior bioengineering student with an interest in product design and development. She is passionate about health care and engineering with the goal of entering the biomedical device industry. Taylor Brightman
Zachary Roy
Goeran Fiedler
Zachary Roy is a senior undergraduate student majoring in Bioengineering at the University of Pittsburgh Swanson School of Engineering with a concentration in Medical Product Engineering. He is obtaining minors in Mechanical Engineering and Studio Arts. After graduation, Zachary intends on pursuing a career in medical product design. Goeran Fiedler, PhD, is an associate professor at the University of Pittsburgh School of Health and Rehabilitation Sciences, teaching in the Prosthetics and Orthotics program. His research interests include optimizing the prescription and fitting of prosthetic devices..
Significance Statement
This research is significant as it proposes a solution to an identified issue with the lower limb prosthetic socket fitting and fabrication process. The proposed solution should reduce socket fitting time and labor. Further research and testing will be required as the prototype device is refined and developed.
Category: Device Design
Keywords: lower limb prosthetic socket, prosthetic socket fitting, rehabilitation science and technology, prototyping
Ingenium 2022
ABSTRACT
Current lower limb prosthetic socket fitting is a very time, resource, and labor-intensive process for both the prosthetist and prosthesis user. Reducing the time, labor, and material required for prosthetic socket fitting would allow prosthetists to fit more patients. Reducing these parameters will also allow those that live in less-accessible areas to have a more-withstanding socket. Additionally, it is imperative that prosthetic sockets fit the user appropriately otherwise users may experience a myriad of issues including residual limb pain and swelling. A multilayer lower limb prosthetic socket concept with a rigid outer layer and a woven inner layer is proposed herein, as well as rudimentary strength testing on a woven inner socket prototype. Due to the proposed woven composition of the inner socket, it should constrict and fit to the contours of the residual limb much easier than a standard rigid prosthetic socket. The few rigid parts of the overall proposed socket (mostly the outer rigid layer) would require less measurements to be made, thus reducing socket fitting time and labor. Additionally, its proposed woven composition allows for the user to adjust the socket fit post-fabrication. Results from the strength testing showed the woven inner socket can withstand up to ~178 N of downward force and remain attached to the residual limb. However, buckling and unintended elongation occurred during testing that will require prototype refinement for future tests.
1. INTRODUCTION
The number of people living with limb loss in the United States is expected to more than double from 1.6 to 3.6 million by 2050 due to causes such as vascular disease (which include diabetes), trauma, and cancer [1]. As the prevalence of limb loss increases, so does the need for prosthetic devices. Generally, every prosthetic limb has a socket which establishes the connection between the residual limb and the prosthesis. Currently, the process of creating standard prosthetic sockets is labor-intensive and several visits need to be made to the prosthetist, spanning several weeks [2]. Typically, the process is as follows: the user will meet with the prosthetist, a cast of the residual limb is made, a mold is produced from that cast, the prosthetist modifies the mold, and then the technicians make the socket based on that mold. One of the more common approaches of optimizing the socket fit is the use of check sockets. For reference, a check socket is a prosthetic user socket that is quickly produced and fit to the user to assess the fit, comfort, and accuracy of the measurements. Several check sockets may have to be produced before the fit is sufficient for use. Once the user is satisfied with the fit of the check socket, a final socket can then be produced. The entire process of socket fitting, even the production of check sockets, still depends heavily on the skill of an experienced prosthetist and/or requires specialized
19
equipment to create and maintain the socket. In any event, the process is very time- and resource- intensive for both the patient and the prosthetist. The process is not concluded with the fitting and delivery of their prosthetic limb, as lower limb amputees can commonly experience chronic swelling, pain, and discoloration of their limb in response to the interface dynamics between limb and socket [3]. Prosthesis users may experience further issues with their posture, muscle atrophy, and gait [3]. Considering these shortcomings, which are even more pronounced in world regions with poor health care infrastructure, this project was aimed at reducing the time and labor required to fit and produce a socket. Our objective was to reduce the socket fitting process to one visit to the clinic while minimizing the number of measurements needed to be taken of the residual limb for fitting. We describe herein the rationale and proofof-concept testing for a novel woven socket and socket fitting system. Our approach is based on the concept of a multilayer prosthetic socket consisting of an inner socket attached to an outer socket. The inner socket would be flexible, breathable, and readily form-fit to the residual limb. The outer socket would be used for structural stability. Also, as the inner socket would form-fit to the limb, the outer socket would not need to be custom made for each user which allows for faster and more affordable socket fabrication. We hypothesize that our socket will have an increase in strength and structural integrity as compared to literature on testing current socket fitting systems which were able to hold ~90 N of downward force [4].
2. METHODS
2.1. Woven Socket Prototype Structure and Rationale
The scope of the project included the development of the inner socket as well as its attachments to the outer socket. The evaluated design for the inner socket utilized the concept and mechanics of a “Chinese Finger Trap” toy, a hollow cylinder typically woven out of paper or fabric. After inserting your fingers into both ends of the toy, any tension created by pulling the fingers lengthwise causes the cylinder’s diameter to constrict. Pushing your fingers together in the toy creates compression in the cylinder, expanding the diameter and allowing for exit. Applications utilizing the mechanics of the finger trap toy already exist for finger and forearm fracture reduction [4]. From a preliminary literature search, application of the finger trap mechanism does not appear to exist in the prosthetics & orthotics field.
Figure 1: This is a schematic for our proposed prosthetic socket with each part labeled accordingly. The parts are as follows: a.) User’s residual limb, b.) Woven inner socket, c.) Tension straps, d.) Rigid proximal ring, e.) Rigid outer socket. Green arrows show tension on the corresponding straps during different phases of the gait cycle which illustrates how the finger trap mechanism stays constricted during gait. Blue arrows show the direction of movement of the user’s limb during that stage of the gait cycle.
Due to its design (as shown in Figure 1), when the inner socket is placed in tension, it constricts, tightens, and form-fits to the limb, creating a suspension system to keep the prosthesis attached to the user. The fingertrap mechanism is woven around a hard ring at the proximal end which allows for easy entry of the residual limb. At the distal end of the finger trap mechanism is a woven cup to encapsulate the distal end of the residual limb (not shown in Figure 1). Constant tension must be maintained in the inner socket throughout the gait cycle to prevent the socket from loosening. Once the limb is placed into the mechanism and the body is in stance phase, the limb pulls the socket downward, causing the nylon straps at the top to pull in the opposite direction, maintaining tension and keeping the limb in the mechanism. When the limb is placed into the mechanism and is in swing phase, the limb pulls upward, while the nylon strap located at the bottom of the mechanism pulls the socket in the opposite direction, causing constant tension, keeping the limb in place. Straps attached both at the proximal ring and through the distal cup connect the inner socket to the outer socket. These adjustable straps keep the socket in constant tension and transfer forces to the outer socket. The inner socket prototype used for strength evaluation testing was a finger trap mechanism composed of 2.54 cm width elastic Dacron webbing woven around a plastic proximal ring. Dacron webbing was utilized for the straps due to its high strength and its existing use in the P&O field. Nylon straps were wrapped around the proximal ring for tension
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Ingenium 2022
at the proximal end. Nylon straps at the distal end were woven through the bottom of the finger-trap mechanism to form a loose mesh cup with a loop.
2.2. Strength Evaluation Testing
Loading Test
Loading Conditions
Additional Weight (N)
Force Held (N)
1
No
-
133
2
Yes
44.52
178
Table 1: Loading Test Conditions and Force Held for testing the strength of the woven inner socket. With additional weight added in LT2 to stabilize the proximal ring, an increase of 45 N was obtained.
4. DISCUSSION
Figure 2: A.) LT 1 with standard set-up, B.) LT 2 with standard set-up and 4.54 kg weight to stabilize the proximal ring which led to an increase in total load achieved For the experimental set up, a foam model of a below-the knee residual limb was inserted into the inner socket. The proximal nylon straps were attached to hooks on a portable wheelchair lift to keep the model and socket suspended. A fish weighing scale (Viking 8930, Hanson, Shubuta, MS) nylon loop strap was hooked onto the distal end of the inner socket onto the loose-fitting mesh cup. A nylon strap was then wrapped through the bottom hook of the fish weighing scale, around the base of the wheelchair lift, and tied using a sliding overhand knot. To determine if the applied force caused elongation of the webbing, 2.54 cm was marked lengthwise along one of the strands. When the knot was tightened, the slack length in the strap decreased, thus creating a pulling force, tension in the inner socket, and subsequent constriction around the limb model. Once sufficiently tightened, the pulling force was recorded from the fish weighing scale. Two Loading Tests (LT) were conducted with a pulling force constricting the inner socket for 5 seconds each: one test without (Fig. 1A) and one with (Fig. 1B) a 4.54 kg weight at the proximal end to prevent ring buckling.
3. RESULTS
LT 1 (Fig. 1A) showed that the woven socket could hold a total of ~133 N. After this point, the plastic ring buckled, collecting the straps at the center of the model, and constricting the ring over the proximal end of the model. Through LT 2, (Fig. 1B) with the additional weight added to prevent proximal ring buckling, the socket withheld ~178 N of force. During loading, the markings along the length of the Dacron webbing measured out to 3.175 cm—an elongation of 0.635 cm. This suggests that the elongation of the socket caused by the force applied did not have a substantial effect on the structural integrity of the finger trap mechanism.
The tensile test was developed using resources available to replicate the testing done with an Instron testing machine [4]. Testing done with the machine determines what the system can safely tolerate when the load is applied to the device during ambulation. The modified version of this tensile test used on the woven socket aimed to do the same. By understanding the limits and potential failure points of this device, the socket can be modified further and optimized for use. Both tests showed an increase of change within the system/device as the force applied increased. While testing showed that the woven socket did not fail in either loading test, it did elongate past the distal end of the model. This was not ideal because the elongation past the distal end leaves open space at the most distal end of the residual limb. To ensure ideal socket fitting, the residual limb should be fully enveloped and make full contact with the socket. Testing also showed that the woven material collected at the center of the model, constricting the ring at the proximal end which caused it to buckle and lose shape. To better combat these issues, modifications will be made in the future to both the inner socket as well as the attachment points. The first modification will be to use a lengthadjustable initial finger-trap to prevent elongation past the distal end of the residual limb. In addition, a solid distal cup will be added to curb elongation as well as increase stability. To prevent buckling, the proximal ring will be composed of a stronger material. As device development continues, additional modifications will be made based on feedback from users and clinicians. Otherwise, these tests closely correlated with how the socket would act while on the user. The constant tension being pulled at the bottom simulated how the socket would react during the swing phase, while the nylon straps at top simulated the limb in the socket during stance phase. The added weight shows the endurance and strength of the materials used, as well as simulating users of varying weights. There was only one repetition test for each of the conditions, creating a limitation that will be addressed in the future by performing more tests. By performing more tests, this will allow for better analysis and decreased error. In addition to more testing, future plans will include allowing the length of the socket to be adjusted to prevent excessive elongation, placing a solid cup at the distal end for increased stability, and using a stronger material to prevent buckling.
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5. CONCLUSIONS
The purpose of this paper was to describe the design methodology of a novel woven prosthetic socket and socket fitting system as well as share results of an informal verification test to compare against inner sockets that are currently being used. More specifically, our design was a woven socket modeled after the popular “Chinese Finger Trap” toy. Based on our testing analysis, we were able to successfully prove our hypothesis. The woven socket’s finger trap mechanism was able to exceed the proposed force of ~20N in our hypothesis. Since the socket elongated past the distal end of the model and the proximal ring buckled, preventative measures will be taken in future testing to better mimic how much the socket will expand in reallife scenarios. The tensile testing done on other sockets such as looped liners with hook fasteners and the Iceross Dermo Liner with pin/lock system showed that with ~90N applied both systems exhibited less than 0.4 cm in movement [4]. The woven socket has shown to be able to withhold higher forces with minimal structural movement/changes. After necessary modifications, the woven socket will continue to be tested, following the testing measures that standard sockets undergo. Based on the success of this testing, the woven socket proves to be a method of socket design that could lead to a more flexible, breathable, and readily form-fitted inner socket for users than existing solutions.
6. ACKNOWLEDGEMENTS
We thank Dr. Goeran Fiedler, Dylan Beam, Jenna Eckerling, Jack Latella, Sofia Main, Samil Paul, and the Accessible Prosthetics Initiative for their support of this project. We also thank the Swanson School of Engineering, Office of the Provost, and the Department of Bioengineering for funding.
REFERENCES [1] K. Ziegler-Graham, E.J. MacKenzie, P.L. Ephraim, T.G. Travison, R. Brookmeyer, “Estimating the prevalence of limb loss in the United States: 2005 to 2050,” Archives of Physical Medicine and Rehabilitation. vol. 89, no. 3, p. 422-429, March 2008. Available: Archives-pmr, https://www.archives-pmr.org/article/ S0003-9993(07)01748-0/fulltext [Accessed July 26, 2021]. [2] Z. Roy, N. Chodapaneedi, T. Brightman, G. Fiedler, “Concept Methodology for a woven socket and socket fitting system,” American Orthotic & Prosthetic Association 2021 National Assembly. Aug. 2021. [Abstract]. [Accessed July 29, 2021]. [3] N. LaRaia, “What are some of the long-term physical effects of using or not using a prosthesis?” inMotion. vol. 20, no. 6, Nov./Dec. 2010. [Accessed July 29, 2021]. [4] N.A Abu Osman, H. Gholizadeh, A. Eshraghi, W. A. B. Wan Abas, “Clinical evaluation of a prosthetic suspension system: Looped silicone liner,” Prosthetics and Orthotics International. vol. 41, no. 5, p. 476–483. October 2016. Available: Sagepub, https://journals.sagepub.com/doi/ full/10.1177/0309364616670396 [Accessed July 26, 2021]. [5] K. S. Akhtar, D. Akhtar, J. Simmons, “A readily available alternative to Chinese finger traps for fracture reduction.” Annals of the Royal College of Surgeons of England. vol. 95, no. 2, p. 159, March 2013. Available: NCBI, https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC4098593/ [Accessed July 13, 2021].
22 Undergraduate Research at the Swanson School of Engineering
Classification of shallow and deep sleep using electroencephalogram signals in real time Mark F. Ciora, Jijun Yin, and Zhi-Hong Mao Department of Electrical and Computer Engineering Mark was born and raised in Butler, PA. He is seeking a bachelors degree in computer engineering at the University of Pittsburgh and has aspirations to attend graduate school and possibly become a professor. Mark F. Ciora
Zhi-Hong Mao
Dr. Mao obtained a dual degree in applied mathematics and automatic control from Tsinghua University, and an SM and PhD from MIT. He is now a professor at the University of Pittsburgh, and his research interests include networked control systems, human-machine and human-robot systems, and neural machine learning.
Significance Statement
Machine learning can be used to classify sleep stages with robust classifiers, but some methods are not feasible on some embedded systems. This can also be done with simplistic classifiers by leveraging unique feature selection properties. Generally, unique properties of specific machine learning problems can be utilized to enhance classification.
Category: Experimental Research or Methods Keywords: medical diagnostics with machine learning, support vector machine, real-time classification
Ingenium 2022
ABSTRACT
Machine learning has been steadily gaining a foothold in medical diagnostics. Specifically, sleep stage classification techniques utilizing electroencephalogram (EEG) signals have grown more advanced in recent years. To establish the most robust and accurate classifiers, deep learning or complex ensemble classifiers are typically used. However, these methods present a challenge in the realm of embedded systems that must be addressed. The computations necessary to make predictions in some classifiers can overwhelm systems with time and processing constraints. For this reason, it can prove beneficial to approach the problem with more speed-cognizant classification techniques. More straight-forward classifiers, such as support vector machine (SVM), generally are much faster to make predictions, but place a greater emphasis on preprocessing and feature selection. Consequently, different transformations, features, and classifiers must be experimented with to find combinations that best categorize the data, while still adhering to the processing constraints. In this project, an assortment of SVM and quadratic discriminant analysis (QDA) classifiers were compared across sets of feature selections both with and without a novel preprocessing filter. The results demonstrated that feature selection and preprocessing steps play a key role in the quality of the classifier, with a mean range of 12.8% difference in accuracy across each set of features used and 7.9% average increase in accuracy when filtering the input. It was also found experimentally that classification with SVM, achieving a high of 89.0% accuracy, was almost as accurate as a baseline of 92.0% accuracy set in prior research. Generally, this provides evidence to support the conclusion that simple classifiers capable of operating on embedded devices can produce comparable results to complex classifiers with excessive computational overhead in EEG sleep classification when the correct data preprocessing and feature selection is performed.
1. INTRODUCTION
Throughout a night of sleep, a person goes through cycles of different levels and types of brain activity called sleep stages [1]. Sleep stages are scored by medical professionals as either stage 1, stage 2, stage 3, stage 4, or REM sleep, with stages 3 and 4 sleep often being combined. The four numbered sleep stages roughly translate into a gradual transition into a deeper sleep state, and REM sleep generally indicates dreaming. In recent years, the automation of sleep stage classification, especially with EEG signals, has opened the possibility of computerized detection of sleep stages, foregoing the need for manual interpretation. Still, real-time classification of sleep stages on embedded systems is a topic that remains largely unexplored. This is of utmost importance because some sleep problems benefit from treatment immediately following diagnosis. For instance, 23
spending too much time in sleep stage 1 and 2 can generally indicate low sleep quality [2]. It should then be possible to attach a small, cheap device to a person that can detect EEG signals and classify sleep stages in real time. Then, another wearable component could administer treatment instantaneously in the form of drugs, soothing sounds, or any other method. In this study, prior sleep stage classification research which focused on postprocessing of EEG data was expanded on to explore high-speed classification in real time, enabling detection of sleep abnormalities through an inexpensive wearable device.
1.1 Past research
Hassan et al. and Abdulla et al. have both explored machine learning classification of sleep stages, finding evidence that it can be accomplished with varying degrees of accuracy [3, 4]. Specifically, Hassan et al. found that shallow sleep, stages 1 and 2, can be differentiated from deep sleep, stages 3 and 4, with an average of 92% accuracy [3]. Past works, such as these, have focused largely on maximizing accuracy or finding novel techniques for classification, but little research has gone into studying real-time classification, especially on embedded devices with processing constraints. The results of these past works act as a baseline for the exploration of real-time sleep analysis in this project.
1.2 Unique properties of real-time classification
Real-time classification on embedded systems inherently gives rise to challenges due to the constraints of the application. Most importantly, classifiers must be able to process data quickly enough to keep up with a constant data stream on a device with finite processing speed. For this reason, classifiers with long classification times or high memory storage requirements are to be avoided. Instead, a light weight classifier, like SVM, should be used for a low-power embedded device. Despite this problem, there is a unique property that results from some time-based analysis which can be exploited to increase prediction accuracy. In machine learning, individual feature vectors are usually thought of as independent from each other. However, temporally similar features gathered from raw EEG signals tend to carry similar information when measured on a low time scale since transitions between sleep stages do not occur very often. Close features can then be considered to be highly dependent on each other. For this reason, features can be averaged or smoothed over time to reduce noise, but only to an extent. Oversmoothed features lose information that is unique to the present state. The weights of surrounding values and ultimately the function used for smoothing can be determined and optimized experimentally.
2. METHODS 2.1 Data
The EEG data used in this project contained 20 data sets from 10 healthy Caucasians aged 25-101 obtained from a PhysioNet database over 2 nights of sleep each [5, 6]. The Fpz-Oz electrode channel was analyzed to mirror the input for the planned device. The data is composed of several 3000 sample epochs sampled at 100 Hz. These were further broken down into 300 sample epochs for more localized feature extraction. All data was labeled as awake, stage 1 sleep, stage 2 sleep, stage 3 sleep, stage 4 sleep, or REM sleep. There were a total of 136,900 shallow and deep sleep epochs used, consisting of 77.55% shallow sleep and 22.45% deep sleep.
2.2 Features
The features explored in classification were approximate frequency band power ratios, Hjorth features, and approximate entropy. The frequency band powers were calculated over 1-4 Hz, 4-8 Hz, 8-12 Hz, and 12-35 Hz, which are the standard ranges corresponding to delta, theta, alpha, and beta waves. Features were smoothed with a simple low-pass filter. The formula used for the low-pass filter system can be seen here: output[n]=input[n]+a*output[n-1] Output[n] is the output of the smoothing filter at time n, output[n-1] is the output at time n-1, and input[n] is the input at time n. A scaling factor a was used that ranges from 0 to 1. Through experimentation, the optimal value of a was found to be 0.82. Data was processed and compared both with and without feature smoothing.
2.3 Classification
All data was processed in MATLAB. The classification methods used were quadratic discriminant analysis (QDA) and support vector machine (SVM). Linear (Lin) SVM as well as radial basis function (RBF) and order 3 polynomial (Poly) kernelized SVM were used. Two-class classifiers were created to compare stages 1 and 2 sleep (shallow) with stages 3 and 4 sleep (deep). Classification models were trained on 19 of 20 data sets at a time and then tested on the remaining data set. This process was repeated until each set had been tested. Figure 1 demonstrates this process.
Figure 1: Train-test Split.
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The results of the classification methods were measured by accuracy (ACC) and sensitivity (SEN). Sensitivity is considered to be an estimate of the probability that a sample is classified correctly given it is in a specified class. ACC =
SEN x=
Correct Classifications Samples Size Correct Classifications of x Sample Size of x
The overall averages of these results over the 20 people tested were measured and recorded for each feature set and model type.
3. RESULTS
3.1 Feature selection and classifiers
The comparison of accuracy and sensitivity for each classifier is compared with the selected features in figure 2. Approximate entropy had very poor results for linear SVM because it is a one-dimensional feature and was not linearly separable. There were only slight differences in overall classifier quality between features, except for approximate entropy. Power band ratios, Hjorth features, and all features at once showed similar results.
Figure 2: Sensitivity & Accuracy Average Percent vs. Classifiers For Each Feature Selection
3.2 Feature smoothing
Figure 3 shows the results of the comparison of the low-pass filtered and the unfiltered features. Filtering showed a great impwrovement in classification.
4. DISCUSSION
Some of the results of the classification approached the baseline accuracy value of 92% found previously [3], with the closest being polynomial kernelized SVM with power band ratio features at 89.0% in section 3.1. Additionally, using multiple types of features did not tend to improve the classifier by itself. The evidence that classification can be done with a low-dimensional feature-space gives enormous weight to the possibility of classifying sleep stages in real time. Additionally, the results in section 3.2 provided evidence that feature smoothing can greatly enhance classification accuracy and sensitivity, yielding an average increase of 7.9% across all measurements. However, it is a technique that cannot be applied to everything. Its use is limited to problems where the feature sets are ordered and close features have some sort of relationship. Still, the broad field of machine learning in digital signal processing stands out as one of the best suited subjects for feature smoothing.
5. CONCLUSIONS
Evidence was found that real-time classification of sleep stages on an embedded device can be done with SVM. Certain techniques like feature smoothing can be applied to SVM to further approach the accuracy baseline set by more costly classifiers. Still, there is work to be done. These classification techniques stand out because of their simplicity and as a result suffer from the consequence that they may not be able to match the robustness of other classifiers, despite coming quite close. Leading research results have found up to 92% [3] classification accuracy, while the methods described here could only achieve a maximum of 89%. The results could also open other possibilities in the realm of real-time EEG analysis. Since there are relatively simple features and preprocessing methods that can result in substantial improvement to the classification process, other uses for real-time machine learning EEG analysis could arise.
6. ACKNOWLEDGEMENTS
Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.
REFERENCES [1] Jouvet M: Neurophysiology of the state of sleep. Physiological Review 47, 117-177, 1967. [2] Carskadon et al. Normal human sleep: an overview. Principles and practice of sleep medicine 4, 13-23, 2005. [3] Hassan et al. Biocybernetics and Biotechnical Engineering 36, 248-255, 2016. [4] Abdulla et al. Expert Systems with Applications 138, 2019. Figure 3: Percent Accuracy vs. Classifiers Filtered and Unfiltered
[5] Kemp et al. IEEE-BME 47, 1185–1194 2000. [6] Goldberger et al. PubMed. 101, 215–220, 2000. 25
Designing metal organic framework-based e-noses to detect lung cancer via volatile organic compounds emitted in breath Spencer C. Conaway, Brian Day, Christopher Wilmer WilmerLab, Department of Chemical Engineering Spencer Conaway was born in Edinboro, PA. He hopes his skills in chemical engineering will help him make a positive impact on society.
Spencer C. Conaway
Christopher Wilmer
Dr. Christopher Wilmer is an associate professor in the Chemical Engineering Department. His research lab focuses on studying the properties of hypothetical materials using computer simulations, and molecular modeling. He is also the co-founder of NuMat Technologies, Aeronics, Inc and the journal Ledger.
Significance Statement
Metal organic frameworks (MOFs) can be utilized as a method of disease detection due to their gas adsorption properties. This research aims to find a series of MOFs that can measure the amount of lung cancer biomarkers in a patient’s breath as a method of screening for lung cancer.
Category: Computational Research
Keywords: MOFs, lung cancer, VOCs, adsorption
Ingenium 2022
ABSTRACT
The purpose of this research paper is to investigate the performance of select MOFs for adsorbing lung cancer VOCs. Grand Canonical Monte Carlo adsorption simulations were run to see how five different biomarkers (isoprene, pentanal, hexanal, octanal, and nonanal) adsorbed into nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU125, UIO-66, and ZIF-8). Most MOFs adsorbed isoprene most readily. All the aldehydes (pentanal, hexanal, octanal, and nonanal) adsorbed in a similar manner. As the aldehydes increased in molecular weight, the adsorbed mass into each MOF decreased. Pentanal, the smallest aldehyde, consistently adsorbed more mass into each MOF than nonanal, the largest aldehyde. These findings show a potential correlation between molecular size and adsorption into a MOF. The findings within this research can be utilized to determine a MOF array that can successfully detect lung cancer from the VOCs that were tested.
1. INTRODUCTION
The purpose of this research is twofold: create a system that maps out complex VOC structures for simulation, and find a series of MOFs that can detect lung cancer biomarkers and be implemented into a sensing array for lung cancer detection. The MOFs identified in this research can be incorporated into an e-nose array that can capture and measure lung cancer biomarkers.
1.1: E-noses and disease detection
Electronic noses (e-noses) are a new type of analysis tool comprised of a sensor array designed to replicate a human nose. It is known that the human nose contains neuron receptors that are stimulated differently by different molecules [1]. Like the human nose, an e-nose uses its sensing array to detect different molecules and measures a strength of signal to deduce concentrations. E-nose technology has been studied heavily by medical researchers due to e-nose scanning being a “noninvasive sampling technique” that is “painless, inexpensive, and can be easily performed” in medical centers around the world [2]. E-nose technology also encourages earlier screening for potentially deadly diseases. Due to the ease of screening, testing for diseases can be completed before symptoms begin to appear. Early diagnosis can then dramatically increase the survivability of the disease. Most current e-nose technology relies on mass spectrometry analysis which is too “time consuming, expensive, and depends on a skilled operator.” [2]. Therefore, e-noses are not heavily used in disease detection currently.
1.2: MOFs for lung cancer detection
Although many different materials can be used for making e-noses, such as carbon nanotubes [3], this study focuses on the use of MOFs as the adsorption material. MOFs are porous, molecular nets that adsorb
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certain gases more readily than others depending on the physical and chemical properties of both the MOF and the gas. MOFs have many applications such as direct air carbon dioxide capture and natural gas storage, but this study focuses on the use of MOFbased e-noses in medicine [4]. Many diseases affect biochemical processes in the body which can alter the composition of volatile organic compounds (VOCs) in the breath, such as kidney disease, COVID-19, and lung cancer [5]. These trace compounds can be measured using an e-nose, enabling physicians to make a diagnosis. Lung cancer causes several VOCs to be emitted in a patient’s breath. In a paper by Fuchs et al., four different aldehydes (pentanal, hexanal, octanal, and nonanal) were found to be in the breath of most lung cancer patients [6]. Another paper by Bajttarevic et. al. found isoprene to be a VOC in the breath of lung cancer patients [7]. This group of biomarkers is not a comprehensive list of all the biomarkers detected in the breath of lung cancer patients. These biomarkers were chosen as a sample of the overall identified biomarkers to assess the possibility of creating a MOF based e-nose for lung cancer detection. Depending on the pore size of the MOF, certain sized molecules can adsorb into the MOF: small pores are better suited for adsorbing smaller molecules, and larger pores are better suited for larger molecules. An ideal MOF would only adsorb one specific molecule, but since that cannot occur, a system of different MOFs with different adsorption properties can be used to evaluate mass ratios of the biomarkers in a breath sample.
2. METHODS
This study consisted in developing a program to create molecule definition files for the known biomarkers and running Grand Canonical Monte Carlo (GCMC) adsorption simulations using RASPA to test a series of nine MOFs for biomarker adsorption. GCMC simulations in RASPA are ideal for adsorption simulations since they can accurately model gas behavior through a statisticalmechanical method [8]. Since breath contains over 1,000 molecules and there are over 130,000 known MOF structures, GCMC simulations must be used to screen through the large quantity of biomarkers and MOFs. Through a series of RASPA simulations run on a supercomputer, adsorption isotherms are created.
2.1: VOC mapping for simulation
The VOC biomarkers analyzed in this study are complex organic molecules: pentanal, hexanal, octanal, nonanal, and isoprene (see Figure 1).
Figure 1: The 5 lung cancer biomarkers used in this study: (a) isoprene, (b) pentana, (c) hexanal, (d) octanal, (e) nomanal
These biomarkers have been cited in previous papers as being found in the breath of most lung cancer patients. Therefore, detecting the adsorption of these complex VOCs would lead to a MOF array that can readily screen for lung cancer. The biomarker’s atoms and orientations need to be mapped out for the simulation software to properly run the adsorption simulations. The molecule definition file provides the information that the software utilizes for each specified molecule. Previously, the molecule definition file was hand-written and inefficient to test the wide variety of biomarkers. As a part of the study, a code was created to automatically assemble RASPA readable molecule definition files from the online database TraPPE [9].
2.2: RASPA simulation of VOCs
GCMC simulations on RASPA model gas behavior by inserting each gas one molecule at a time. The start of the simulation begins in a vacuum environment where only the MOF exists, then gas molecules are gradually added to the system over a series of “steps” until the system achieves the final specified pressure [8]. Once the molecular structure is mapped in RASPA via the definition file, pure adsorption isotherms were run for each biomarker. Nine MOFs were tested: IRMOF-1, HKUST-1, NU-125, ZIF-8, UIO-66, MgMOF-74, NU-100, MOF-801, MOF-177. These MOFs have a wide variety of pore sizes so different adsorption behavior can be observed. Ambient pressure varied from 1e-5 pascals to 5e+6 pascals with temperature constant at 298K. The pure adsorption isotherms determined how effectively each MOF adsorbs the gas biomarkers.
3. RESULTS
Each simulation generates the adsorbed mass for each biomarker as a function of pressure. Generally, the absorbed mass for each biomarker increases as the pressure increases. Pressure ranges in each plot are near atmospheric pressure due to the system that is being simulated. For this experiment, the system is a patient breathing into a MOF exposed to the atmosphere. An average human breath is about 1.8 psi which is nominal compared to atmospheric pressure 27
[10]. Therefore, the pressures near atmospheric pressure should be focused on. The values recorded are not time dependent and are instead recorded after the total number of steps has achieved an equilibrium within the MOF. Figure 2 displays the adsorbed masses of each biomarker for HKUST-1. Isoprene adsorbed the most into HKUST-1 which occurred in several other MOFs. The aldehydes adsorbed in a pattern according to their size: pentanal (the smallest aldehyde tested) adsorbed the greatest, while nonanal (the largest aldehyde tested) adsorbed the least. Also note the sharp increase in adsorption for octanal and nonanal at 5e+5 pascals of pressure.
Figure 4 shows data for the MgMOF-74. This MOF does not follow the same trends as HKUST-1 and IRMOF-1: the aldehydes adsorbed just as well as isoprene. The smaller aldehydes continue to adsorb more readily than the larger aldehydes. The sharp increase in adsorption at 5e+5 pascals does not occur in MgMOF-74.
Figure 4: MgMOF-74 pure absorption isotherms for lung cancer biomarkers
Figure 5 displays the adsorption data for MOF-177. This MOF performs in a similar manner to IRMOF. MOF-177 adsorbs more isoprene than aldehydes, the aldehydes adsorb according to their size, and there is a sharp increase in adsorption at 5e+5 pascals. Figure 2: HKUST-1 pure adsorption isotherms for lung cancer biomarkers.
Figure 3 shows the adsorption data for the IRMOF-1. IRMOF adsorbs significantly more isoprene mass than the aldehydes. The sharp increase in adsorption still occurs at 5e+5 pascals, and the aldehydes adsorb according to their molecular size.
Figure 5: MOG-177 pure absorption isotherms for lung cancer biomarkers.
Figure 3: IRMOF-1 pure absorption isotherms for lung cancer biomarkers.
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Figure 6 displays data for the MOF-801. MOF-801 shows unique adsorption behavior. The smallest aldehydes adsorb considerably better than the larger aldehydes and isoprene, but hexanal adsorbs better than pentanal despite hexanal being larger than pentanal. No extreme adsorption increases occur in MOF-801 as well.
Figure 8 displays data for the MOF NU-125. NU-125 also adsorbs large amounts of isoprene, and the aldehydes adsorb from smallest to largest. The sharp increase in aldehyde adsorption occurs at 5e+5 pascals.
Figure 8: NU-125 pure adsorption isotherms for lung cancer biomarkers. Figure 6: MOF-801 pure adsorption isotherms for lung cancer biomarkers.
Figure 7 shows adsorption data for the MOF NU100. This MOF readily adsorbs isoprene and adsorbs minimal amounts of the aldehydes.
Figure 7: NU-100 pure adsorption isotherms for lung cancer biomarkers.
Figure 9 shows the adsorption data for the MOF UIO66. This MOF deviates from the trends of IRMOF, MOF177, and NU-100. UIO-66 adsorbs the smaller aldehydes the most, followed by isoprene, and adsorbs the largest aldehydes the least.
Figure 9: UIO-66 pure adsorption isotherms for lung cancer biomarkers.
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Figure 10 displays the adsorption data for the MOF ZIF-8. ZIF-8 adsorbs each of the biomarkers at close to equal mass amounts. ZIF-8 adsorbs biomarkers at around 1 Pa and increases adsorption until about 5e+2 Pa.
Figure 11: HKUST-1 Crystal Structure
4.1: Structure and Adsorption
Figure 10: ZIF-8 pure adsorption isotherms for lung cancer biomarkers.
Some of the MOFs experienced a sharp increase in aldehyde adsorption at about 5e+5 pascals of pressure. The origins of this phenomenon are currently unknown, and further testing will be done to find a cause for the increase. As shown from the previous figures, the nine MOFs tested in RASPA adsorb each of the biomarkers differently. The difference of adsorption from the MOFs can be utilized to construct a MOF array that can detect lung cancer.
4. DISCUSSION
The data presented in the results section depicts how the biomarker adsorbs into all nine MOFs as a function of pressure. The results from the RASPA simulations give a starting point to determine each MOF’s sensitivity toward the various molecules. An ideal MOF would only adsorb one biomarker and none of the other molecules. This, however, is not reality. Instead, some MOFs adsorb significantly more of one biomarker than the others, and this MOF can still be used for a breath sample analysis. Other MOFs can adsorb for one functional group more than another. Those MOFs adsorb more aldehydes, regardless of size, because of the aldehydes similar functional group. For example, IRMOF-1 is especially sensitive toward isoprene, and adsorbs the aldehydes relatively poorly. For many of the MOFs (HKUST-1, NU-125, MOF-801, UIO-66 specifically), as the molecular size of the biomarker increases, the adsorbed mass decreased. Certain MOFs such as HKUST-1 have small pore sizes (see Figure 11), that smaller molecules adsorbed into more readily. Other MOFs like MgMOF-74 and ZIF-8 adsorbed similarly regardless of molecular size.
These adsorption simulations also show how chemical or physical structure can affect the adsorption into each MOF. With certain biomarkers, the functional group has a stronger influence on adsorption. Other biomarkers, on the other hand, can be more affected by their physical properties such as molecule size and shape. In HKUST-1 and MOF-801, the size of the aldehyde affects the adsorption into the MOF. Pentanal, the smallest aldehyde tested, adsorbed the most into HKUST-1, while nonanal, the largest aldehyde tested, adsorbed the least into HKUST-1. As the size of the aldehyde decreases, the adsorption into the MOF increases. Some MOFs adsorbed similarly regardless of MOF size, including MgMOF-74 and ZIF-8. Specifically, these MOFs were not selective based on either chemical or physical traits: those certain MOFs adsorbed about an equal mass of each molecule. These MOFs have larger molecular pores than HKUST-1 and MOF-801 which were more selective based on molecular size.
5. CONCLUSIONS
Overall, of the nine MOFs that were tested, some had certain selectivity toward the nine biomarkers that were being evaluated. IRMOF and MOF-177 adsorbed isoprene particularly well, while UIO-66 and MOF-801 adsorbed the smaller aldehydes particularly well. Some MOFs are more selective toward the size of a molecule (IRMOF and MOF-177 adsorbing the smaller molecule, isoprene), while others adsorb all the biomarkers equally regardless of size (ZIF-8 and MgMOF-174). This variety of adsorption behavior is needed to create a MOF array that can distinguished and measure the weight of each biomarker in the breath of a patient. Finding MOFs that adsorb each of the biomarkers differently can help pinpoint a series of MOFs that can accurately deduce the mass ratio of biomarkers within a patient’s breath. From these results and future evaluation of other MOFs, a series of MOFs can then be implemented to determine the existence of biomarkers within a patient’s breath.
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6. ACKNOWLEDGEMENTS
This research was conducted under the supervision of Dr. Wilmer and the mentorship of Brian Day. Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. The author also thanks Kennametal for the Kennametal Internship Fellow nomination.
REFERENCES [1] Morrison, J. Human nose can detect 1 trillion odours. Nature, 2014. [2] Farraia, Mariana Valente MSca,∗; Cavaleiro Rufo, João PhDb; Paciência, Inês MSca,b,c; Mendes, Francisca MSca; Delgado, Luís PhDd,e; Moreira, André PhDa,b,f The electronic nose technology in clinical diagnosis: A systematic review, Porto Biomedical Journal: July-August 2019 - Volume 4 Issue 4 - p e42 doi: 10.1097/j.pbj.0000000000000042 [3] Orzechowska, S., Mazurek, A., Świsłocka, R., & Lewandowski, W. (2019). Electronic Nose: Recent Developments in Gas Sensing and Molecular Mechanisms of Graphene Detection and Other Materials. Materials (Basel, Switzerland), 13(1), 80. https://doi.org/10.3390/ma13010080 [4] Hao Li, Libo Li, Rui-Biao Lin, Wei Zhou, Zhangjing Zhang, Shengchang Xiang, Banglin Chen, [5] Porous metal-organic frameworks for gas storage and separation: Status and challenges, EnergyChem, Volume 1, Issue 1, 2019, 100006, ISSN 2589-7780,5. Jia, Z., Zhang, H., Ong, C. N., Patra, A., Lu, Y., Lim, C. T., & Venkatesan, T. (2018). Detection of Lung Cancer: Concomitant Volatile Organic Compounds and Metabolomic Profiling of Six Cancer Cell Lines of Different Histological Origins. ACS omega, 3(5), 5131–5140. [6] Fuchs, P., Loeseken, C., Schubert, J.K. and Miekisch, W., Int. J. Cancer, 126, 2663-2670, 2010. [7] Bajtarevic, A., Ager, C., Pienz, M. et al. BMC Cancer 9, 348, 2009. [8] J.-H. Yun, Y. He, M. Otero, T. Düren, N.A. Seaton, [9] Adsorption equilibrium of polar/non-polar mixtures on MCM-41: experiments and Monte Carlo simulation, Editor(s): F. Rodriguez-Reinoso, B. McEnaney, J. Rouquerol, K. Unger, [10] Studies in Surface Science and Catalysis, Elsevier, Volume 144, 2002, Pages 685-692, SSN 01672991, ISBN 97804445126119. M.G. Martin, and J.I. Siepmann J. Phys. Chem. B 102, 2569-2577, 1998. [11] Elliot, David. The Mechanics of Breathing. Encyclopædia Britannica, Encyclopædia Britannica, Inc.
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VOF modeling of annular gas-liquid flow regimes in horizontal pipes Joshua Dewalda, Dr. Wai Lam Lohb Swanson School of Engineering, Mechanical Engineering Department, bNational University of Singapore, Mechanical Engineering Department a
Joshua Dewald
Wai Lam Loh
Josh is a graduating mechanical engineering student from the University of Pittsburgh. He has a passion for fluid mechanics, mechatronics, and dynamic systems which led to his pursuit of computational research. He plans to pursue further research and graduate studies after working in the industry and gaining additional perspective on modern challenges in the field. Dr. Loh is an Associate Professor in the Department of Mechanical Engineering at the National University of Singapore. Before becoming an Academic, Dr. Loh spent many years in the oil and gas industry, where he successfully invented, patented, and developed numerous devices and systems for the industry and won several prestigious international awards, including The Royal Society’s Esso Energy Award from the UK.
Significance Statement
This research presents unique flow visualization data in multiphase flow. By utilizing these data, CFD models can be developed to optimize applications, especially relating to oil and gas, involving these mechanics. This work demonstrates how a model can be developed in a short period of time with limited computational resources.
Category: Computational Research Keywords: ANSYS Fluent, annular flow, VOF modeling, multi-phase flow, CFD
ABSTRACT
A computational model of the annular gas-liquid flow regime was developed to validate experimental findings, existing theory, and to pave the way to develop more accurate predictive CFD models for annular flow regimes. These regimes are important to the oil and gas industry because they tend to offer significant reduction in pressure losses in the transport of heavy oils. The Volume of Fluid (VOF) model, an established multiphase model which produces clear fluid interfaces, is applied using ANSYS Fluent to a 50 [mm] pipe to simulate the flow regime using air and water at STP. The model, in conjunction with the RANS based SST k-w turbulence model, was developed over a pipe of 5 [m]. Unique hi-speed footage of annular flow from the NUS Multiphase Flow Loop and flow regime maps are used as a guide for boundary conditions. As a result, a wavy annular flow regime is observed to develop between roughly 0.75 – 1.5 meters downstream of the inlet. Axial velocity data are post processed and a GCI study, the ASME standard for discerning discretization error [1], was conducted revealing average variation up to ±44.48 [m/s] with an apparent order p = 5.43. The portion of the pipe where the flow regime is observed resembles the observations made at the NUS facilities and were achieved using superficial velocities within the predicted region of the flow regime maps. However, with significant variation present, the model requires further refinement.
1. INTRODUCTION
Research in multiphase fluid mechanics has, over the years, well defined a variety of flow regimes through experimentation and theoretical models. While these data are useful, advances in computational fluid dynamics are beginning to enable more accurate predictive modeling of these systems for use in engineering design. For annular flow, the few models currently being considered utilize VOF and RANS turbulence models similar to this study, for example Pinilla’s [2] application to vertical air-water annular flow and Nouri’s [3] application to horizontal oil-water annular flow. Notably, these models are applied to slightly different cases and lack direct experimental flow visualization data to reinforce their findings. In this case, horizontal air-water annular flows are simulated and compared against novel flow visualization data in addition to the empirically developed theory. By utilizing these data to further validate and refine VOF models, more accurate predictive tools can be developed for annular or other multiphase flows.
2. PROBLEM SETUP
The VOF method tracks surfaces over a fixed Eulerian mesh by solving a single set of momentum equations coupled with a volume fraction equation, a modified form of continuity, which tracks phase fractions throughout the domain. The model is developed 32
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by first observing high speed footage of the regime captured at the NUS Multiphase Flow Loop (Fig. 4) and utilizing superficial velocities derived from the footage. Superficial velocity is defined as the average velocity of a phase if the pipe were filled with only that phase. It can be defined mathematically as the volumetric flow rate divided by the pipe cross-sectional area, J = Q/Ac, where Q = vavg x A. Additionally, the geometry of the pipe used in the experiment is recreated in the model, the diameter used is 50 [mm] throughout the study. Using this information alongside the flow regime map seen in Fig. 3, the model was developed over a pipe length of 5 [m]. It is well established that the existence of a specified flow regime is independent of the path used to arrive at that state, it is only dependent on the set of flow conditions for a given situation [4, p. 48] and the appropriate length-scale to fully develop. Therefore, the geometry of the inlet conditions in the model are selected such that the meshing process is simple and facilitates a structured mesh. A 3D model is constructed to obtain a cross-sectional view of the flow as well as velocity profiles in the horizontal plane. The inlet geometry is shown in Fig. 1.
From Eq. 1, the inlet flow of air will be turbulent. This necessitates the inclusion of a turbulence model, the RANS based SST k-w model is applied in this case for reasons discussed in section 2.2. From Eq. 2, it is assumed that surface tension effects are negligible.
2.1 Meshing and Boundary Conditions
In this case, linear hexahedral elements are imposed on the body with a multizone mesh. Inflation near the wall of the cylinder is applied to capture the effects of viscous friction due to the no-slip boundary condition. Because of model constraints in ANSYS Student, the mesh is relatively coarse, and is not expected to capture the annular ring with high fidelity. To mitigate this, a dynamic boundary condition is used called “wall adhesion” in Fluent. Surface tension effects globally may be negligible in this case as governed by the Weber number, but the wall adhesion condition necessitates the use of a surface tension model. Thus, the continuum surface force model with wall adhesion, as proposed by Brackbill et al. [5], is used. The inclusion of this model results in a source term in the momentum equation wherein the surface tension coefficient between phases must be defined, 0.072 [N/m] for air-water at STP is used in this case. The inclusion of wall adhesion uses the contact angle that the fluid is assumed to make with the wall to adjust the surface normal in cells near the wall [6, p. 521]. After considering the boundary conditions near the wall, the face of the inlet is separated into the regions described by Fig. 1. The final resulting structured mesh is seen in Fig. 2.
Figure 1: Inlet Geometry for BCs
The superficial velocities derived from experiment were Ja =25 and Jw =0.05, all velocities are expressed in [m/s]. Using the inlet geometry and superficial velocities, initial boundary conditions for inlet velocities are determined as vavg,a =39.06 and vavg,w =0.139. Using these velocities, the material properties, and geometry of the model, the Reynold and Weber numbers are used to determine the range of physics needed to represent the flow regime. Rea = rVD/m @ 107,000 >> 4,000 We = rLu /s @ 130,000 >> 1 2
(1) (2)
Figure 2: Inlet face of the hexahedral mesh
This is the general mesh used throughout the rest of the development of the model. The only modifications being lengthwise refinement downstream of the inlet and overall refinement for the purposes of a GCI study. The outlet boundary condition is simply set to outflow. That is, because the outflow conditions are unknown, but expected to be fully developed, Fluent will extrapolate the required information from the interior to solve for the boundary nodes. To prevent a
33
backflow condition in the flow-field, an initial velocity field set to 39.06 [m/s], composed entirely of air, in the downstream direction is applied. The final step in setting up the boundary conditions for the model is to set the inlet velocities, referencing the flow regime map in Fig. 3, the superficial velocities provided from the experiment should produce an annular flow regime and thus are imposed on the model.
Figure 3: Multiphase flow regime maps from Taitel & Dukler [4] and Mandhane [7] based on superficial velocities.
Although the inlet conditions from experimentation were expected to produce annular flow, the model resulted in a dispersed water in air flow regime when applied. To prevent the breakup of the water phase and allow the regime to develop, the superficial velocity of the water phase was increased to Jw = 0.45, which still falls within the range of the flow regime map in Fig. 3. This change produced considerably more reasonable results. It should also be noted that the flow regime map was developed in reference to a pipe diameter of 25 [mm], slightly smaller than used in this study but still within a similar scale.
2.2 VOF and SST k-w Theory
The VOF method can model two or more immiscible fluids by solving a set of momentum equations and modified continuity equations to track the phase fractions throughout the domain. One phase can be modeled as a compressible ideal gas [6, p. 510]. In this case, these effects were considered negligible because the flow is sub-sonic by a large margin. For each phase in the model, a variable for the volume fraction is included to determine the phase fractions in every computational cell. The modified continuity equation, otherwise known as the “Volume Fraction Equation” is defined by equation (3).
(3)
Where ṁ pq is the mass transfer rate from phase p to phase q. The volume fraction equation is only solved for secondary phases, the primary phase is calculated by the constraint that the summation of phase fractions must equal unity. The properties appearing in the transport equations are determined by the fractions of component phases in each cell. Similarly, the momentum conservation equation, as defined in [6, Pg. 517], is also dependent on the volume fractions of phases through the properties of r and m. With respect to turbulence modeling, the Reynoldsaveraged Navier-Stokes (RANS) based SST k-w model is employed. The standard k-w model, developed by Wilcox [8], is an empirical model based on transport equations for the turbulence kinetic energy, k, and the specific dissipation rate, w. The standard k-w model however has a well-known sensitivity to freestream velocities. To reconcile this and include the effects of turbulence shear stress transport (SST), the SST k-w model was developed by Menter [9], which includes a new term in the dissipation rate equation called the cross-diffusion term. This inclusion among other adjustments to the model allow for reasonable independence of free-stream velocity in the far field, effectively transitioning to a k-e model and maintains the accuracy of the k-w model in the near-wall region. Additionally, using this turbulence model has the capability to utilize turbulence damping. In multiphase flows high velocity gradients at the interface between the phases results in high turbulence generation. Using SST k-w enables the inclusion of a source term to the w equation which allows for local turbulence damping at the interface [6, Pg. 65].
2.3 Numerical Methods and Setup in Fluent
Although the flow is expected to fully develop, turbulent flow is fundamentally unsteady, so a timedependent formulation of the model is developed. With respect to flow solvers, the VOF model in Fluent requires the use of a pressure-based solver. In this case, the coupled scheme was selected as it offers an improved rate of solution convergence, notably in the case of a poor-quality mesh, compared to a segregated algorithm despite higher memory demands. For the pressure-based solver, ANSYS Fluent uses a controlvolume-based approach to discretize the momentum and continuity equations. This method is based on applying the unsteady conservation equation for transport of a scalar quantity to the finite cells in the computational domain. The solver stores pressure and velocity values at cell centers and employs an interpolation scheme to determine the values of flow variables at cell interfaces or faces.
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In this model, the PRESTO! interpolation scheme is employed for pressure. This scheme is recommended for use with the VOF model by ANSYS as the flow is expected to contain high speed rotating flows due to turbulence [6, Pg. 693]. A second order upwind scheme is employed for interpolation of convective quantities. This scheme is also employed with respect to turbulent kinetic energy and specific dissipation rate. With respect to the spatial discretization of the volume fraction equation (Eq. 3), an explicit timedependent formulation is used. The main advantage to using an explicit scheme for the volume fraction equation is the ability to utilize the Geometric Reconstruction Scheme for interpolation near the fluid-fluid interface. This scheme, developed by Youngs [10], is the most accurate interface capturing scheme ANSYS offers. The interface is represented using a piecewise-linear approach defined in [6, 514]. By using an explicit scheme, the model is limited to using a first-order implicit time scheme used to temporally discretize all the time-dependent equations related to the model. The model is then solved iteratively at each time-step before advancing to the next based on the criteria for convergence. In this case, adaptive time advancement is utilized with criteria for the local (max. 200) and global (max. 2) CFL numbers to ensure stability in the model. The time step is initiated at 1e-5 [s] and is increased until a maximum stable time-step is achieved. This stable time-step ranged from roughly .1 to .5 [ms]. The model is calculated over a period of 1 second.
3.1. Analysis of results with respect to flow visualization
The results demonstrate for a brief length of pipe, approximately between 0.75 – 1.5 [m], a developed wavy annular flow regime with entrained liquid droplets. Comparing high-speed footage taken from experiment, Fig. 4, to the numerical results in Fig. 5, a few observations can be made. The contour demonstrates the air volume fraction for air along the vertical midplane. The computational model demonstrates similar visual properties to the experiment in three ways. The flow of water is biased toward the bottom of the pipe due to buoyancy effects. The annulus is wavy, with ripples along the interface. And both clearly contain entrained droplets of water in the core of the flow. However, the interface in the model appears smoother with less waves or disturbances. Additionally, the entrained droplets in the model appear to be larger and less defined.
Figure 4: Annular flow observed in the NUS Multiphase Flow Test Facility
Figure 5: Vertical midplane air-fraction contour between 0.75 1.5 [m] downstream
The visual discrepancies could be due to a variety of reasons related to the setup of the model and the mesh. The smooth interface and size of the droplets in the model is likely due to a coarse mesh and parameters of the turbulence model that are not appropriately tuned. Taking a contour of a cross-section of the pipe in the regime, the water annulus is clearly observed in Fig. 6.
Figure 6: Air-fraction contour of a cross-section of the pipe at z = 1.15 [m] 35
While the annulus has developed, some discontinuity and blurring of the interface occurs. In addition to the quality of the mesh, the shared-field approximation of the VOF method likely contributes to this distortion. Another reason may be the application of the walladhesion boundary condition. As the curvature is adjusted in cells near the wall, the interface may be separated in this process.
3.2 Estimating discretization uncertainty
After realizing annular flow in the model, the mesh was refined within the limits of ANSYS Student (250k nodes) to conduct a GCI analysis of axial velocity data taken from a midplane at the cross-section z = 1.15 [m]. Refinement factors r¬32=1.129 and r21=1.15 were used. Extrapolated velocity profiles including some error bars indicating the estimated uncertainty are shown in Fig. 7-8.
and ±44.48 [m/s] in the horizontal profile. The widely varying uncertainty could be the result of many variables, but significantly, the flow is turbulent, discretized in time by a 1st order accurate method, and iterated in time with adaptive time-steps.
4. CONCLUSION
Overall, the model consistently and reliably produces an annular flow regime for nearly a 1 [m] section of pipe beginning roughly 0.75 [m] downstream of the inlet. The results follow the visualization data well despite numerical variation. With respect to discretization uncertainty, further analysis of iteration error could lead to improvement, especially as it relates to the temporal iterations. Refinements that could improve the model’s accuracy include, but are not limited to, using quadratic elements in the mesh, tuning turbulence parameters to promote stability of the regime, refining the mesh significantly near the wall to reduce the need for the wall adhesion boundary condition, and employing a compressive discretization scheme for the fluid interface. Last, the model should be extended over a longer pipe and time period to observe the evolution into fully developed and stable flow, approximately 200 diameters long per experimental experience.
5. ACKNOWLEDGEMENTS
This study was conducted as part of the SERIUS undergraduate research program. Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh. I would like to acknowledge Professor Barry, M. of the Swanson School of Engineering for his advice and inspiration in the field of Fluid Mechanics and Computational Modeling. Figure 7: Extrapolated profile along the vertical midplane with discretization uncertainty
Figure 8: Extrapolated profile along the horizontal midplane with discretization uncertainty The uncertainty is notably high with an average of ±20.6 [m/s] in the vertical profile,
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REFERENCES [1] Celik, Ghia, Roache, Freitas, Coleman, Raad, 2008, “Procedure for Estimation and Reporting of Uncertainty Due to Discretization in CFD Applications”, J. Fluids Eng., vol. 130 [2] Pinilla, A., Guerrero, E., Henao, D.H. et al. CFD modelling of two-phase gas–liquid annular flow in terms of void fraction for vertical down- and upward flow. SN Appl. Sci. 1, 1382 (2019). [3] Nouri, Saliha & Hafsia, Zouhaier & Boulaaras, Salah & Allahem, Ali & Alkhalaf, Salem & Feng, Baowei. (2021). A Three-Dimensional Model of Turbulent Core Annular Flow Regime. Journal of Mathematics. 2021. 10.1155/2021/5296700. [4] Taitel, Y. and Dukler, A.E. (1976), A model for predicting flow regime transitions in horizontal and near horizontal gas-liquid flow. AIChE J., 22: 47-55. [5] Brackbill, J. U., D. B. Kothe, and C. Zemach. “A Continuum Method for Modeling Surface Tension”. J. Comput. Phys. 100.335–354.1992. [6] ANSYS, “ANSYS Fluent Theory Guide 18.1” (2017). [7] Mandhane, J. M., G. A. Gregory, and K. Aziz, “A Flow Pattern Map for Gas-Liquid Flow in Horizontal Pipes,” Intern. J. Multiphase Flow, 1,537-553 (1974). [8] Wilcox, D. C.” Turbulence Modeling for CFD.” DCW Industries, Inc. La Canada, California.1998. [9] Menter, F. R. “Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications.” AIAA Journal. 32(8).1598–1605. August 1994. [10] Youngs, D. L. “Time-Dependent Multi-Material Flow with Large Fluid Distortion.” Numerical Methods for Fluid Dynamics. K.W. Morton and M. J. Baines, editors. Academic Press. 1982.
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New perspectives on clustered linear regression Jared Lawrence, Jourdain Lamperski Department of Industrial Engineering Jared Lawrence is a junior industrial engineering student originally from River Vale, NJ. He is passionate about data, modeling, and optimal decision-making. He currently plans to pursue a graduate degree in operations research. Jared Lawrence
Jourdain Lamperski
Jourdain Lamperski is an assistant professor in the department of industrial engineering. He received a PhD from the MIT Operations Research Center in 2020 and a BS in mathematics from the University of Pittsburgh in 2015. His research interests include optimization, machine learning, and applications in healthcare.
Significance Statement
In this paper, we consider a machine learning model called Clustered Linear Regression (CLR). We establish the sample complexity of CLR and propose a new algorithm for computing its parameters. Through computational experiments we gain an understanding of when the algorithm performs well and when CLR is useful.
Category: Methods
Keywords: clustering, linear regression, machine learning
ABSTRACT
Clustered Linear Regression (CLR), a method combining unsupervised and supervised learning, is not well understood. CLR has the potential to combine two steps of analysis – clustering and linear regression – into a single step, and to perform well where other methods fail. However, not enough is known about when CLR should be applied, and under what conditions it performs well. We perform computational experiments on a synthetic and a real data set to help answer these questions. These experiments are performed with a novel gradient based iterative algorithm tested with 3 different initializations. Additionally, we present an analysis of sample complexity to answer the question of how many samples are needed.
1. INTRODUCTION
Motivation. Linear regression is a simple, but powerful, model that finds a number of applications in a variety of fields. For example, it is used in finance to predict future stock prices with current market data [1] and in healthcare to predict future costs with past claims data [2]. The model assumes a linear relationship between the inputs, collected into an n-dimensional vector a (e.g., current market data), and the output y (e.g., future stock price), namely (1) where x is an n-dimensional vector of coefficients. Accordingly, given inputs, we can use equation (1) to compute the corresponding output, or in other words, make a prediction. We construct the coefficients of the model with inputoutput data (a1,b1),…,(am,bm ); more specifically, we solve the optimization problem (2) That is, we choose the coefficients that minimize the sum of squared errors between predicted outputs and actual outputs. From a geometric standpoint, we choose the hyperplane that best fits the data. We note that we can solve (2) efficiently. In practice, it is common to first cluster the data into groups and then construct a linear regression model for each group. (K-means [3] is one commonly used clustering method and groups points by proximity.) This potentially removes nonlinearities between the inputs and output (that linear regression cannot model), which leads to prediction accuracy improvements. However, if clustering and linear regression are implemented independently, we may nonetheless witness poor performance; see Figure 1. We see that K-means clusters the data into two V-shaped clusters, while the data can clearly be separated into line-shaped clusters.
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of data points needed to recover the true coefficient up to some degree of accuracy. Finally, we assess the performance of our algorithm on synthetically generated data and a real dataset.
2. METHODS Figure 1: (Left) Unclustered data and poorly fit regression. (Middle) Data clustered by K-means resulting in poorly fit regressions. (Right) Intuitive result with correct clustering and regressions.
Clustered Linear Regression (CLR) is a model that addresses this issue. CLR considers the clusters that minimize mean squared error (MSE) to k linear regressions across all the data. Formally, we solve the following optimization problem to determine the clusters and the coefficients x_1,…,x_k for each cluster.
In this section, we introduce a statistical model for CLR (mainly so that we can establish a sample complexity guarantee), our algorithm, and the setup for the computational experiments.
2.1 Model
For CLR, the data is assumed to be sampled from k linear distributions with noise as followed, (4) where the cluster that each data point belongs to is not known a priori, • • • is Sub-Gaussian with variance proxy σ2, and
(3) We note, however, that while we can solve (2) efficiently, solving (3) is NP-hard [4]. This is not too surprising as in addition to constructing coefficients, (3) also involves determining clusters. Literature review. Spath [5] first considered CLR and proposed a greedy algorithm that, at each iteration, constructs a linear regression model for each current cluster and then constructs new clusters [5]. Later DeSarbo [6] proposed another greedy algorithm involving an expectation maximization approach. Greedy algorithms make the locally optimal decision at each iteration to determine the coefficient estimates that minimize MSE. However, as (3) is a nonconvex optimization problem, iterative methods will terminate at a local minimum of (3) that is not necessarily a global minimum causing a sub-optimal result. There have been several studies that attempt to boost the performance of these methods (towards finding better local minima) using other techniques such as column generation [4], simulated annealing [7], and variable neighborhood search [8]. There has been a limited amount of work on cluster initialization; previous work randomly initializes the clusters. Other work considers an extension for prediction [9] and a mixed integer formulation [10]. Several applications of CLR, which have been studied, include evaluating trade show performance, quantifying consumer satisfaction, and stock keeping unit clustering for forecasting [4][6]. Contributions. Our contributions to the literature on CLR are as follows. First, we present a gradient based iterative algorithm; we propose 3 different ways to initialize the method. Next, we present an analysis of the sample complexity of CLR, namely the number
•
2.2 Algorithm
We present Algorithm 1 below as a first order method that can be used to solve (3). Algorithm 1: 1. Initialize clusters using a prechosen method. 2. Construct regression coefficients for each cluster. 3. While current iteration is less than preset maximum and change in MSE remains above a preset tolerance: i. Holding cluster assignment constant, use gradient to adjust regression coefficients. ii. Holding regressions constant, assign points to the cluster that minimizes error. Existing iterative algorithms solve for k regressions each iteration. Algorithm 1 presents an alternative approach that has lower cost per iteration, but more total iterations, as each iteration adjusts the coefficients rather than resolving completely. Like previous methods, Algorithm 1 will terminate at a local minimum that is not necessarily a global minimum. To reach a better local minimum, prior studies run their algorithm multiple times with different initial clusters and choose the result from the best run. This is more effective than running the algorithm once as the local minimum reached is heavily dependent on initialization [4][6][9][10]. However, prior studies almost exclusively use random initialization for cluster assignment. We experiment with 3 different cluster initialization schemes: Random (denoted CLR Rand), K-means (denoted CLR K-means), and Gaussian Mixture (denoted 39
CLR GM).
variables as predictors, and we stripped the datapoints of labels.
2.3 Experiment Setup
In addition to optimization problem (3), we apply another optimization problem that we denote by CLR Hybrid to the iris dataset. The objective function of CLR Hybrid is a weighted average of the linear regression
We perform computational experiments on synthetic and real data. We assess performance using two metrics: accuracy – as correct assignment is known in these examples – and MSE. In the experiments, we run Algorithm 1 with a maximum of 1000 iterations and a stopping threshold of 10^(-6). We run CLR Rand with 5 different random initializations, and we run CLR K-means and CLR GM one time because K-means and Gaussian Mixture generally yield the same or similar clusters across runs. To determine MSE for K-means and Gaussian Mixture, a linear regression is fit on each resulting cluster.
objective and K-means objective. In our experiments, we set the weight of the linear regression objective to .95 and the K-means objective to .05.
3. RESULTS
3.1 Sample Complexity
2.3.1 Synthetic Data Experimental Setup
Sample complexity is the number of samples needed to recover the true coefficients up to some degree of accuracy. Determining the sample complexity of (3) is an open problem. We show (5)
We perform an experiment using the following parameter values to define the ground truths: •
Number of clusters K: 3, 5, 10
•
Size of the data m: 1000, 2500, 10000
•
Number of variables n: 10, 15, 25
•
Cluster distance apart d: 0, 1, 3
but omit the proof due to stay within the page limit. Here x ̂i denotes the optimal coefficients and x_i the ground truth regression coefficients for the ith cluster.
•
Noise standard deviation σ: 0, ¼, ½, ¾, 1
3.2 Synthetic Data Experiment Results
The performance for CLR with any initialization, referred to as CLR, greatly varies under different conditions as seen in Table 1. The performance of all methods decreases as noise level or number of clusters increase with CLR performance being especially sensitive. The number of variables and size of data affect model performance as expected: As number of variables increases, performance moderately decreases, and as size of data increases, performance moderately increases. The differences are similar regardless of whether CLR, K-means, or Gaussian Mixture is used. A factor of particular interest is cluster distance. As expected, the more overlapping the clusters, the worse K-means and Gaussian Mixture perform. Interestingly, CLR Rand performance stays consistent regardless of cluster distance.
We generate the synthetic data as follows. We generate points for the ith cluster from a multivariate normal distribution with mean d×e_i and covariance matrix I, where e_i is the ith unit vector. Accordingly, when d is large, the clusters are more separated and when d is small, the clusters are overlapping. We generate each coefficient from a uniform distribution on [-1, 1], and we determine response values by multiplying the predictors by the coefficients and then adding normally distributed noise (with mean 0 and standard deviation σ).
2.3.2 Iris Data Experiment Setup
We use the Iris dataset. The dataset consists of 150 datapoints evenly split across 3 iris species and each observation contains 4 variables. We arbitrarily chose petal width as the output, leaving the other three
Table 1. Accuracy and mean squared error for each factor averaged across all synthetic data trials Cluster Distance Algorithm
Number of Clusters
Metric 0 Accuracy 0.55
Number of Variables
Size of Data (thousands)
Noise Level
1
3
3
5
10
10
15
25
1
2.5
10
0
0.25
0.50
0.75
1
0.55
0.56
0.83
0.61
0.22
0.57
0.56
0.53
0.48
0.56
0.63
0.73
0.63
0.54
0.47
0.41
0.23
0.23
0.24
0.25
0.27
0.18
0.18
0.22
0.30
0.24
0.23
0.23
0.08
0.12
0.20
0.32
0.45
Accuracy 0.27 CLR K-Means MSE 0.40
0.28
0.57
0.52
0.37
0.23
0.40
0.38
0.33
0.36
0.37
0.38
0.45
0.41
0.37
0.33
0.30
0.41
0.29
0.63
0.35
0.13
0.24
0.33
0.53
0.35
0.37
0.38
0.28
0.30
0.36
0.41
0.49
Accuracy 0.27
CLR Rand
K-Means
MSE
0.31
0.69
0.50
0.41
0.35
0.46
0.43
0.37
0.42
0.42
0.42
0.43
0.43
0.43
0.42
0.41
0.85
0.90
1.28
1.21
0.95
0.88
0.80
0.98
1.25
0.97
1.03
1.04
0.94
0.94
0.99
1.06
1.13
Accuracy 0.39
MSE
0.47
0.70
0.71
0.54
0.32
0.57
0.54
0.46
0.47
0.53
0.57
0.73
0.60
0.50
0.42
0.36
0.33
0.30
0.19
0.42
0.28
0.13
0.18
0.25
0.40
0.28
0.28
0.27
0.12
0.17
0.27
0.36
0.46
Gaussian Accuracy 0.39 Mixture MSE 0.91
0.50
0.91
0.75
0.61
0.44
0.67
0.62
0.52
0.53
0.61
0.66
0.74
0.65
0.59
0.54
0.49
0.82
0.47
0.72
0.74
0.73
0.45
0.62
1.13
0.86
0.76
0.58
0.44
0.52
0.68
0.89
1.13
CLR GM
MSE
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Table 2. Iris Experimental Results CLR Rand
CLR K-means K-means
CLR GM
Gaussian Mixture
CLR Hybrid
Actual (correct clusters)
Accuracy
0.44
0.49
0.89
0.75
0.97
0.92
1.00
MSE
0.007
0.008
0.028
0.009
0.024
0.025
0.024
3.3 Iris Data Experiment Results
4. DISCUSSION
4.1 Sample Complexity Discussion
The sample complexity of CLR is the same as the sample complexity of linear regression, except for two additional factors of k. One factor of k follows from the fact that CLR involves k linear regressions, and the other factor of k follows from the fact that points can be misassigned.
4.2 Synthetic Data Experiment Discussion
As mentioned in 3.2, CLR performance is more sensitive to the number of clusters and noise level than K-means and Gaussian Mixture. A potential explanation is that an increase in either leads to more and potentially worse local minima of (3). The results are consistent with sample complexity in (5) as number of clusters, k, and noise level, σ, both appear as squared terms in the numerator. Intuitively, noise level on the observed outputs has a greater impact on CLR, compared to K-means and Gaussian Mixture as the output is more important to cluster assignment in CLR. A greater number of clusters also leaves less data for each cluster, possibly causing CLR to struggle more due to the relative importance of a single variable, the output. Interestingly, as cluster distance decreases, other methods struggle while CLR Rand performance is consistent. This indicates that when predictors are highly overlapping, as seen in Figure 1, CLR Rand may be better at recovering the ground truth. To reiterate, CLR works best with a small number of clusters and less noise. It also outperforms K-means and Gaussian Mixture when clusters are overlapping. Across trials where these conditions are satisfied – there are 3 clusters, the noise level is 0.25 or 0.50 (noiseless trials are excluded for being unrealistic), and the cluster distance is 0 or 1 – the average accuracy of CLR Rand is 86.3% compared to 68.7% for Gaussian Mixture and 40.8% for K-means. As CLR K-means and CLR GM perform similarly to their respective initializations, if the conditions for choosing CLR over other methods are satisfied, CLR Rand should be the chosen method.
Actual (no clusters)
0.036
regression alone. Overfitting is evident as the MSE values for the CLR Rand and CLR K-means models are about much lower than MSE with true clustering. Another behavior that CLR can exhibit, contributing to overfit, is that one cluster can steal points from another faraway cluster. If the regression hyperplane of one cluster goes near the points of another, some points may be misassigned to the former because that is the closest hyperplane. CLR Hybrid was able to partially fix both problems reigning in overfit. By partially weighting the objective with a K-means objective, the stealing points effect is mitigated as the penalty for points joining a faraway cluster will be high. A hybrid objective also helps in a highly linear dataset as the clusters are more likely to group nearby points together.
5. CONCLUSIONS
CLR is a method where various approaches have been tried, but some basic questions had not been answered. We applied a gradient based iterative algorithm to the problem, opening the door to other first order methods that may run faster and/or reach better local minima. By deriving sample complexity, we contribute to the question of how many samples are needed. By running an experiment on synthetically generated data, we learn what can affect CLR performance and when it may be advantageous to use CLR over other methods. Additionally, we did so while comparing different initializations. Specifically, experimenting on synthetic data showed that when there are a small number of clusters, the clusters are overlapping, and the output is not too noisy, CLR Rand can have much higher accuracy than K-means and Gaussian Mixture. Lastly, we explain why a hybrid objective can help prevent overfitting. Overall, our work contributes to a greater understanding of CLR, so that it can become a method to use when others fail.
6. ACKNOWLEDGMENTS
Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.
4.3 Iris Data Experiment Discussion
CLR can easily overfit the data if it is already close to linear. Without any clustering, the R-squared value for a linear regression fit to the Iris data is 0.938 indicating that the data can be well summarized by linear 41
REFERENCES [1] Bini, B. S., & Mathew, T. (2016). Clustering and regression techniques for stock prediction. Procedia Technology, 24, 1248-1255. [2] Bertsimas, D., Bjarnadóttir, M. V., Kane, M. A., Kryder, J. C., Pandey, R., Vempala, S., & Wang, G. (2008). Algorithmic prediction of health-care costs. Operations Research, 56(6), 1382-1392. [3] Lloyd, S. P. (1957). Least squares quantization in PCM. Technical Report RR-5497, Bell Lab, September 1957. [4] Park, Y. W., Jiang, Y., Klabjan, D., & Williams, L. (2017). Algorithms for generalized clusterwise linear regression. INFORMS Journal on Computing, 29(2), 301-317. [5] Späth, H. (1979). Algorithm 39 clusterwise linear regression, Computing 22(4): 367–373. [6] DeSarbo, W. S. and Cron, W. L. (1988). A maximum likelihood methodology for clusterwise linear regression, Journal of Classification 5(2): 249–282. [7] DeSarbo, W. S., Oliver, R. L., & Rangaswamy, A. (1989). A simulated annealing methodology for clusterwise linear regression. Psychometrika, 54(4), 707-736. [8] Hansen, P. (2005). Variable Neighborhood Search for Least Squares Clusterwise Regression G. Caporossi. Les Cahiers du GERAD ISSN, 711, 2440. [9] Gitman, I., Chen, J., Lei, E. and Dubrawski, A. (2018). Novel prediction techniques based on clusterwise linear regression, ArXiv abs/1804.10742. [10] Bertsimas, D., Sobiesk, M., & Wang, Y. Optimal Predictive Clustering.
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Generation of obese adipose tissues from induced pluripotent stem cells (iPSCs) Katelyn E. Lipaa,b and Hang Lina,b Department of Bioengineering, Department of Orthopaedic Surgery a
Katelyn E. Lipa
Hang Lin
b
Katelyn Lipa is sophomore bioengineering student from Doylestown, PA. Her passions include tissue engineering, with a focus on in vitro methods to simulate in vivo circumstances. After graduation, she intends to pursue a PhD/MD graduate program to further her research and knowledge. Dr. Lin is an assistant professor (tenure track) working in the Department of Orthopaedic Surgery. His research interests are to understand the relationship between aging and osteoarthritis (OA), develop disease-modifying drugs to treat OA, and regenerate articular cartilage through tissue engineering strategy. Currently, Dr. Lin is supported by both internal and external grants, including several ones from the NIH.
Significance Statement
Recent findings support a biochemical mechanism correlating obesity and osteoarthritis (OA). However, there are lacking in vitro models to study how obese fat tissues influence joint elements, such as cartilage. We created adipose tissue from induced pluripotent stem cells (iPSCs) and induced obesity-like changes. To the best of our knowledge, this is the first in vitro iPSCderived obese adipose tissue model, which allows us to study fat-joint interaction in OA pathogenesis.
Category: Experimental Research
Keywords: induced pluripotent stem cells (iPSCs), in vitro disease modelling, osteoarthritis (OA), obesity Abbreviations: Osteoarthritis (OA), induced pluripotent stem cells (iPSCs)
ABSTRACT
Osteoarthritis (OA) is multifactorial joint disease, leading to joint inflammation, chronic pain, and restricted range of motion. Though OA is correlating with a variety of risk factors, one of the dominating correlations occurs with obesity. This relationship was inferred as due to additional joint loading caused by additional weight, but contrastingly, hand OA also associates with obesity, uprooting this belief. Therefore, other biochemical factors should be evaluated to correlate OA and obesity. Currently, there are little to no models to simulate and evaluate changes in adipose tissues during obesity, or assess the influences of these changes on joint health. To address this, we developed a 3-dimensional (3D) scaffold using induced pluripotent stem cells (iPSCs)-derived progenitor cells and induced hypertrophy, a represent phenotype observed in obese adipose tissues in humans, using sodium palmitate. Results showed that inflammatory factors including interleukin 8 (IL-8) and adipokine leptin were significantly increased in obese fat models, when compared to the control that was not exposed to sodium palmitate. This new in vitro obese fat model will be applied to test the effects of their secretion on joint tissues in OA pathogenesis.
1. INTRODUCTION
1.1 The growing problems within obesity and osteoarthritis (OA)
Osteoarthritis (OA) is a debilitating joint disease impacting millions worldwide and is a leading cause of chronic pain. OA causes pathogenic changes to all joint elements, including cartilage degradation, bone remodeling, osteophyte formation, and synovitis [1, 2]. Though there are many risk factors for OA [3], one of the biggest risk factors is obesity (Body Mass Index (BMI) ≥ 30). It is predicted that by 2030, roughly one in two United States citizens will be obese, and one in four will be morbidly obese [4]. Thus, it is important to determine mechanisms correlating OA and obesity. The correlation was initially perceived as due to the additional mechanical load on the joint, but conflictingly, obesity also correlates with OA in nonweight-bearing joints, such as the hand [5]. Therefore, other biochemical factors–in particular those secreted by fat tissues–are being evaluated for their potential impact on OA progression [6]. Markedly, the hypertrophy of adipose tissue releases numerous proinflammatory cytokines and adipokines, including leptin, tumor necrosis factor α (TNFα), Interleukin 8 (IL-8) and IL-6 [7]. Interestingly, increased levels of these cytokines are found in OA patients [8]. However, as of now, there is very limited study to create in vitro model to simulate the changes of fat tissues in obesity and the influences in OA pathogenesis. The goal of this study was to create 3-dimensional (3D) in vitro obese fat models from induced pluripotent stem cells (iPSCs).
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1.2 The importance of using a 3D in vitro disease model
In vitro models have emerged as an opportune way to develop disease models and simulate dynamic human systems. These engineered structures avoid moral and ethical implications of direct human testing [9] and allow different diseases to be modelled and drugs tested, before moving onto the next stage of drug development. In vitro models also avoid the issues caused by dissimilarities between animal and human physiology and biochemistry, as well as the response to drug treatments [10]. Since the derivation of iPSCs from human fibroblasts, they are suggested as ideal for in vitro models as a cheap, unlimited cell source that can be differentiated into any tissue type [9,11]. Thus, generating an in vitro fat model using iPSCs is a step towards modeling diseases concerning adipose tissue. Though used for decades, 2-dimensional (2D) monolayer culture may not be able to best represent in vivo conditions, and thus many researchers are deviating towards 3D models [12]. In addition, 2D models have limited cell-to-cell and cell-to-extracellular matrix (ECM) interactions compared to in vivo tissues. In comparison, 3D culture is advantageous in its higher complexity and its allowance of cell-to-ECM interactions [13]. iPSCs have been successfully differentiated into fat cells in previous studies. However, these studies were done under monolayer conditions, and exhibited low differentiation efficacy [14]. This suggests that the lobular, highly vascular network of adipose tissue in vivo cannot be accurately modeled in 2D culture. In efforts to remedy this, studies have begun using suspended iPSC-embryoids to generate fat cells [15]. However, to our knowledge, no study has used a 3D hydrogel scaffold, or used these scaffolds to mimic obese adipose conditions.
1.3 Use of palmitic acid (PA)
In this study, sodium palmitate, a salt of palmitic acid is evaluated for its ability to induce hypertrophy of iPSCs-derived adipocytes [16]. Palmitic acid is a saturated fatty acid that induces an inflammatory response via a multitude of signaling pathways and is shown to increase lipid drop size within adipocytes [17]. Increased intake of palmitic acid is highly associated with obesity and obesity-related metabolic disorders and is shown to increase body weight [18-20]. In this study, we first differentiated iPSCs into adipose tissues within a 3D scaffold, and then used sodium palmitate to induce obese-like changes. We hypothesize that the addition of sodium palmitate into the culture of iPSCs-derived adipose tissue would result in adipose hypertrophy and secretion of proinflammatory cytokines and adipokines.
2. MATERIALS AND METHODS
2.1 Creation of stiff hydrogel iPSC scaffolds
iPSCs were purchased from Alstem, Richmond, CA. Passage 5 (P5) iPSCs-derived progenitor cells were seeded in T150 (Corning Inc., Corning, NY) flasks in growth medium until they reached ~80% confluency (approximately 14 days). Growth medium was DMEM/ F12 (Fisher Scientific, Hampton, NH) with 10% FBS (Sigma-Aldrich, St. Louis, MD) and 1% AntibioticAntimycotic (Sigma-Aldrich). These cells were then washed and photo-crosslinked into 3D methacrylated gelatin (GelMA) scaffolds (6-mm diameter by 2-mm height) with 0.15% (w/v) lithium phenyl-2,4,6-trimethylbenzoyl phosphinate (LAP) (Sigma-Aldrich, St. Louis, MO). To photo-crosslink, scaffolds were exposed to an UV flashlight at 395nm for two minutes. Gels were divided to different well plates with 4 gels in each experimental group.
2.2 Growth medium supplements
Two steps of differentiation medium were used, and both adipogenic mediums were made following a protocol derived from researchers at Côte d’Azur University. This method was used previously to successfully induce adipogenesis of iPSCs [21]. Medium I was made with the following method: EBM-2 (Lonza, Walkersville, MD) supplemented with 0.1% FBS, 1x Antibiotic-Antimycotic, 5μM SB431542 (Abcam, Cambridge, UK), 25μg/mL Ascorbate (Sigma-Aldrich), 4μg/mL hydrocortisone (Sigma-Aldrich), 10ng/mL EGF (Fisher Scientific), 0.5mM IBMX (Sigma-Aldrich), 0.25μM Dexamethasone (Sigma-Aldrich), 0.2nM T3 (Sigma-Aldrich), 1μg/mL ITS+ (Fisher Scientific), and 1μM Rosiglitazone (Sigma-Aldrich). Medium II was identical to Medium I, without adding IBMX and Dexamethasone. IBMX and Dexamethasone induce CCAAT-enhancer-binding protein δ (C/EBPδ) and C/ EBPβ pathways. Expression of C/EBPδ and C/EBPβ induce C/EBPα and peroxisome proliferator-activated receptor γ (PPARγ) expression, which are both positive modulators of adipogenesis [22]. These exert positive feedback on one another, and thus continued IBMX and Dexamethasone supplementation is not needed.
2.3 Supplementing Medium II with sodium palmitate
200mM Sodium Palmitate (Sigma-Aldrich) was made with a 70% methanol solvent. To dissolve this solution, the mixture was placed in an oven at 50°C overnight. 0.2% (v/v) was added to Medium II with 1g/100mL bovine serum albumin (BSA) (Sigma-Aldrich). The mixture was placed in an incubator and shaken at 37°C until homogenously dissolved, then filtered and added to the well plates.
2.4 Scaffold medium treatments
Gels were maintained in growth medium for 1 day before the medium was completely removed. After removal, cells were quickly washed with phosphatebuffered saline (PBS) (Corning Inc.) and Medium I was
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added to the plates, marking Day 0. On Day 3, Medium I was removed and Medium II added. To supplement adipogenesis, this medium was changed weekly. On Day 21, the mediums were aspirated and replaced, the experimental group receiving Medium II supplemented with 0.2% sodium palmitate. The scaffolds were conditioned in this medium for 10 days (replaced on day 7) then collected for analysis.
Moreover, significantly higher expression of Leptin and IL-8 was observed in PA-treated group, indicating that hypertrophic adipocytes could generate proinflammatory cytokine and adipokines (α=0.05) (Figure 2).
2.5 Methods for Analysis
RNA Extraction was completed using a Qiagen RNeasy Plus Universal Kit (Qiagen, Germantown, MD). Real-Time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was done using a QuantStudio 5 RealTime PCR System (Applied Biosystems, Foster City, CA), using 96-well plates and β-actin as the housekeeping gene. Relative gene expression was found using the 2-ΔΔCt method and SYBR green chemistry. Oil Red O staining was done using the following method: scaffolds were rinsed with PBS several times, then fixed in 4% paraformaldehyde (PFA) for 1 hour at room temperature. PFA was removed and scaffolds were then rinsed in 60% isopropanol, before it was quickly aspirated. Oil Red O stock solution was filtered, and scaffolds were submerged in the solution for 30 minutes. The solution was then removed, and scaffolds were washed in PBS to eliminate excess stain. Gels were transferred to a clean plate and images were taken using an Olympus SZX16 Stereo Microscope (Olympus, Waltham, MA).
3. RESULTS
After 31 days, Oil Red O-stained gels were imaged (Figure 1). In the both groups, we observed significant amounts of staining, indicating successful generation of adipocytes from iPSCs. In addition, cells with higher oil red staining were noticed in the sodium palmitatetreated group, when comparing to the control group, implying a hypertrophy phenotype.
Figure 1: Increased Lipid droplet size in experimental Palmitic Acid group. Control group maintained in adipogenic medium (Left) versus adipogenic medium supplemented with sodium palmitate (Right). Images were taken using an Olympus SZX16 Stereo Microscope.
Figure 2: Palmitic acid increased IL-8 and Leptin expression. Induced pluripotent stem cells (iPSCs)-derived adipose tissues were subjected to sodium palmitate culture (Group w/PA). Untreated tissues were used as the control (Group Control). PCR results showed sodium palmitate increased interleukin (IL)-8 (Left) expression in the experimental group, consistent with an inflammatory phenotype (p=0.0072, CI: 1.930-5.5688). Leptin (Right) was increased in the experimental group, which typical in obesity (p= 0.0279, CI: 0.3037-2.554). *, p<0.05; **, p<0.01.
4. DISCUSSION
As seen, the 2-step differentiation medium was successful in initiating the adipogenic potential of iPSCs. Sodium palmitate was seen to induce hypertrophy of adipocytes. This agrees with the results from studying mature 3T3-L1 adipocytes [23]. High inflammatory cytokine expression was seen through examining the expression levels of leptin and IL-8. A study found that IL-8 concentrations in obese (BMI: 32.7 ± 3.29) versus lean patients (BMI: 22.85 ± 2.48) increased to 4.31 ± 1.43 pg/ml compared to 3.24 ± 1.07 pg/ml [24]. Additionally, investigations of serum leptin levels found that obese subjects (BMI: 35.1 ± 7.2) had leptin levels of 31.3 ± 24.1 ng/ml. Comparing to lean patients (BMI: 23.0 ± 2.5) had leptin levels of 7.5 ± 9.3 ng/ml, which is remarkedly lower, consistent with the study [25]. Further genes should be tested in the future, including Interleukin 1 β (IL1β), matrix metalloproteinase 12 (MMP12), and MMP13 to confirm the hypertrophy phenotype of sodium palmitateinduced adipose tissue. The increase of cell size in the sodium palmitate group and lipid content needs to be further confirmed through additional quantitative assays, but current results imply these iPSC-s derived adipocytes function like native adipocytes. With these promising results, the study may be applied to future research. The pro-inflammatory cytokines released by hypertrophic adipose tissue can be co-cultured with other tissues to test the effects of the inflamed fat tissue on other types of tissues, such as synovial tissue or cartilage. For
45
example, the pro-inflammatory cytokines released may allow macrophage polarization in synovial tissue and thus may cause cartilage degradation, a marker phenotypically characteristic of OA.
REFERENCES
Further testing using longer exposures to sodium palmitate and utilization of different ratios should be done to understand if this may cause further adipocyte hypertrophy. Repeated, extensive testing using larger sample sizes and different iPSC lines should also be done to promote better, standardized results.
[2] He Y, et al. Pathogenesis of Osteoarthritis: Risk Factors, Regulatory Pathways in Chondrocytes, and Experimental Models. Biology, 2020. 9(8): p.194
Though additional tests should be biorun, iPSCs show high adipogenic capacity. Sodium palmitate can induce adipocyte hypertrophy and higher inflammatory cytokine expression. The study also shows inflamed adipocytes secrete inflammatory factors associated with OA. As obesity is defined as an enlargement of adipocytes, the results of this study show the high likelihood for a biochemical affiliation between OA and obesity.
[4] Ward Z, et al. Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity. The New England Journal of Medicine, 2019. 381: p. 2440-2450
5. CONCLUSION
[6] Dahaghin S, et al. Do metabolic factors add to the effect of overweight on hand osteoarthritis? The Rotterdam Study. Ann Rheum Dis., 2007. 66(7): p. 916-920.
In this study, iPSCs were successfully differentiated in vitro into adipose tissue using a 2-step differentiation medium protocol. After differentiation, the experimental group was cultured in medium supplemented with sodium palmitate, a salt of palmitic acid. Cells exposed to palmitic acid showed a hypertrophic phenotype, shown via an enlarged lipid droplet. Inflammatory cytokines leptin and IL-6 were highly expressed in the experimental group, both of which are also expressed in OA tissues.
6. ACKNOWLEDGEMENTS
We would like to acknowledge the support from Department of Orthopaedic Surgery School of Medicine and Swanson School of Engineering. We especially thank Zhang Xiurui, Yuchen He, and Jian Tan for their guidance throughout the experiment’s progression.
[1] Hunter D. et al. Osteoarthritis. BMJ (Clinical research ed.), 2006. 332(7542): p. 639-642.
[3] Cimmino, et al. Risk factors for osteoarthritis. Semin Arthritis Rheum., 2004. 34(6): p. 29034
[5] Reyes C, et al. Association Between Overweight and Obesity and Risk of Clinically Diagnosed Knee, Hip, and Hand Osteoarthritis: A Population-Based Cohort Study. Arthritis Rheumatol., 2016. 68(8): p. 1869-1875.
[7] Makki, et al. Adipose Tissue in Obesity-Related Inflammation and Insulin Resistance: Cells, Cytokines, and Chemokines. ISRN Inflammation, 2013. [8] Wang T, et al. Pro-inflammatory cytokines: The link between obesity and osteoarthritis. Cytokine & growth factor rev., 2018. 44: p. 38-50. [9] Benam K, et al. Engineering In Vitro Disease Models. Annu. Rev. Pathol. Mech. Dis., 2015. 10: p. 195-262 [10] Pearson R. In-vitro techniques: can they replace animal testing? Hum Reprod., 1986. 1(8): p 559-560 [11] Takahashi K, et al. Induction of pluripotent stem cells from adult human fibroblast by defined factors. Cell, 2007. 131(5): p. 861-872. [12] Duval K, et al. Modeling Physiological Events in 2D vs. 3D Cell Culture. Physiology, 2017. 32(4): p. 266277. [13] Centeno E, et al. 2D versus 3D human induced pluripotent stem cell-derived cultures for neurodegenerative disease modelling. Mol Neurodegener, 2018. 13(1): p. 27. [14] Chen Y., et al. Small molecule mesengenic induction of human induced pluripotent stem cells to generate mesenchymal stem/stromal cells. Stem cells Transl Med., 2012. 1(2): p. 83-95.
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[15] Yao X, et al. Human Pluripotent Stem Cells: A Relevant Model to Identify Pathways Governing Thermogenic Adipocyte Generation. Front. Endocrinol, 2020. 10(932). [16] Sikaris K. The clinical biochemistry of obesity. The Clinical biochemist, 2004. 25(3): p. 165-181. [17] Haczeyni F, et al. Causes and mechanisms of adipocyte enlargement and adipose expansion. Obes Rev., 2018. 19(3): p. 406-420. [18] Saraswathi V, et al. Lauric Acid versus Palmitic Acid: Effects on Adipose Tissue Inflammation, Insulin Resistance, and Non-Alcoholic Fatty Liver Disease in Obesity. Biology. 9(11): p. 346. [19] Hellmann J, et al. Increased saturated fatty acids in obesity alter resolution of inflammation in part by stimulating prostaglandin production. J Immunol, 2013. 191(3): p. 1383-1392. [20] Deng Y, et al. High Level of Palmitic Acid Induced Over-Expressed Methyltransferase Inhibits AntiInflammation Factor KLF4 Expression in Obese Status. Inflammation, 2020. 43: p. 821-832. [21] Hafner A, et al. Differentiation of Brown Adipocyte Progenitors Derived from Human Induced Pluripotent Stem Cells. Methods Mol Biol., 2016. 1773: p. 31-39. [22] Ahmad B, et al. Molecular Mechanisms of Adipogenesis: The Anti-adipogenic Role of AMPActivated Protein Kinase. Front Mol Biosci, 2020. 7(76). [23] Morita N, et al. Novel Mechanisms Modulating Palmitate-Induced Inflammatory Factors in Hypertrophied 3T3-L1 Adipocytes by AMPK. J Diabetes Res, 2018. 2018. [24] Straczkowski M, et al. Plasma Interleukin-8 Concentrations Are Increased in Obese Subjects and Related to Fat Mass and Tumor Necrosis Factor-α System. J Clin Endocrinol Metab., 2002. 87(10): p. 4602-4606. [25] Considine R, et al. Serum Immunoreactive-Leptin Concentrations in Normal-Weight and Obese Humans. The New England Journal of Medicine, 1996. 334: p. 292-295.
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Simultaneous local and bulk polymer crystallization analysis using microfluidic dilatometry Ryan J. MacElroy and Sachin S. Velankar Sachin Velankar Research Lab, Department of Chemical and Petroleum Engineering> Ryan MacElroy is a chemical engineering student from Yardley, PA. His research interests include polymers and process intensification, and he hopes to earn a Ph.D. in chemical engineering after he graduates in 2023. Ryan J. MacElroy
Sachin S. Velankar
Dr. Sachin Velankar is a professor in the Department of Chemical & Petroleum Engineering, Swanson School of Engineering, and a courtesy appointment in the Department of Mechanical Engineering and Materials Science. He conducts research on a variety of topics in soft materials including polymers, capillary phenomena, colloids, and rheology.
Significance Statement
Polymer crystallization research is essential to understand a polymer’s properties. However, crystallization analysis of two-phase polymer solutions via bulk dilatometry can be costly. The proposed microfluidic dilatometer requires small samples and simultaneously characterizes the local and bulk crystallization kinetics, reducing cost and saving time.
Category: Device Design
Keywords: polymers, crystallization, dilatometry, microfluidics
Ingenium 2022
ABSTRACT
Polymer crystallization kinetics can be characterized by the local spherulite growth velocity and the bulk crystallization rate. For the case of polyoxacyclobutane (POCB) and water mixtures of interest, determining these rates conventionally is inefficient for two reasons. First, it requires two separate experiments, one for local growth velocity measurement and one for bulk crystallization analysis. Second, while differential scanning calorimetry (DSC) can measure the bulk crystallization rate of a single-phase polymer, POCB-water mixtures are two-phase prior to cocrystallization, which is beyond the capabilities of DSC. Thus, bulk crystallization studies require dilatometry, which requires large volumes of material. The microfluidic dilatometer allows for local crystallization to be analyzed via microscopy and bulk crystallization to be analyzed via dilatometry- all in one experiment. It can dramatically increase the rate and facility of experimentation for determining crystallization kinetics while also reducing the volume of material used. A prototype of the device has demonstrated proof of concept, but more design iterations are required to improve device performance.
1.INTRODUCTION
Polymers are the foundation of everyday life. They make up clothes, construction materials, and DNA [1]. Polymers also have useful advanced applications, ranging from coatings for commercial aircraft to artificial heart valves [2]. Creating these life changing applications requires an understanding of the polymer’s structure and function. To that end, it is vital to research the crystallization kinetics of polymers, since this area directly reflects the properties of the given polymeric material. The primary goal of this project is to characterize the unusual cocrystallization behavior of polyoxacyclobutane (POCB) with water. POCB is an unusual polymer because it forms a crystalline hydrate when mixed with water at room temperature. While little is known about the POCB hydrate, it has the exciting potential to offer waste-heatbased desalination. Specifically, it may be possible to leverage the unique phase behavior of POCB to desalinate water using a purely thermal process; no membrane, pressure differential, or refrigeration would be required. This research could lead to a massive beneficial shift in the energy used to create fresh water. To fully characterize the crystallization kinetics of POCB, experiments at different molecular weights of POCB must be conducted. Crystallization kinetics can be characterized by analyzing the local crystal or spherulite growth velocity and the bulk crystallization rate. Common techniques include optical microscopy, X-ray scattering, dilatometry, and differential scanning calorimetry (DSC) [3]. DSC is widely used to measure the bulk
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crystallization rate [4]. However, analysis of two-phase POCB-water mixtures via DSC is unfeasible due to the evaporation of water in the sample. For these mixtures, dilatometry is an appropriate alternative method to determine bulk crystallization kinetics [5, 6]. A major limitation with dilatometry is that it requires a relatively large amount of polymer sample- about one gram per iteration for POCB. For lab synthesized POCB of high molecular weights, synthesizing enough POCB for flask dilatometry would be costly and time consuming. The proposed microfluidic dilatometer (MFD) requires a fraction of the polymer sample used in conventional flask dilatometry while direct observation of local crystallization simultaneously allows for the measurement of the local growth velocity. To our knowledge, no such device has been developed previously.
2.METHODS
The MFD consists of two layers. The bottom layer is aluminum (5 mm), and the top layer is glass (1 mm). The aluminum plate features a circular well and a rectangular channel, created by milling (see figure 1). The circular well’s diameter and depth is 10.5 mm and 0.5 mm, respectively. The rectangular channel’s crosssectional area is 0.5 mm by 0.5 mm. Figure 1 illustrates the current MFD design.
Figure 1: The current design for the microfluidic dilatometer. A cross-sectional view and a top-down view of the MFD is shown.
20,000 MW polyethylene oxide (PEO) synthesized at the University of Pittsburgh was used as the polymer sample due to its low cost, moderate melting point, and structural similarity to POCB. For a given trial, the sample is first placed into the well. Then, drops of mineral oil are pipetted over the sample and the glass slide is placed on top. The oil fills the sample chamber and extends into a thin channel, forming a meniscus (Figure 1). The excess oil saturates the aluminum-glass interface and flows out of the MFD due to the weight of the glass slide. To position the meniscus in the middle of the channel, oil is removed from the open channel using a slim needle. Once the MFD is assembled, it is heated to 75 °C. Then, the MFD is placed on an electric heat block set to the desired crystallization
temperature with a thin layer of mineral oil between the heat block and the bottom face of the MFD to facilitate heat transfer. As the sample crystallizes, the meniscus recedes. Therefore, the volume change per unit time of the crystallizing polymer can be calculated given the known cross-sectional area of the channel and the displacement of the meniscus. Photographs of the meniscus and the sample are taken at regular time intervals for the duration of the experiment. The photos of the spherulites allow for the calculation of the spherulitic growth velocity using ImageJ. Similarly, the photos of the meniscus allow for the quantification of the meniscus’ displacement and the calculation of the bulk volume change of the sample using the tracking feature in Blender. For this analysis to be possible, it is vital for the MFD to reach thermal equilibrium with the heat block before crystallization begins. This is because the meniscus moves due to both crystallization and the thermal contraction of the MFD upon cooling.
3. RESULTS
Figure 2 is an example photograph of local spherulites. These structures expand radially as the sample crystallizes.
Figure 2: Spherulites in a PEO sample heated to 75 °C and crystallized at 51 °C.
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Figure 3 illustrates the growth velocity profile of three candidate spherulites in the crystallization of PEO at 52 °C after heating the MFD to 75 °C. The radius of each spherulite appears to increase linearly with time.
Figure 5 presents meniscus velocity generated by an acrylic prototype of the current MFD. The meniscus displacement due to thermal contraction and due to crystallization can be distinguished by a change in meniscus velocity.
Figure 3: Spherulite radius versus time of 20,000 MW PEO heated to 75 °C and crystallized at 52 °C.
Additionally, figure 4 plots spherulite velocity against crystallization temperature after heating the MFD to 75 °C. As the holding temperature increases, the crystallization rate decreases, and thus the spherulite velocity decreases.
Figure 4: Spherulite velocities from a sample crystallized at 51 °C, 52 °C, and 53 °C.
Figure 5: Meniscus velocity for the crystallization of PEO on a 30 °C heat block using an acrylic MFD prototype.
4. DISCUSSION
To determine the local crystallization rate, photographs of the local crystallization (see Figure 2) were processed using ImageJ to calculate the surface area of a given spherulite over time. Consistent with existing experimental data [7], the spherulite growth velocity of PEO (see Figure 3) was observed to be approximately linear. Additionally, the measured spherulite velocities (see Figure 4) decrease as the temperature of the heat block increases, as expected. Photographs of the meniscus were processed using the tracking feature in Blender. Since this software measures distance using pixels, pixels were converted to millimeters using a 1 mm marking on the MFD (see Figure 1). Figure 5 illustrates how the bulk crystallization rate of a polymer sample can be determined. From 0 minutes to 4 minutes, the MFD approached thermal equilibrium with the heat block. The meniscus movement in this region is due to the thermal contraction of the MFD. At 4 minutes, the MFD can be verified to be near thermal equilibrium since the meniscus approaches zero velocity. The first spherulite formed at approximately 4.25 minutes, the point at which the meniscus velocity increases. Therefore, the region between 4.25 minutes and 7 minutes reflects the bulk crystallization rate of the sample. Using the prototype’s known channel dimensions, the bulk volume change due to the crystallization of the PEO can be calculated. The crystallization kinetics of PEO can vary with different sample dimensions and preheating temperatures [8]. Therefore, it may be suspect to
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compare experimental data from the MFD to existing literature data. However, completed experiments demonstrate that the MFD is capable of simultaneously producing local crystallization data and bulk crystallization data for a polymer sample. Still, the MFD can be improved. More experiments need to be conducted to determine measurement uncertainty. In addition, the current MFD prototype can show non-monotonic meniscus movement when placed on the heat block. As a result, the meniscus velocity due to crystallization is sometimes difficult to reliably calculate. This phenomenon may be due to inadequate temperature control from the heat block. To test this hypothesis, past experiments will be repeated on a temperaturecontrolled water bath instead of an electric heat block. If the MFD still displays non-monotonic meniscus movement, another possible explanation for the nonmonotonic behavior is the different degrees of thermal expansion of aluminum and mineral oil. To account for this, additional refinements may include better sealing of the top glass against the aluminum base. Once the MFD displays monotonic meniscus movement, cocrystallization of POCB-water mixtures will be analyzed.
5. CONCLUSION
These results demonstrate that simultaneous measurement of the local and bulk crystallization rates is possible with the MFD. The current device produced local crystallization data that is consistent with trends established in literature data. In addition, a prototype of the current device demonstrated that the volume change due to crystallization can be measured. However, further iterations on the design of the MFD are required, such as improved sealing and temperature control. These improvements will bring our lab one step closer to understanding the properties of POCB and exploring the polymer’s potential in desalination.
REFERENCES [1] H. Namazi, “Polymers in our daily life,” Bioimpacts, vol. 7, no. 2, pp. 73-74, 2017. [2] N. R. Council, Polymer Science and Engineering: The Shifting Research Frontiers. Washington, DC: The National Academies Press, 1994, p. 192. [3] N. S. Murthy, “Chapter 3 - Experimental Techniques for Understanding Polymer Crystallization,” in Crystallization in Multiphase Polymer Systems, S. Thomas, M. Arif P, E. B. Gowd, and N. Kalarikkal Eds.: Elsevier, 2018, pp. 49-72. [4] J. M. Sturtevant, “Biochemical Applications of Differential Scanning Calorimetry,” Annual Review of Physical Chemistry, vol. 38, no. 1, pp. 463-488, 1987. [5] Y. S. Yadav, P. C. Jain, and V. S. Nanda, “A Study of the Crystallization Kinetics of PVF2 by DSC and Dilatometry,” Thermochimica Acta, vol. 80, no. 2, pp. 231-238, 1984. [6] J.-W. Housmans et. al., “Dilatometry: A Tool to Measure the Influence of Cooling Rate and Pressure on the Phase Behavior of Nucleated Polypropylene,” Macromolecular Materials and Engineering, vol. 294, no. 4, pp. 231-243, 2009. [7] Y. K. Godovsky, G. L. Slonimsky, and N. M. Garbar, “Effect of molecular weight on the crystallization and morphology of poly (ethylene oxide) fractions,” Journal of Polymer Science Part C: Polymer Symposia, vol. 38, no. 1, pp. 1–21, 1972. [8] J. N. Hay, M. Sabir, and R. L. T. Steven, “Crystallization kinetics of high polymers. Polyethylene oxide—Part I,” Polymer, vol. 10, pp. 187-202, 1969.
6. ACKNOWLEDGEMENTS
Funding was provided by the National Science Foundation.
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Preliminary development of a hemoadsorption device for removal of cell-free plasma hemoglobin Anna L. Maywara, Ryan A. Orizondoa,b, Nahmah Kim-Campbellc Department of Bioengineering, bMcGowan Institute for Regenerative Medicine, cDepartment of Critical Care Medicine a
Anna Maywar was raised in Rochester, NY. Her desire to contribute to the medical field and improve the lives of others led to her interest in studying bioengineering and pursuing research in biomedical device development. Anna L. Maywar
Dr. Ryan Orizondo was born and raised in Ann Arbor, MI. His fascination with physiology and engineering led him to research in the field of artificial organs and life support devices. Ryan A. Orizondo
Nahmah Kim-Campbell
Ingenium 2022
ABSTRACT
PHb is toxic when not confined within an intact red blood cell and is generated by the rupture of red blood cells (hemolysis), a common occurrence during the use of extracorporeal therapies. The development of an extracorporeal hemoadsorption device for the selective removal of PHb from whole blood via Hp-bound agarose beads has potential to prevent the toxic effects of PHb. The early work to explore the hypothesis that creating such a device is possible entails developing a repeatable manufacturing process for devices suitably sized for rat trials, assessing acute device hemocompatibility within a rat model of extracorporeal membrane oxygenation (ECMO), optimizing methods for Hp immobilization on the bead surface, and assessment of PHb binding to Hp-containing beads. The results demonstrated the development of a consistent and repeatable manufacturing process for bead-filled columns, which includes using a peristaltic pump at a specific flow rate to pack the beads to a certain distance from the top filter of the device when placed vertically. Additionally, our small-scale studies demonstrated the ability to immobilize Hp on to the beads and the capacity of such beads to remove PHb from whole blood flow as Hp-containing beads were able to remove up to 68% (n=3) of PHb.
1. INTRODUCTION Nahmah Kim-Campbell was born in State College, PA and was raised in Smithtown, NY. Her passion for the care of critically ill children has motivated her to pursue research focused on improving outcomes in patients undergoing extracorporeal therapies.
Significance Statement
Cell-free plasma hemoglobin (PHb) is toxic to the body and high levels can be generated using extracorporeal therapies, which can increase mortality rates and ICU length of stay. Thus, targeted removal of PHb can improve patient outcomes. Haptoglobin (Hp), the body’s natural scavenger of PHb, offers a promising solution.
Category: Device Design
Keywords: device, cell-free plasma hemoglobin, haptoglobin, extracorporeal therapies
Hemolysis and the breakdown of red blood cells leads to the production of cell-free plasma hemoglobin (PHb), which is now widely regarded as toxic. The impact of PHb on nitric oxide bioavailability and/or contribution to oxidative stress via its peroxidase activity has been studied in extracorporeal therapies such as cardiopulmonary bypass and extracorporeal life support [1-8]. Its toxicity can have detrimental effects on the vasculature as well as in several organ systems [9] with levels greater than ~50mg/dL associated with >4-fold increased odds of mortality [10, 11]. The toxic effects of PHb are unfortunately not limited to its tetrameric or heterodimeric forms and extend to its downstream reaction products such as hemin [9]. The multifactorial aspect of the toxicities of PHb has hindered the development of a single definitive therapy and thus, we identify PHb itself as the most meaningful therapeutic upstream target. There are currently no devices intended for targeted PHb removal from the blood, rather they are nonspecific in their removal of cytokines, PHb, plasma proteins, etc. For example, high-cutoff membranes show some ability to remove PHb [12]; however, PHb has a relatively large MW (~64kD) which suggests that a membrane permeable to PHb is also permeable to other potentially beneficial plasma proteins. The toxic effects of hemoglobin are regulated within the intact erythrocyte and by the formation and removal of PHb-haptoglobin (Hp) complexes [13, 14] after hemolysis. We thus hypothesize that it is possible to harness the
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neutralizing effects of Hp and develop an extracorporeal hemoadsorption device consisting of porous agarose beads that have Hp covalently bound to the surface for the selective removal of PHb from whole blood.
2. METHODS
First, devices without immobilized Hp were used for optimization of the fabrication process and assessment of hemocompatibility. As seen in Figure 1, these consisted of ~8 mL porous agarose beads (Sepharose 4 Fast Flow, Cytiva, Marlborough, MA) within an acrylic column with two 40 mm screen-type filters (Blood transfusion filter, Haemonetics, Braintree, MA) at either end that confined the beads (diameter ~90 mm) yet allowed blood flow.
buffers. Assessment of Hp immobilization onto beads and subsequent characterization of their PHb binding capacity was performed in bead volumes of ~0.3-1 mL. First, varying Hp concentrations in the incubation solution (10-20 mg Hp/mL beads) were assessed to characterize the Hp loading capacity of the beads. Hp concentrations in the incubation and wash solutions following immobilization were measured using a colorimetric protein assay. During immobilization experiments, average Hp concentrations in incubation and wash solutions were multiplied by the associated volumes to calculate the unbound Hp mass. Bound Hp mass was calculated as the difference between the initially added mass and unbound mass. Binding efficiency was calculated by dividing the bound Hp mass by the total Hp mass.
2.3 Binding Capacity
Figure 1. Device packed with porous agarose beads. Filters at either end of the column confine beads within the device. Stopcocks allow simple attachment into the rat ECMO circuit.
2.1 Bead Permeability
The agarose beads are stored in 20% ethanol and lose functionality if removed from solution and allowed to dehydrate. Thus, methods for column packing could not include measuring a specific bead mass. Instead, Reynolds number calculations enabled the use of Darcy permeability to determine bead permeability. Measurement of Darcy permeability of each column was performed using previously described methods for porous media [15], used to characterize interdevice variability and consistency of the bead packing procedure, and compared to the theoretical value calculated from the average particle diameter (D_p) and porosity (ε) of the random packing of solid spheres in the following equation: D_p^2 ε^3/150(1-ε)^2.
Subsequently, the ability of Hp-bound beads to bind PHb was assessed in rat plasma/blood with PHb concentrations of ~150-200 mg/dL. Solutions at this concentration were created by adjusting the rat donor blood with known concentrations of PHb from plasma separated from freeze thawed rat blood. Initial, simplified experiments assessed PHb binding during incubation of beads in a solution of PHb in plasma. During characterization of PHb binding capacity, average PHb concentrations, measured using a colorimetric hemoglobin assay kit (SigmaAldrich, St. Louis, MO), were multiplied by associated plasma volumes to calculate total remaining PHb mass following bead exposure. Removed PHb was calculated as the difference between final PHb mass for Hp-bound beads and control beads containing no Hp. Results were compared to the theoretical PHb binding capacity. Because there is a 1:1 binding ratio between the PHb and Hp, the theoretical value of PHb binding was calculated by dividing the molecular weight of the tetramer of Hb by the average molecular weight of the three isoforms of human Hp. Control experiments using beads without Hp were conducted in parallel.
2.4 Studies in Whole Blood
As seen in Figure 2, a device without immobilized Hp was incorporated into an extracorporeal circuit during rat ECMO and exposed to 30 min of diverted continuous blood flow at ~4 mL/min.
2.2 Binding Efficiency
Human Hp (Abcam, Cambridge, MA) was covalently immobilized on NHS-functionalized agarose beads (Sepharose 4 Fast Flow, Cytiva, Marlborough, MA) via formation of stable amide bonds using the manufacturer recommended coupling process. Modifications to this protocol included replacing the recommended coupling buffer with phosphate buffered saline (PBS) as experiments showed no significant difference in binding efficiency between the two
Figure 2. Rat ECMO circuit. Schematic for a device within a rat model of extracorporeal membrane oxygenation (ECMO) to assess acute device hemocompatibility.
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Following blood exposure, the column was passively rinsed with saline. Darcy permeability was measured pre- and post-blood exposure to assess any blockages that may have developed due to thrombosis. Evidence of thrombosis in the device would be indicated by a decrease in permeability. Additionally, benchtop binding capacity experiments assessed PHb binding under whole blood flow (0.6 mL/min). Outflowing blood was collected in oneminute intervals and centrifuged for plasma collection. Similar methods as previously described for assessing PHb binding in an incubation solution were used to determine the binding capacity of the Hp-bound beads under whole blood flow. Additionally, calculation of percent removal of PHb during whole blood flow was performed by dividing the PHb concentration in control samples by the corresponding PHb concentration in Hp-bound bead samples.
3. RESULTS
3.1 Bead Permeability
We expected permeability to be the same for each device and comparable to the theoretical value of 6.15 mm2. Rat-sized devices exhibited a Darcy permeability of 8.90 ± 0.41 mm2 (n=13), given as the mean value ± the standard deviation, with all data falling within 11% from the average (Figure 3). The theoretical Darcy permeability of the device is 6.15 mm2.
Figure 4. Incubation concentration and resulting Hp immobilization. The bound Hp per bead volume for each Hp incubation concentration are plotted with standard deviations.
3.3 Binding Capacity
Simplified PHb binding experiments in plasma demonstrated a PHb binding capacity of 0.24 ± 0.08 mg PHb/mg immobilized Hp (n=6), corresponding well with theoretical values (~0.28 mg PHb/mg Hp). 3.4 Studies in whole blood: We measured Darcy permeability pre and post exposure to whole blood flow to test for blockages that could have developed due to thrombosis (as indicated by a decrease in permeability). Column permeability exhibited minimal change (< 4% decrease, n=1) post blood exposure during rat ECMO. Additionally, Figure 5 displays PHb concentrations in outflowing blood during flow experiments. Hp-containing beads were able to remove up to 68% (n=3) of PHb.
Figure 3. Column permeabilities. Each packed column and their respective Darcy permeabilities are graphed (n=13). The average Darcy permeability, 8.90 mm2, is plotted in green and the theoretical permeability, 6.15E mm2, is in orange.
3.2 Binding Efficiency
A critical variable is the binding of Hp to the beads, as this determines device efficiency. As seen in Figure 4, the mass of Hp immobilized on the beads increased with the Hp concentration used during incubation with maximal loading at an incubation concentration of 20 mg Hp/mL bead that corresponded with an average immobilized Hp concentration of 13.6 ± 2.0 mg Hp/mL bead and binding efficiency of 72 ± 7 % (n=4).
Figure 5. PHb concentration during whole blood flow experiments. PHb concentration of control and Hp groups plotted with error bars. Average PHb concentration of blood used in experiments plotted in green at 213.5 mg/dL.
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4. DISCUSSION
Darcy permeability results demonstrate intercolumn consistency and thus suggest the development of a repeatable column manufacturing and bead packing process. Differences in average and theoretical permeabilities of the columns were expected as theoretical values were based on solid rather than porous spheres as well as average, not real, bead diameter. This packing procedure used for the rat sized devices can be easily adapted to create future models of the device, such as those for later sheep studies. Small-scale studies also demonstrated the ability to immobilize Hp on the beads and the capacity of such beads to remove PHb from the incubation solution. Binding efficiency of the Hp to the beads and to PHb are important parameters in determining device size and efficiency. These results will be used to determine the bead volume required for a therapeutic effect. In Figure 4, the bound Hp per bead volume does not plateau between any adjacent pair of incubation concentrations. The largest incubation solution concentration (20 mg Hp/mL) thus resulted in the maximum binding of all conditions assessed and was used in later experiments. Additionally, the minimal change in permeability preand post-blood exposure during rat ECMO suggests that thrombosis did not occur and indicated a favorable hemocompatibility; however, with an n=1, repeated trials are needed to confirm such results. More importantly, the binding capacity and percent removal of PHb in the small-scale benchtop studies demonstrated the capacity of the Hp-bound beads to remove PHb from whole blood flow. In Figure 5, low concentrations of PHb were initially noted in both groups presumably due to hemodilution from the saline used to prime the columns. As expected, PHb in the control group quickly increased back to the starting PHb concentration. As expected in the Hp group, PHb concentrations eventually returned to the starting concentration due to the saturation of binding sites. For each sample collected, the PHb concentration in the control bead group was greater than that of the Hpbound beads, indicating that the Hp-bound beads have bound to and have removed PHb from the blood.
5. CONCLUSIONS
With all devices falling within 11% from average Darcy permeability, the permeability measurements from this early work suggest that we developed a consistent and repeatable manufacturing process of devices suitably sized for rat trials. Results from our first rat ECMO study suggest favorable hemocompatibility of the device, as there was a less than 4% decrease in permeability, and future work will continue to use these devices to further assess hemocompatibility. We optimized methods for Hp immobilization on the bead surface, demonstrated by the 72% binding efficiency. We also assessed PHb binding capacity and showed that Hp can be used to selectively remove PHb from whole blood flow since Hp-containing beads removed up to 68% of PHb. Future work will repeat the Hp and PHb binding experiments within the rat-sized device, utilize the rat model to assess PHb removal under in vivo and explore the device’s ability to mitigate acute kidney injury during ECMO.
6. ACKNOWLEDGEMENTS
Funding was provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh.
REFERENCES: [1] S. Christen et al., “Oxidative stress precedes peak systemic inflammatory response in pediatric patients undergoing cardiopulmonary bypass operation,” Free Radic Biol Med, vol. 38, no. 10, pp. 1323-32, May 15 2005. [2] I. C. Vermeulen Windsant et al., “Hemolysis during cardiac surgery is associated with increased intravascular nitric oxide consumption and perioperative kidney and intestinal tissue damage,” Frontiers in physiology, vol. 5, p. 340, 2014. [3] C. Betrus et al., “Enhanced hemolysis in pediatric patients requiring extracorporeal membrane oxygenation and continuous renal replacement therapy,” Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia, vol. 13, no. 6, pp. 378-83, Dec 2007. [4] Z. Ricci et al., “High levels of free haemoglobin in neonates and infants undergoing surgery on cardiopulmonary bypass,” Interact Cardiovasc Thorac Surg, vol. 19, no. 2, pp. 183-7, Aug 2014. [5] T. M. Cheng et al., “Haemoglobin-induced oxidative stress is associated with both endogenous peroxidase activity and H2O2 generation from polyunsaturated fatty acids,” Free Radic Res, vol. 45, no. 3, pp. 303-16, Mar 2011.
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[6] C. E. Cooper, R. Silaghi-Dumitrescu, M. Rukengwa, A. I. Alayash, and P. W. Buehler, “Peroxidase activity of hemoglobin towards ascorbate and urate: a synergistic protective strategy against toxicity of Hemoglobin-Based Oxygen Carriers (HBOC),” Biochim Biophys Acta, vol. 1784, no. 10, pp. 1415-20, Oct 2008. [7] A. Kapralov et al., “Peroxidase activity of hemoglobin-haptoglobin complexes: covalent aggregation and oxidative stress in plasma and macrophages,” The Journal of biological chemistry, vol. 284, no. 44, pp. 30395-407, Oct 30 2009. [8] F. Vallelian et al., “The reaction of hydrogen peroxide with hemoglobin induces extensive alphaglobin crosslinking and impairs the interaction of hemoglobin with endogenous scavenger pathways,” Free Radic Biol Med, vol. 45, no. 8, pp. 1150-8, Oct 15 2008. [9] D. J. Schaer, P. W. Buehler, A. I. Alayash, J. D. Belcher, and G. M. Vercellotti, “Hemolysis and free hemoglobin revisited: exploring hemoglobin and hemin scavengers as a novel class of therapeutic proteins,” Blood, vol. 121, no. 8, pp. 1276-84, Feb 21 2013. [10] S. Borasino et al., “Impact of Hemolysis on Acute Kidney Injury and Mortality in Children Supported with Cardiac Extracorporeal Membrane Oxygenation,” The journal of extra-corporeal technology, vol. 50, no. 4, pp. 217-224, Dec 2018. [11] H. R. Omar et al., “Plasma Free Hemoglobin Is an Independent Predictor of Mortality among Patients on Extracorporeal Membrane Oxygenation Support,” PLoS One, vol. 10, no. 4, p. e0124034, 2015. [12] M. Hulko M et al., “Cell-free plasma hemoglobin removal by dialyzers with various permeability profiles,” Sci Rep, vol. 5, no. 1, pp. 1-9, Nov 2015. [13] W. Koch et al., “Genotyping of the common haptoglobin Hp 1/2 polymorphism based on PCR,” Clin Chem, vol. 48, no. 9, pp. 1377-82, Sep 2002. [14] C. F. Tseng, C. C. Lin, H. Y. Huang, H. C. Liu, and S. J. Mao, “Antioxidant role of human haptoglobin,” Proteomics, vol. 4, no. 8, pp. 2221-8, Aug 2004. [15] H. E. Pacella, H. J. Eash, and W. J. Federspiel, “Darcy Permeability of Hollow Fiber Bundles Used in Blood Oxygenation Devices,” J Memb Sci, vol. 382, no. 1-2, pp. 238-242, Oct 15 2011.
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Development and testing of a novel biomechatronic balance aid and rehabilitation device Nathaniel Mitrika,b, Alexandra Delazioa, Goeran Fiedlera, and David Brienzaa,b a
Department of Rehabilitation Science and Technology, Department of Bioengineering
b
Nathaniel Mitrik
David Brienza
Nathaniel Mitrik was born Pittsburgh, PA. He is currently pursuing a dual undergraduate/graduate degree in Medical Product Engineering at the University of Pittsburgh. His passion for medical product design and device innovation motivates him to become an entrepreneur in the medical product field. David Brienza is a professor in the Departments of Rehabilitation Science and Technology and Bioengineering. He serves as associate dean for Technology and Innovation in the School of Health and Rehabilitation Sciences at the University of Pittsburgh. His work focuses on the development and evaluation of rehabilitation Technology with a particular focus on technology for pressure injury prevention and mobility.
Significance Statement
Conventional approaches to fall prevention need to be addressed with more advanced biomechanical engineering methods to improve negative outcomes for the many adults experiencing falls. Biomechatronic balance aids can generate forces to transmit onto the body and open the future of ambulation care to automation over reliance on user control.
Category: Device Design
Keywords: biomechatronic, rehabilitation, balance, and fall prevention
ABSTRACT
Human balance, particularly in the elderly and injured populations requires the use of walking aids. Walking aids are controlled by the user (canes, walkers, etc.) and rely on expanding the user’s base of support, which is limited by the user’s attentiveness, accuracy of stable walking aid placement, and the user’s environment. Applying modern biomechanics, the generation of a dynamic biomechatronic walking aid may be able to improve balance by working more similarly to the body’s existing balance strategies than standard walking aids. This biomechatronic approach may solve issues caused by the limitations associated with the reliance on an expanded base of support. The prior art revealed potential effect in such biomechatronic balance aids and motivated this design to generate a product with reasonable scope. A biomechatronic balance aid was built, programmed, tested, and the recorded effect was analyzed. The analysis of the biomechatronic balance aid’s effect on user balance showed that corrective balance perturbations can be generated and, using the measured effect, an estimate of the magnitude of transmitted force in the tested control scenario was generated.
1. INTRODUCTION
In the U.S., more than one in four adults over the age of 65 falls each year [1]. Maintaining balance is essential for mobility and preventing fall-related injuries while walking, so many walking aids seek to enhance user balance by expanding their base of support. This convention for addressing fall prevention includes wheeled mobility devices, canes, and crutches. Standard walking aids are limited by the user’s attentiveness to the task of using their aid to increase the base of support, the user’s accuracy of placement to maintain a stable base, and the user environment’s contribution to the aid’s difficulty for use (e.g., stairs, uneven/low-traction floors). Less conventional approaches for fall prevention and balance aid include orthotic devices and exoskeletons. Orthotics achieve their function by mechanically stabilizing the body, while exoskeletons replace or supplement some muscular function. The proposed alternative solution to the problem of body imbalance is the use of a biomechatronic balance aid, acting as a middle ground between conventional approaches and exoskeletons. Utilizing the stabilization of an orthotic device for device attachment and force transmission, the device is worn by the user. We hypothesized that a device introducing rotational forces to oppose extreme angular perturbations could either prevent falls entirely or provide the user additional recovery time to regain their balance, rather than simply expanding the user’s base of support. A biomechatronic prosthetic design has been seen in several other publications. An example of biomechatronic application in prosthetic design is
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the “Arque” tail [2]. Arque is a biomimetic prosthetic tail inspired by the natural design of seahorse tails, and has the effect of purposeful body perturbations, which initially solidified the base assertion that biomechatronic influenced design can provide increased value to prosthetic development in the context of body balance. Nabeshima et al.’s design is limited from being an applied balance aid because it is controlled through a pneumatic system that must be attached to the device at all times for it to function. Needing a compressed air cylinder to be connected and near to the device during use causes the Arque tail balance aid application to become far less accessible to a general user. The research of Nabeshima et al. inspired Roy et al. to test the effect of rotational momentum transfer on human static balance when applied by a biomechatronic tail less sophisticated than the Arque tail. Roy et al. found that a rotating mass attached via orthotic to the posterior torso of humans was able to significantly disrupt static balance [3]. The conclusion section from Roy et al. included the prospect that the device effect could be improved to provide balance perturbations in the opposite direction of potential falls with a feedback control loop and, therefore; an improved biomechatronic tail could be applied as a balance aid. Supported by the evidence of significant disruption, the expectation that a purposeful control of the rotating mass was pursued further. Mitrik et al. published on further biomechatronic device development and analysis of effect on an inverted pendulum to validate the device effect on a static human balance model, the inverted pendulum, prior to device modification for user testing [4]. Inconsistencies during device improvement indicated that simultaneously utilizing both rotational and linear momentum contributions in early prototypes was too complex to approach non-systematically. To simplify the design process and identify which momentum supplied the more significant contribution to balance, the prototype design was split into a rotational focus and a linear focus, scheduled for later development. The design thus shifted to focus on rotational momentum whereby a flywheel was used to ensure equal distribution of mass about the rotational axis, thus preventing linear contributions. This design decision requires proof of concept and was not able to be appropriately scaled to user level design without lower resolution verification. These design and research decisions innovate further the conceived application of a biomechatronic balance aid by synthesizing functional limitations and successes. The prior art is improved on by this research through application of an iteratively validated balance aid design tested to improve human balance while focusing on viability of user adoption.
2. METHODS
This research was analyzed through testing on a member of the research team who volunteered to wear the biomechatronic balance aid (Male, 21). The test subject, who is free of injury or physical impairment was fully informed of the risks of experimentation and had control of the device power switch to prevent safety risks. A plaster wrap spinal brace was custom fit to the test subject by a member of the research team with experience in orthotic spinal stabilization, providing spinal stability during the tests. A steel plate was used to attach the orthotic and the biomechatronic to transfer generated torques to the body. An Arduino Uno Microcontroller in conjunction with a Cytron 10A DC Motor Driver Shield and an ST LSM6DSOX Accelerometer and Gyroscope are the core electronic components of the device. The mechanical parts of the device include a motor and transmission assembly from a DeWalt 20V MAX Right Angle Drill, attached with a custom adapter to a fly wheel (Figure 1).
Figure 1. Biomechatronic Balance Aid Assembly: The biomechatronic assembly is pictured as worn by the test subject. The parts of the assembly are labeled in the following order: 1. Flywheel, 2. Motor Power, 3. Motor, 4. Connection Plate, 5. Circuit Assembly (Uno, LSM6DSOX, and Cytron Motor Driver), 6. Spinal Wrap Orthotic, 7. Location of Biomech Lab Toolkit Sensor (not pictured).
The testing performed utilized the device’s design to reach maximal flywheel speed. During which, code in the Uno actively uses the LSM6DSOX as a static inclinometer to read the user’s vertical angle of perturbation. The subject was then asked to stand straight while being slowly leaned forward. The LSM6DSOX continued recording the perturbation angle, and the code in the Uno was actively looping to check for a perturbation angle of five degrees, this angle was selected to be approximate the perturbation at which the center of gravity leaves the base of support. Upon reaching the balance boundary of the base of support the Uno was coded to send a correction signal to the
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Cytron motor driver. The correction signal indicates to the motor shield that a stopping signal should be sent to the motor. By stopping the motor, the flywheel’s angular momentum is translated into the orthotic and thus, onto the torso of the test subject. A proprietary application called Biomech Lab Toolkit with a corresponding sensor attached to the test subject’s upper back at the base of the neck was used to actively record the angular perturbation from vertical. The Biomech Lab Toolkit acted separately from the angle reading of the LSM6DSOX and was used only for data collection as the test subject was lowered forward to simulate a potential fall forward. The correction signal was sent at an angle of 5 degrees, causing the motor to stop by an angle of ten degrees, and the test was run 3 times before mechanical failure caused the device to lose effect.
3. RESULTS
The Biomech Lab Toolkit application recorded the test subject’s angle from the vertical reference over time. The corrective angle experienced from the deceleration of the flywheel was collected directly from the Biomech sensor Toolkit application data. The corrective angle was input into an inverted pendulum model analysis of the test subject to extract the translated torque from the flywheel to the torso. Angular output graphs from the Biometric application, which does not allow for effective data scaling, were analyzed to determine the corrective angle (Figure 2).
Figure 2. Biomech Lab Toolkit Angular Deviation Plot: The angular output from the Biomech Lab Toolkit application. The yellow highlighted ‘U’ shape represents the angular correction, and the red dashed ellipse represents the base of support.
The corrective angle read from the BioMech Lab Toolkit application data was observed to be 1 degree for each of the three instances of experimentation. The model assumes the test subject acted as a rigid body inverted pendulum and that the torque translated to the body was about the center of mass. Using results from the model analysis, the torque translated was determined to be 12.6 N*m.
4. DISCUSSION
Our hypothesized effect of a device introducing rotational forces to oppose extreme angular perturbations to prevent falls entirely or providing the user additional recovery time to regain balance was tested and showed a measurable and scalable effect. A 1-degree correction result may not appear to be promising as the correction seems to be quite small, however; it is worth considering outside factors when analyzing the potential for application of this biomechatronic device as a balance aid. The most significant factor for consideration, in the opinion of the research team, is that the device experienced mechanical failure after only 3 trials. The mechanical failure was assessed and determined to be associated with the motor’s attachment to the flywheel through a transmission assembly. It was concluded that the motor transmission was likely experiencing slippage prior to the complete failure of the transmission and as such an unknown amount of torque failed to be translated into angular torso correction. The lost torque would predictably increase correction, which if increased to five or more degrees would allow for correction back into the base of support. The prior art from Mitrik et al. predicted a maximal angular correction of seventeen degrees, using the previous design on an inverted pendulum model. While this work provides a much lower correction, the experiment is performed with a human test subject rather than the inverted pendulum which likely accounts for the difference. Additional factors to consider include that the biomechatronic tested for this analysis was significantly hindered by low resolution components throughout the assembly. An argument is made that a more robust mechanical design as well as an analysis of the effect of different physical properties (to reduce overall mass, size, gear slippage, and maximize force transmission) is warranted. Seeing the potential effect even in mechanical failure validates steps to create a higher resolution device to test for higher range of effect without slippage or failure. It is also worthwhile to compare the biomechatronic discussed in this research paper with a similarly developed device that utilizes linear momentum rather than rotational momentum.
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5. CONCLUSION
To improve upon current balance aids, the use of biomechatronic prosthesis was considered to generate a more biomechanically supported strategy. Supported by evidence in the literature and prior work performed by the research team, the application of a rotational momentum-based balance strategy implemented by a biomechatronic device was investigated. Despite mechanical failure, the biomechatronic balance aid has been shown to have, at least in small amounts (the correction angle of 1-degree from the vertical), an effect on a human’s balance. Future directions should include the application of improved mechanical design as well as control theories in conjunction with further developed biomechanical engineering concepts. These efforts would show greater balance correction and perhaps sufficient evidence of the feasibility of biomechatronic balance aid adoption.
6. ACKNOWLEDGEMENTS
Funding was provided by the Swanson School of Engineering, the Department of Bioengineering, and the Office of the Provost at the University of Pittsburgh. Thanks to Zachary Roy for his help and support. Thanks to the Department of Rehabilitation Science and Technology for their lab space and resources.
REFERENCES [1] G. Bergen et al. “Falls and Fall Injuries Among Adults Aged ≥ 65 Years.” MMWR Morb Mortal Wkly. [2] Nabeshima et al. “Arque: artificial biomimicryinspired tail for extending innate body functions.” ACM SIGGRAPH 2019 Posters. 2019. [3] Z. Roy et al. “Proof-of-Concept Testing for an Inertia-based Prosthetic Tail”, American Academy of Orthotists and Prosthetists, Annual Meeting. [4] N. Mitrik et al. “Inertia-Based Prosthetic Tails: Balance Improvement, Equation Development, and Proof-of-Concept”, Biomedical Engineering Society, Annual Meeting. 2021
60 Undergraduate Research at the Swanson School of Engineering
Effects of a feedback time delay in a megahertzfrequency, nonlinear resonator Joseph Mockler, Thomas Hinds, Nikhil Bajaj Department of Mechanical Engineering and Material Science Joe is originally from Allentown, PA and is studying mechanical engineering. His research interests lie in nonlinear dynamics and control systems.
Joseph Mockler
Nikhil Bajaj
Dr. Nikhil Bajaj is an assistant professor in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. His research interests include nonlinear dynamical and control systems, and the analysis and design of mechatronic systems.
Significance Statement
Bifurcation-based sensors provide a more sensitive alternative approach for analyte detection compared with traditional vibration-based sensors. However, these methods require greater control and modelling to achieve desirable dynamics. This work aims to study the potential challenges of high-frequency design in bifurcation-based schemes.
Category: Computational
Keywords: nonlinear dynamics, time-delay differential equations, bifurcation sensing
Ingenium 2022
ABSTRACT
Vibration-based sensors have traditionally relied on monitoring small changes in natural frequency to detect structural changes. In contrast, bifurcationbased sensing schemes rely on the detection of a qualitative change in the behavior of a system as a parameter is varied. Thus, these sensors offer a more sensitive approach to analyte detection compared with traditional methods. Bifurcation behavior may be produced reliably via nonlinear feedback, however prior research and efforts have existed predominantly in the sub-MHz range. This work demonstrates the design and implementation of a nonlinear feedback architecture, realized via a pair of diode, to exploit bifurcation behavior in frequencies near 16 MHz. In particular, the effects of variation in time delay at higher frequencies is critically investigated for softening configurations, and an improved model is studied numerically.
1. INTRODUCTION
Similar to existing traditional, natural frequency shifting sensors, bifurcation-based sensing relies on subtle shifts in structural changes to induce a dynamic output. In contrast, though, bifurcation-based sensors exploit inherent nonlinearities to create regions of both stable and unstable solutions in the frequency domain; this allows for subtle changes in frequency to generate sharp changes between stable solutions, or a bifurcation [1]. This behavior may be physically realized by implementing a nonlinear term to a traditional feedback architecture and monitoring the system behavior in the frequency domain. This work investigates the potential characteristics and challenges associated with time delay in a feedbackbased circuit. While existing work has successfully realized bifurcation behavior in low-frequency resonators, implementing similar architectures in high-frequency resonators present unique challenges and considerations [2]. For higher frequencies studied (around 16 MHz), circuit time delay becomes a critical consideration, even with minimal delays of only 1 to 2 ns. Further, at higher frequencies, subtle nonlinear, bifurcation behavior was found in previously assumed linear resonators, producing disparities between existing models when implemented in the feedback architecture. [2]. Building on existing analytical models that include realistic diode behavior (polynomial V-I curves), these updated models also incorporate timedelay at substantially higher frequencies [3] with a refined resonator model to include nonlinear behavior. In this work, the system is first analytically studied by solving a time-delay, nonlinear differential equation as a function of, principally frequency. A numerical solution via direct integration is also built to validate the proposed analytical models while matching experimental configurations. This section explores a new, nonlinear model for high-frequency resonators. Finally, the circuit architecture is developed, and an experimental system is constructed with time-delay lines to demonstrate varying time-delay. 61
2. METHODS 2.1 Analytical Model
Beginning with a sinusoidally driven, 2nd order system, the system is described as follows: where the stiffness, k(x, xτ) - a function of the state x and the time delay τ - may be described by a 9th order, fitted approximation of the diode response. From [2], the nonlinear model for k(x, xτ) as a function of the diode, 9th order polynomial is written: k(φ)=k(x,x τ)=k1 x+R fGeG2 p(G1x τ )/L Where, p(x), represents an experimentally fitted polynomial expression for non-ideal diode behavior, solved with a least-squared solution, and xτ represents the state, x, with a time delay [4]. The remaining terms are built from the assumption that the resonator acts linearly. A general solution to the sinusoidal excitation can be written, and the impacts of small time-delays are readily seen: xr =asin(φ–τω) At small frequencies, time delay generated a negligible phase shift, yet, with increasing frequency, the effects of time delay are more significant, especially in the 16 MHz region. Existing literature developed an approximate solution to the differential equation via the method averaging [3]. The stability of the system is then computed over the studied excitation frequency develop a set of solutions to the delay differential equation (DDE), including stable and unstable regions.
A numerical scheme is constructed in Simulink and MATLAB to directly integrate the governing differential equation and match the experimental approach. This model, unlike the analytical, includes a more refined approach to modelling the resonator that accounts for nonlinear hardening at high frequencies. A feedback loop is first constructed in Simulink where the timedelay signal added to the driving signal and passed through the resonator. The resonator is first modelled after a Butterworth Van-Dyke model [5] with a nonlinear duffing term, α¬c. This nonlinear duffing term seeks to better model the nonideal behavior of high frequency resonators. The second-order system may be numerically represented by solving for, and then integrating, the 2nd order term, with the states fed back into the subsystem representation. The governing equation for the state of the resonator may be written as a nonlinear, second order differential equation:
This model is solved with parameters that map to the experimental resonator behavior with no feedback. The signal is then integrated and passed through the 9th order polynomial fit, representing the diode architecture. This generates nonlinear feedback and will, ultimately, yield a bifurcation. A transport block then models the time-delay. Other parameters, including matching excitation, feedback, and lowpass equivalent gain are computed and incorporated to further tune the model [2]. Table 1 outlines the matching values.
2.2 Numerical Model
Figure 1: Simulink representation of experimental model. This builds a time delay, differential equation that is then solved numerically in MATLAB.
Table 1. Experimental System Parameters Parameter R¬C [Ω] L¬C [H] C¬C [F]
α¬C [s2-V-2]
Ge [-]
Value
-0.00112
-2.73x108
94
0.10
8.248x10-16
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(1+R14/R13) = G1 [-] 5.158
(1+R22/R21) = G2 [-] 1.997
R¬f [Ω] 43.43
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A MATLAB script is then written to control a frequencysweep of the model. Care is taken to maintain elements of the analytical description while matching the experimental construction, including continuity and phase delay. Continuity is maintained within both states of the model and in the input excitation. Previous states are saved and subsumed as initial states of each successive run. Further, continuity is maintained in excitation inputs by selectively computing a phase shift in the input signal such that successive sinusoids have continuous ends. These modifications promote continuity between the analytical and experimental models.
3. RESULTS
The analytical model and numerical model are solved with matching parameters in the experimental configuration, with the numerical model accounting for nonlinearities in the resonator. An example input peak-to-peak voltage (0.25 VPP) is shown of the numerical and experimental solutions (Figure 4 and 5, respectively), and 0.10 VPP shown for the existing analytical solution (Figure 3) to demonstrate limitations in existing work [2].
2.3 Experimental Model
Figure 2: Experimental PCBA with delay traces. Trace lines are ~0 Ω resistors that impart a small time delay on the system.
Upon constructing the physical circuit (Figure 2), a frequency sweep is run to realize the bifurcation-based nonlinearities. A Zurich Instruments HF2LI lock-in amplifier is used to generate the input signal. A time delay is integrated into the circuit using 0 Ω lines on the PCB with lengths selected to generate 0, 0.5, 1, and 2 ns delays, based on circuit board trace propagation speeds. Sinusoids with frequencies over 15.994 to 16.002 MHz with steps of 2 Hz were generated with the lock-in amplifier and swept with through the circuit. A 4th order, lowpass filter with a time constant of 1.564 ms was used for input generation. Caution was taken to ensure appropriate settling time by waiting 50 ms between each step.
Figure 3: Analytical model of feedback-driven resonator, where a black line indicates a stable solution, and a red line, unstable. Dotted points indicate an experimental solution and reveal a mismatch between existing analytical models and experimental results.
The resonator was first characterized. By applying a 0 Ω resistor to connect the integrated signal to ground, rather than enter the feedback loop, the prescribed frequency sweep is run, and resonator characteristics may be readily matched between numerical and experimental models. The 0 Ω resistor is then removed and connected to feedback, where the sweeps are conducted. Combinations of time-delays of 0, 0.5, 1, and 2 ns, and 0.05 to 0.3 Vin (in steps of 0.05 V) were swept, with the transimpedance (TIOUT) and integrated/high-pass filter (INHPOUT) values recorded.
Figure 4: Numerical solution with an improved resonator model and bidirectional sweeps at 0.25 V¬in. The improved resonator model demonstrates an improvement over existing analytical methods.
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Figure 5: Experimental results of bidirectional sweeps of a physically realized, nonlinear feedback circuit. The physical circuit clearly demonstrates a sharp jump between stable solutions.
Figure 6: Numerical Simulation to variation in Vin with time delay, steps of 0.05 Vin. Increasing input voltage produces larger, more pronounced bifurcations.
4. DISCUSSION
Preliminary results indicate both qualitative and quantitative agreement between numerical and experimental models with moderate input voltages, and the new resonator model demonstrated a noticeable improvement over existing analytical models. While the analytical and experimental solutions shared similar bifurcation characteristics, their amplitudes and widths between bifurcations differed greatly. The numerical and experimental solutions, however, were markedly similar. At 0.25 V, both numerical and experimental responses demonstrated a bifurcation width of about 200-300 Hz with no time delay and similar peak amplitudes. Further, both models revealed a decay of both bifurcation width and amplitude as time delay increased, with a nearly continuous response generated at 2 ns of delay. And while these qualities existed in the analytical model, they were drastically different from the numerical and experimental solutions; this demonstrates that a nonlinear resonator model better represents the constructed system, and the presented numerical simulation shows a marked improvement over current existing models. For moderate excitation voltages (around 0.25 V), the nonlinear resonator model presented helps account for disparities between existing analytical work and experimental realizations. There are, however, some disparities between the experimental and numerical model, seen in Figure 6 and 7.
Figure 7: Experimental response to Variation in Vin with Time Delay, Steps of 0.05 V. As numerically predicted, increasing voltage produced larger bifurcations in the experimental configuration, however, the point of bifurcation appears to shift further than predicted under the numerical solution.
Most notable is the difference in sensitivity to time delay when comparing the numerical and experimental models; 0, 1, and 2 ns time delays peak amplitudes fit well, but 0.5 ns did not. Further, at more extreme excitation amplitudes, the disparities in both amplitude and frequency response grew. A possible reason for these discrepancies lie in the circuit architecture; the circuit contains some nonzero time delay, which is considered nominally 0 and thus unaccounted for in the Simulink model. A small time delay, as demonstrated, would diminish the peak amplitude and diminish the nominally 0.5 ns simulation, as realized in the experimental model. The experimental model also revealed a significant shift in frequency response that
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did not agree with the numerical simulation, and a larger bifurcation width at smaller time delays (~0 ns). A more refined model for high frequency resonators may help explain this behavior; in particular, an investigation on how the feedback-enabled response is impacted by resonator nonlinearities may refine the model.
REFERENCES
5. CONCLUSION
[2] N. Bajaj. (2017). Microresonator-Based Sensors with Feedback Enabled Nonlinearities. Doctoral Dissertation, Purdue University.
Building on existing efforts, this work proposed a revised model for high frequency resonators that includes a nonlinear duffing term. In comparison with the existing analytical solution explored in this paper, the numerical approach with a refined resonator model produced a marked improvement in matching the experimental behavior. Further, when this system is introduced to nonlinear feedback, numerical simulations produced results that demonstrate some key experimental behavior that was not well modelled in existing work: bifurcation widths, peak amplitude, and time delay responses. Finally, as time delay is introduced to the system, the numerical simulation qualitatively modelled the diminished bifurcation behavior well, seen in figures 4 and 6. Quantitative mismatch begins to arise, however, when the changes in excitation amplitude are studied. Experimental results revealed a pronounced response to changes in excitation voltage; in the numerical approach, this behavior was elicited, but to a lesser extent. While this paper proposes one explanation, a revised model for the resonator or other experimental components may demonstrate the experimental results better. Further, an investigation on how feedback impacts the nonideal, nonlinear resonators may develop a more holistic model for high-frequency resonators.
[1] J. F. Rhoads, S. W. Shaw, and K. L. Turner (2010). Nonlinear dynamics and its applications in microand nanoresonators. Journal of Dynamics Systems, Measurement, and Control, 132(3):034001.
[3] N. Bogoluibov, et al, (1961). Asymptotic Methods in the Theory of Non-Linear Oscillations. Hindustan Publishing Corp. 4. C. J. Begley and L. N. Virgin (1995). A comparison of piecewise linear and continuous approximating models. Mechanics Research Communications, 22(6):527–532. [4] S. J. Martin, V. E. Granstaff, and G. C. Frye (1991). Characterization of a quartz crystal microbalance with simultaneous mass and liquid loading. Analytical Chemistry, 63(20):2272–2281. [5] N. Bajaj (2018). Megahertz-Frequency, Tunable Piecewise-Linear Electromechanical Resonator Realized Via Nonlinear Feedback. Journal of Sound and Vibration, 425, 257-274.
There are other avenues for potential investigations and design considerations. By tuning nonlinear feedback parameters, a designer can generate a wider, and more sensitive, bifurcation that can be implemented in a variety of analyte detection schemes. Further, an improved, nonlinear model for high frequency resonators can aid potential designers in generating and tuning selected responses.
6. ACKNOWLEDGEMENTS
Funding was provided by the Swanson School of Engineering. I would like to thank Dr. Bajaj for his instrumental role in the success of this project.
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Difference in excitatory and inhibitory neuron oxygen metabolism elucidated by intrinsic optical imaging and optogenetics in awake and anesthetized mice Andrew E. Toader1, Alberto L. Vazquez 2, 3 Departments of 1Electrical and Computer Engineering, 2 Radiology, 3Bioengineering
Andrew Toader
Alberto Vazquez
Andrew Toader is an Electrical and Computer Engineering student at the University of Pittsburgh. His research interests include biosignals processing and using neuroimaging techniques to evaluate brain health after ischemic events. He plans to pursue an MD upon graduation. Alberto Vazquez is an associate professor of Radiology and Bioengineering at the University of Pittsburgh. Alberto works on the basic understanding and development of methods to image brain function and dysfunction. He obtained his BS in Biomedical Engineering from Rensselaer Polytechnic Institute in Troy, NY and his PhD in Biomedical Engineering from the University of Michigan in Ann Arbor, MI.
Significance Statement
It is currently not known whether oxygen metabolism differs between excitatory and inhibitory neurons. This study suggests that inhibitory neurons have a higher oxygen metabolism than excitatory neurons. Understanding the differences in oxygen consumption between neuronal subtypes may help elucidate neuronal vulnerability in various diseases of the brain.
Category: Experimental Research
Keywords: CMRO2 , oxygen metabolism, excitation, inhibition, optogenetics
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Ingenium 2022
ABSTRACT
Neurons rely on a continuous supply of oxygen to sustain their function. Many studies have shown that metabolic consumption is proportional to overall brain activity, but it is not clear whether oxygen metabolism is different between excitatory and inhibitory neurons, the two major neuronal subtypes. It is thought that dysfunction in excitatory and inhibitory neurons plays a key role in the pathologies of various diseases including Epilepsy and Alzheimer’s Disease. This work investigated the difference in the cerebral rate of oxygen metabolism (CMRO2) in awake mice between excitatory and inhibitory cortical neurons using optical imaging methods sensitive to blood oxygenation coupled with optogenetic tools to selectively manipulate the activity of these neurons. Our results suggest that optogenetic activation of excitatory neurons is less metabolically expensive than optogenetically activating inhibitory neurons by 4.6 times. We compared these results to our previous study conducted under light ketamine anesthesia using similar experimental procedures where optogenetic activation of excitatory neurons was less metabolically expensive than activating inhibitory neurons. Understanding the oxygen consumption of neuronal subtypes can elucidate functional disturbances in areas across the brain and may aid in the discovery of various therapeutic modalities for diseases that preferentially target exhibiting excitatory or inhibitory neuronal dysfunction.
1. INTRODUCTION
Neurons are the principal cellular units of the brain; however they lack energy stores to sustain their function. As a result, the brain relies on the continuous supply of oxygen and glucose provided by the vasculature. Two major types of neurons exist in the mammalian brain: excitatory neurons, which promote firing of connected neurons, and inhibitory neurons, which suppress their firing. Excitatory neurons make up about 80-85% of all neurons in cortex, while inhibitory neurons make up 15-20% [1]. Many studies have shown that metabolic consumption is proportional to overall brain activity [2,3,4], but it is not known whether oxygen metabolism is different between excitatory and inhibitory neurons, especially under awake conditions. Understanding the metabolic consumption of neurons can lead to a greater insight into neuronal physiology and brain homeostasis, such as whether the vasculature is preferentially responsive to the metabolic demands of specific neuronal subtypes. Examination of specific sub-types of neurons is possible using optogenetics, a technique where lightsensitive ion channels are genetically inserted into the cell membranes of specific neuronal populations [5]. In this study we used Channelrhodopsin-2 (ChR2), a non-selective ion channel opened by activation with 450-490nm light.
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The cerebral metabolic rate of oxygen (CMRO2) is a metric used to quantify the oxygen consumption in a region of interest. Oxygen is metabolized in mitochondria and traditional methods to measure it rely on direct measurements of tissue oxygen using positron emission tomography (PET) or polarographic oxygen microelectrodes [6,7]. Oxygen is delivered to tissue by blood at the capillary level, such that the rate of oxygen consumption depends on cerebral blood flow (CBF). Hemoglobin, the carrier molecule of oxygen in the blood, is a strong absorber of visible light, and its absorption properties depend on the incident light wavelength as well as the number of oxygen molecules bound to it (i.e., oxygen saturation) [8]. Hence, optical imaging methods can be used to measure regional CMRO2 with measurements of cerebral blood flow (CBF) through cranial windows with good spatial and temporal resolution. Numerous assumptions are generally made such that relative changes in CMRO2 are most commonly measured regionally in the cortex upon activation of neurons mapping to that cortical region [8,9]. The majority of studies assessing the cerebral metabolic rate of oxygen consumption (CMRO2) using these methods have been conducted in anesthetized animals. By design, anesthetics act to suppress neuronal activity and, therefore, do not provide a clear understanding of brain metabolism. In 2005, Dunn et al reported CMRO2 upon forepaw and whisker stimulation in mice using optical imaging techniques [9]. Stimulation of sensory systems such as vision and somato-sensation, however, indiscriminately activate neurons in the brain and it is very difficult to isolate metabolic responses to a subset of neurons. Subsequent studies used optogenetic techniques to stimulate a certain subset of inhibitory and excitatory neurons separately to examine the CMRO2 response in the mouse cortex [2,3,4]. These studies, however, did not consider the difference in number of neurons activated between excitatory and inhibitory neuron activation. In this work, we examine CMRO2 in awake mice, normalize the CMRO2 by the amount of neurons activated, and compare our results with those published in anesthetized mice to determine if there is a difference in oxygen consumption between neuronal subtypes.
2. METHODS
Mice were obtained from the Jackson Laboratories for experimentation (n=2 Thy1-ChR2-YFP stock#007612, n=2 VGAT-ChR2-EYFP stock#014548). In Thy1-ChR2-YFP mice (excitatory animals), ChR2 is inserted in excitatory pyramidal neurons (mostly in Layer 5), while in VGATChR2-YFP mice (inhibitory animals), expression targets most cortical inhibitory neurons via the vesicular inhibitory amino acid transporter (VGAT) gene [5,10]. Thus, the ion channels only depolarize in a specific neuronal population when irradiated with light. ChR2 in these animals was fused with yellow fluorescence
protein (YFP) to identify the location of its expression. All experimental procedures conducted were approved by our institutions IACUC. The experimental design was similar to that previously used by our group in the ChR2 mouse model [11]. Mice underwent an initial surgery to affix a head bar and cranial window for awake optical experiments, which took place 2-4 weeks after recovery from surgery. Imaging was conducted by a camera with each frame sequentially illuminated at 572nm and 620nm using light emitting diodes at an effective frame rate of 10 Hz per color. A laser doppler flow probe was used to assess cerebral blood flow (CBF) in sensory cortex and an 473nm optogenetic fiber (CrystaLaser, Inc., Reno, NV) was used to optogenetically activate ChR2 neurons. Optogenetic stimulation occurred at 5 Hz for 1 second using 1mW 10ms light pulses. Stimulation periods were repeated every 30s a total of 10 times. Hemoglobin is a molecule within blood which carries oxygen to tissue and exists in two states: HbO (oxygenated) and HbR (deoxygenated). The addition of the concentrations of the two states gives rise to HbT (total hemoglobin). The change in concentrations of each of these states can be used to compute the oxygen consumption within an area. The values of the change in concentrations of the two states of hemoglobin were solved for using the optical imaging data and the modified Beer-Lambert’s relationship in Eq. 1 [9]:
(1) ∆A is the absorbance, as captured by the camera at a certain wavelength for a particular image frame. eHbO and eHbR are the extinction coefficients of oxyhemoglobin and deoxyhemoglobin, and D is the differential pathlength factor, all at a certain wavelength. The values for eHbO, eHbR , as well as D have been reported by Ma [8] over a range of wavelengths, and those values were used for calculations in this work. The change in CMRO2 was then computed using Eq. 2 [9]:
(2)
where rCBF is change in blood flow measured by the laser doppler flow probe. ΔCHbR and ΔCHbT are the changes in the concentrations of deoxyhemoglobin and oxyhemoglobin, respectively, calculated from Eq (1). The variables CHbR,0 and CHbT0 are the 67
initial concentrations of deoxyhemoglobin and oxyhemoglobin, and were assumed to be 40µM and 100µM, respectively, as published in Dunn et al [9]. Finally, the variables γr and γt denote vascular weighting coefficients which account for the combination of HbR and HbT in the veins and arteries from the observed optical measurement. We assigned a value of 1 to both of these weighting coefficients as done in Dunn, et al [9]. To account for the difference in the number of activated neurons upon optogenetic stimulation in the two subpopulations, the emission of the YFP bound to ChR2 was quantified, serving as an approximation to the volume of neurons activated. This was accomplished by computing the percent change in camera signal during optogenetic stimulation relative to that before stimulation (dividing each pixel in the mean image during stimulation by the corresponding pixel in the mean image before stimulation). This allows for quantification of percent change in fluorescence over the brain as a result of the YFP emission. An example of this image is shown in figure 1b. Since the same optogenetic stimulation parameters were used in all experiments, the illuminated area was approximately the same and the maximum YFP value (in %) in the stimulation region was used as an indicator of total YFP expression.
3. RESULTS
rCBF, ∆CHbR, and ∆CHbT were calculated pixel-by-pixel over the cranial window for each animal. These were then used to calculate rCMRO2. A raw image, an image of the YFP emission, and an image of rCMRO2 over 1-sec following optogenetic stimulation are show in Fig. 1.
Figure 1. A reference image of the brain (A), YFP emission upon optogenetic stimulation (B), and average rCMRO2 following optogenetic stimulation in a VGAT-ChR2-YFP animal (C). The scale equivalent in all images and orientation (anterior and lateral) is shown in panel A.
Optogenetic stimulation evoked a confined increase in rCMRO2, shown in red in panel b of figure 1. The time series over the activated region were averaged across animals and the average rCBF and rCMRO2 responses for excitatory and inhibitory animal models were collated. These results are shown in Fig. 2.
Figure 2. Average rCBF (A) and rCMRO2 (B) evoked by optogenetic stimulation in Thy1 (blue) and VGAT (red) animals. Shading around timeseries indicates the standard error of the mean, and the grey boxes denote the light stimulation period.
The average rCBF in the Thy1 animals was 48.69% and in VGAT was 34.45%. The mean rCMRO2 calculated in the Thy1 animals was 38.94%, and in the VGAT animals was 23.64%. The values for each animal are presented in Table 1. Since the number of neurons expressing Chr2 is different between models, we quantified the YFP emission produced by optogenetic stimulation as an indirect measure of the number of neurons expressing the Chr2 protein. We use the maximum YFP emission to compare against known differences in neuronal populations in mouse cortex. The maximum increase over the brain region for the Thy1 mice was 122.78%, and for the VGAT mice was 17.70%. Table 1. Summary of rCBF, rCMRO2 and YFP expression Thy1 (Excitatory) VGAT (Inhibitory) Mouse 1, 2 Mean
Mouse 1, 2 Mean
rCBF
59.4, 38.0% 48.7%
28.2, 43.1% 34.5%
rCMRO2
44.8, 33.1% 39.0%
17.8, 31.7% 23.6%
rCBF/ rCMRO2 YFP
1.33, 1.15
1.24
1.59, 1.36
168.0, 77.6% 0.27, 0.43
122.8%
22.3, 13.1% 17.7%
0.35
0.80, 2.44
rCMRO2 YFP
1.47
1.62
4. DISCUSSION It is well known that excitatory neurons outnumber the inhibitory neurons in mouse cortex by about 4:1. Therefore, if excitatory and inhibitory neurons consume the same amount of oxygen upon stimulation, activation of the same volume of cortex by optogenetic stimulation would ideally show rCMRO2 changes matching this proportion in Thy1:VGAT models. Since we do not know the exact number of cells expressing ChR2 in the models used, we relied on the YFP emission produced by optogenetic stimulation as an indirect measure. Our results show that rCMRO2 in Thy1 mice is 1.65x larger than that of VGAT mice. However, when we scale rCMRO2 by the YFP emission, the rCMRO2 from VGAT mice exceed that of Thy1 mice by 4.6x.
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This suggests that inhibitory neurons consume more oxygen than excitatory neurons. An important issue to consider in our experimental design is that excitatory neuron activation likely activates inhibitory neurons. To avoid this issue, pharmacological agents can be used to prevent neuro-transmission and restrict activity to the activated neuronal population. Nonetheless, in awake conditions, we would expect optogenetic activation of excitatory neurons to over-estimate excitatory rCMRO2. Although additional experimentation is necessary to isolate neuronal activity to ChR2-positive neurons alone, experiments under anesthesia have shed some light on this potential issue. In a study previously performed by our laboratory in anesthetized mice, we found rCMRO2 changes of +1.5% in Thy1 mice and -5% in VGAT mice, showing the effect of inhibition on overall brain metabolism. After pharmacological blockade of neuro-transmission (only done in VGAT mice), optogenetic stimulation evoked an increase in rCMRO2 of +3.9%. In general, these findings were similar to those obtained in two other studies using similar but not identical conditions and mouse models [2,3,4]. Both awake and anesthetized results support the notion that inhibitory neurons consume more oxygen than excitatory neurons, despite their numbers and differences in anatomy and arborization extent. It is important to note that the rCMRO2 response computed depend on γr and γt values, as well as the initial concentration of HbR and HbT, as shown in equation (2). We assumed γr and γt values to be 1 in this study, which we know to not always be true areas with fewer capillaries, where signal contribution from arteries and veins is not equal. Additionally, initial concentrations of HbR and HbT were assumed to be 40µM and 100µM, respectively. These values are commonly assumed in the literature [2,9], however can impact the computed rCMRO2. Future studies will be aimed at consolidating these assumptions.
5. ACKNOWLEDGEMENTS
Funding was provided by the National Institutes of Health (NIH) NIH-R01-NS094404 as well as the Summer Undergraduate Research Internship (SURI) grant from the Swanson School of Engineering at the University of Pittsburgh.
REFERENCES [1] Keller D, Erö C, Markram H, “Cell Densities in the Mouse Brain: A Systematic Review,” Front Neuroanat, vol. 12, Oct 2018. [2] Vazquez AL, Fukuda M, Kim SG, “Inhibitory Neuron Activity Contributions to Hemodynamic Responses and Metabolic Load Examined Using an Inhibitory Optogenetic Mouse Model,” Cereb Cortex, vol 28, pp. 4105-4119, Nov 2018.
[3] Dahlqvist MK, Thomsen KJ, Postnov DD, Lauritzen MJ, “Modification of oxygen consumption and blood flow in mouse somatosensory cortex by cell-typespecific neuronal activity,” J Cereb Blood Flow Metab, vol. 40, pp 2010-2025, Oct 2020. [4] Lee J, Stile CL, Bice AR, Rosenthal ZP, Yan P, Snyder AZ, Lee JM, Bauer AQ, “Opposed hemodynamic responses following increased excitation and parvalbumin-based inhibition,” J Cereb Blood Flow Metab, vol. 41, pp 841-856, Apr 2021. [5] Wang H, Peca J, Matsuzaki M, Matsuzaki K, Noguchi J, Qiu L, Wang D, Zhang F, Boyden E, Deisseroth K, Kasai H, Hall WC, Feng G, Augustine GJ, “Highspeed mapping of synaptic connectivity using photostimulation in Channelrhodopsin-2 transgenic mice,” Proc Natl Acad Sci U S A, vol. 104, May 2007. [6] Mintun MA, Raichle ME, Martin WR, Herscovitch P, “Brain oxygen utilization measured with O-15 radiotracers and positron emission tomography,” J Nucl Med, vol. 25, Feb 1984. [7] Piilgaard H, Lauritzen M, “Persistent increase in oxygen consumption and impaired neurovascular coupling after spreading depression in rat neocortex,” J Cereb Blood Flow Metab, vol. 29, Sep 2009. [8] Ma Y, Shaik MA, Kim SH, Kozberg MG, Thibodeaux DN, Zhao HT, Yu H, Hillman EM, “Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches,” Philos Trans R Soc Lond B Biol Sci, vol. 371, Oct 2016. [9] Dunn AK, Devor A, Dale AM, Boas DA, “Spatial extent of oxygen metabolism and hemodynamic changes during functional activation of the rat somatosensory cortex,” Neuroimage, vol. 27, Aug 2005. [10] Zhao S, Ting JT, Atallah HE, Qiu L, Tan J, Gloss B, Augustine GJ, Deisseroth K, Luo M, Graybiel AM, Feng G, “Cell type–specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function,” Nat Methods, vol. 8, Sep 2011. [11] Vazquez AL, Fukuda M, Crowley JC, Kim SG, “Neural and hemodynamic responses elicited by forelimband photo-stimulation in channelrhodopsin-2 mice,” Cereb Cortex, vol. 24, Nov 2014.
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Development of a Graphical User Interface to Facilitate Automated BioModel Selection for Synthetic Biology Gene Circuit Design Kristyn Usilton a, Chueh Loo Poh b Department of Bioengineering, Swanson School of Engineering; bDepartment of Biomedical Engineering, National University of Singapore a
Kristyn Usilton
Chueh Loo Poh
Kristyn Usilton is a junior bioengineering student who is pursuing the medical product engineering track and an Industrial Engineering minor. Her professional interests involve human factors engineering within medical product design and she hopes to pursue a career designing and improving medical devices for individuals with disabilities in the future. Dr. Chueh Loo POH is an associate professor with the Department of Biomedical Engineering at National University of Singapore (NUS). He is also a principal investigator of NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), leading the development of NUS biofoundry. His research interests focus on synthetic biology.
Significance Statement
Often beneficial programs are hindered by a lack of user interface. Developing an interface for BioModel Selection System 2 extends the benefits of automated model selection to researchers with little programming experience, allowing more time to reach conclusions. Generally, many developed programs would also be more helpful with an interface.
Category: Device Design
Keywords: modeling, bmss, gene circuits, user interface
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ABSTRACT
Continuous advances in the field of synthetic biology gene circuit design enable increasing control and manipulation of cellular gene expression for use in sensing and manufacturing fields. Thus, there is a growing demand for accurate modeling of genetic circuits to guide experimental design and reduce cost and time spent selecting a model that best mimics the genetic behavior in the experiment. The novel unified BioModel Selection System 2 (BMSS2) automates the model selection process by evaluating both the deconstructed parts of gene circuits and the entire system’s dynamics. The software is advantageous but challenging to use without knowledge of the individual functions and structure of the software and programming languages. Thus, a graphical user interface would remove the need for researchers to interact with source code and would subsequently allow researchers to utilize the software quickly and easily. The objective of development was to present a user-friendly interface for researchers to characterize their own biological data. This reduced the amount of time spent learning how to use the source code of the software and left more time to complete model selection. Ultimately, a user interface able to correctly select the ‘best’ model for a set of characteristic data will increase the number of individuals that are able to take advantage of the software’s functionalities.
1. INTRODUCTION
The goal of synthetic biology is to model existing biological parts and systems, such as genes and gene circuits, to design and construct new biological systems easier and more accurately. Genes provide hereditary information via stretches of DNA responsible for protein or functional RNA molecules [1]. By regulating gene circuits in synthetic biology, an organism’s protein expression and resulting bodily functions can be more closely and precisely controlled [2]. The central dogma explains this flow of information and in turn how to regulate it. Within transcriptional control, the promoter and related factors control rates of transcription. In translational control, the strength of the ribosome binding site is the main control mechanism for how much protein is made [1]. These factors can be regulated and tested to analyze their effect on gene expression using ordinary differential equation mathematical modeling. Modeling aims to simplify a system while retaining a level of accuracy necessary to produce a model which captures the key behaviors of a system such as the effect that modifying the ribosome binding site strength has on gene expression. Using ordinary differential equations specifically allows variables to change over time but not over space and there is no variation randomness within the model [1]. Thus, these models are best for analyzing both system dynamics and how individual parts change gene expression over time. The process
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of balancing accuracy with simplicity when modeling a gene circuit is hard to achieve without the assistance of an automated software. The method prior to BMSS2 development was tedious trial and error. So, BMSS2 was created as a modular and customizable program that automates the model selection process for uploaded experimental data [3]. The ability to automatically select the best model decreases time spent trying to balance complexity with accuracy in selecting a best model. However, using the software required extensive knowledge of Python syntax and time to customize the source code for individual applications. The challenge, therefore, was to create a simple graphical user interface that inherited a user’s data and model information, along with accessing database information, to correctly select a model in an automated manner. Given the missing link between BMSS2 and researchers with no programming experience, the objective of this research was to make model selection using BMSS2 easier and more accessible. Certain success criteria to be met in developing the software were that the interface needed to be developed using Python and had to have an intuitive flow, making it easy to use. Computationally it needed to accept all required inputs and allow customizations where the BMSS2 software does. It ultimately needed to select and display the best model and provide characteristic data based on the selected model.
2. METHODS
regarding design and functional needs, research progressed to designing the visual aspects of the interface. Design considerations were how many windows needed to be in the program, where each button should be located, and necessary built-in checks and help options for users. These were included in the initial design and altered throughout testing. Test users were initially confused by the one main window, stating lack of direction. To rectify this problem a simple main window with clear top-down progression and a single column of buttons, was created.
Figure 2. The main window (left) displays to the user first. The images on the right show one possible first step, the “Browse Files” window (top, right). Success and error messages are shown on the lower right and display based on the data upload success.
Initial test users also had difficulty confirming their successful use of the program as they progressed. The addition of success, error, and information message windows as well as an in-depth help window helped clarify for them. The comprehensive ‘Help’ window provides the user with information about settings parameters, core model upload compulsory information, and examples for how to correctly populate the initial value and parameter value fields of the settings.
Figure 1. A visual display of user interface development methods.
User interface development methodology commenced with learning how to use the BMSS2 software. Potential design features and functional needs to be incorporated into the user interface were noted throughout the learning process. Identified required inputs were biological data points, data types supported by BMSS2, and a choice of optimization method. Outputs needed to include the best model, the ranked AIC value table, and the selected model’s parameter values. Customizations included the option to modify initial and/or parameter values and an optional customization of settings for the models themselves. When sufficient data was acquired
Figure 3. This figure shows the comprehensive “Help” window which is accessed from the settings window (Fig. 4) or the upload model window.
Ensuring the interface met computational criteria meant integrating functionalities and testing each segment of the system. The BMSS2 software includes data and sample code to demonstrate successful use.
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These helped in testing the functional success of the interface at each step and revealed a few errors in the design. For example, the user was unable to select an optimization technique for model fitting. Additionally, some models, such as logic gate circuits, need modified parameter and initial values. The original design was missing a field to customize these values and was therefore unable to analyze logic gates. An updated text box enables users to update code directly in the interface via a textbox. This code is then extracted and utilized in the model selection process. These additions were vital to the completion of model selection.
analysis is pulled from the associated BMSS2 MBase database. To upload models instead, the user clicks the “Upload Models” button on the main window (Figure 2). Finally, BMSS2 supports four optimization methods: simulated annealing, SciPy basin hopping, SciPy dual annealing, and SciPy differential evolution. The decision of which to use is up to the user. Simulated annealing, the most common optimization method, was chosen in the case study.
3. RESULTS
The defined success criteria were an intuitive and functionally accurate interface that accepted required inputs, allowed customizations where necessary, and ultimately choose the best model. Results of this research can be summarized in a case study using data the GusA gene. The data measured both growth and activity of lactic acid bacteria in response to regulation of the GusA gene. Activity data was reported in arbitrary units while growth was measured with absorbance at 450nm and normalized to optical density. Testing required that the interface could accept the optical density data and analyze which growth model best fit the data. An additional step was to take the activity data and select the best constitutive promoter model.
Figure 4. Final user interface steps of use/progression.
When beginning model selection via the BMSS2 graphical user interface, the user first interacts with the main window (Figure 2). All steps in the main window must be completed before continuing or error messages will display. In this case study, the optical density data for growth based upon control of factors within the GusA gene was uploaded and the optical density data type was selected from the drop-down menu. Next the models being analyzed for GusA, in this case various growth models, were selected from the database (Figure 5). These models require little input from the user as most data for their model selection
Figure 5. One next possible step, the “Choose Models” window (left), allows users to select model(s) of their choice from the models database. The success/error messages display based on the success of model choice and main window optimization method choice.
The user then specifies settings details based upon the first step. The pregenerated settings file must be completed by populating or updating the file and specifying modifications for initial values and parameter values. A message box (Figure 6) explains what the user needs to do in this step. The user must first fill in code in the main ‘Upload Settings File’ window to modify initial values and parameters inside a built-in text window (Figure 6). There is also an option to select not to modify the values at all. Then the user can open the pregenerated file and either populate it for their own models or check and update any desired values taken from the database. An example of a correctly populated settings files, as well as a blank one, are provided to the user via the comprehensive ‘Help’ window (Figure 3).
Figure 6. The “Settings Message” box (top left) displays after initial information is provided. The “Upload Settings Files” window (center) displays next. The user can modify parameters/ initial values and can edit pregenerated settings files (right).
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After these pieces of information are provided without error, model selection proceeds. Behind the scenes, the optimization and curve fitting processes begin, along with AIC calculations and graphing. The results window displays information that the user provided prior to model selection as well as the ranked AIC table, chosen model parameters and general summary statement. The last feature of the results window is the export option to create a zipped file containing the results of model selection.
Figure 7. The results display is the final window. The results window (left) provides information about the entire selection process. An “Export Results” button creates a zipped file containing the results and graphs (success message for this step, bottom right).
4. DISCUSSION
Three growth models were tested within the case study of the developed user interface using the simulated annealing optimization method. The model selected was the Verhulst Deviation logistic model. While the fit was generally good, it lacked the same sigmoidal shape that the Verhulst models typically show, instead graphing in an exponential fashion (Figure 7). This indicates a possible error in in graphing or analysis. Further testing and altering of parameters are needed to confirm this model choice and the robustness of the user interface itself. The applications of the BMSS2 system are vast and are furthered by the development of the user interface. Completing model selection without the user interface took around a week for someone with programming experience but with little experience in synthetic biology and gene circuits. However, with the user interface the process of model selection could be completed in less than a day. The GUI still cannot accept some inputs and run smoothly in all cases. Beyond the three supported data types incorporating support of AU data would also be highly beneficial. There is still much to be done in the development of this graphical user interface for the selection system software. The primary future work would be testing more sets of data and model types to assess the systems effectiveness in those cases.
5. CONCLUSIONS
The model selection and characterization processes were automated and made available to more users by BMSS2 and its accompanying graphical user interface. The objectives for development of the user interface a simple design that allowed researchers with little programming experience to use BMSS2 and spend less time on the model selection process. With a developed GUI, test users with minimal programming knowledge and basic knowledge of gene circuit modeling and design could use the user interface and BMSS2 proficiently within one day. The model selection functionality worked moderately well, but more testing should be done to check the selection and graphing of results. The second task will be achieved after the data is converted from arbitrary units to a supported datatype or the software can accommodate the arbitrary unit data type. Ultimately, the development of the user interface expands the possibility of using BMSS2 for researchers in the field of synthetic biology gene circuit design and is a useful tool within a growing industry.
6. ACKNOWLEDGEMENTS
This work was funded by the Swanson School of Engineering, the Office of the Provost, and the Department of Bioengineering at the University of Pittsburgh. Thank you to Professor Chueh Loo Poh, Jing Wui, and Russell for their support this summer at the National University of Singapore.
REFERENCES [1] Baldwin, G., Bayer, T., Dickinson, R., Ellis, T., Freemont, P. S., Kitney, R. I., … Stan, G.-B. (2015). Synthetic Biology - A Primer. https://doi.org/ doi:10.1142/p1060. [2] O’Connor, C. M. & Adams, J. U. Essentials of Cell Biology. Cambridge, MA: NPG Education, 2010. [3] Ngo, R., Yeoh, J.W., Fan, G., Loh, W., Poh, C.L (2021) BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis. bioRxiv 2021.02.23.432592; doi: https://doi. org/10.1101/2021.02.23.432592.
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Development of an infra-red imaging device to detect visceral arteries Oldrich Viraga, Mohammad H. Eslami b, David A. Vorpa,b,c,d,e,f, Timothy K. Chunga Department of Bioengineering, University of Pittsburgh, Pittsburgh PA, bDivision of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh PA, c Department of Surgery, dDepartment of Cardiothoracic Surgery, and eDepartment of Chemical and Petroleum Engineering, f McGowan Institute for Regenerative Medicine, gCenter for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA a
Oldrich Virag
Oldrich Virag was born in the Czech Republic and immigrated to the United States with his family at a very young age. His interest in health care and engineering aligns with his goal of pursuing a bachelors in Bioengineering before pursuing higher education. Timothy K. Chung, PhD is a Department of Bioengineering research assistant professor at the Swanson School of Engineering. Research interests include biomechanics, medical devices, and artificial intelligence.
Timothy Chung
Significance Statement
Abdominal Aortic Aneurysm (AAA) accounts for 15,000 deaths annually, with stent-grafts being the primary pre-rupture therapy. Patients with complex aneurysm geometries in need of immediate repair use costly customized fenestrated stent-grafts that extend the time-to-intervention. Our proposed arterial detection device is the first step of an in-situ fenestration approach that significantly reduces costs, time-torepair, and improves ease-of-use for clinicians.
Category: Medical Device Design
Keywords: Abdominal Aortic Aneurysm, printed circuit board, infra-red, fenestrated stent-graft
ABSTRACT
Fenestrated Endovascular Aortic Repair (FEVAR) is a minimally invasive surgical procedure utilizing customized stent-grafts that require accommodations for branching visceral arteries. Manufacturing time for these customized devices can take up to several months as each stent-graft must be tailored to each patient’s aneurysm geometry. This project focuses on the development of a visceral artery detection device, the first part of an in-situ fenestrated stentgraft device used for the repair of Abdominal Aortic Aneurysms (AAA). This was accomplished by manufacturing a custom printed circuit board (PCB) that housed an array of infra-red (IR) light detection sensors. The voltages read at each corresponding sensors were measured, where the highest voltage sensor values directly related to the proximity of the emitter. The voltages were processed in real-time by meshing the scattered data onto a surface plot while recording data from a custom MATLAB script to visualize the detection of IR light from an Arduino microcontroller data stream. A computer-aided design (CAD) model was 3D-printed to serve as a simplified aorta with varying visceral artery diameters. The use of the device was analyzed by placing an IR light within a 3D-printed phantom model of visceral arteries and sweeping the larger cylindrical chamber with our PCB and IR sensor array. Conducting these tests in both an air and deionized water media demonstrated that the PCB and IR sensor matrix could detect and display the location of an orifice when an IR signal was present. The visceral artery detection device could locate visceral arteries through an IR point source which would enable clinicians to modify stent grafts in-situ.
1. INTRODUCTION
Abdominal aortic aneurysm (AAA) is a cardiovascular disease resulting in the irreversible dilatation of the mid aorta. AAA must be preventatively treated with stent-grafts. Stent-grafts or endografts are the primary method of repair for AAA that significantly lessens the pressure experienced by the aneurysm wall, preventing the aneurysm from further growth and possible rupture. AAA is the 13th most common cause of death in the United States [1] and carries a 90% mortality rate making them one of the most fatal surgical emergencies [2]. Each year more than 15,000 people die of AAA [3]. Current clinical guidelines state that when an aneurysm reaches a maximum diameter criterion of 5.5 cm or greater, surgical intervention is offered as long as the patient is eligible. Traditional open surgery used to be the preferred method of repair, which required that both the chest and abdominal cavities be opened [4]. Open surgery was the first treatment option offered for AAA but is strenuous on a patient and carries a higher mortality rate than endovascular aneurysm repair (EVAR).
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Table 1. Fenestrated Stent-Graft Logistic Open Endovascular Surgery Repair (EVAR) Graft and $36,000 $32,000 Hospital Cost Time to Immediate Immediate Intervention Mortality Rate 11.9% 1.96% Recovery Time 5-10 days ICU 2-4 days
Fenestrated EVAR (FEVAR) >$75,000 6-8 Weeks 1.30% 2-4 days
Table 1: Average time and cost for varying repair treatments of AAA including open surgery, EVAR, and FEVAR. Displays high wait times and high costs associated with FEVAR.
Figure 1: AAA Repair Method Distributions. Visual Diagram of various AAA repair procedure frequencies and mortality rates.
EVAR is a minimally invasive approach that places a stentgraft to attenuate the pressure of the aneurysm wall, whereby reducing the risk of further growth and rupture. EVAR has since become the preferred repair method for AAA, where the procedure involves the deployment of the stent-graft through the femoral artery. Complex geometry aneurysms occur when the aneurysm dilation extends to the junction of the lowest renal artery or superiorly up to the celiac artery. A traditional stent-graft would cover the visceral arteries preventing blood flow through critical organs, where fenestrated EVAR (FEVAR) or open surgery are the only options to treat these patients’ complex geometry aneurysms. FEVAR is similar to EVAR but requires a custom prefabricated stent that accommodates the visceral arteries and is more technically challenging for a clinician. FEVAR requires custom tailored openings based on patient geometry in the main stent-graft body to prevent obstruction of blood through the visceral arteries [5]. Guidewires are placed in the openings to place and deploy branching stents to restore blood flow through visceral arteries into their respective organs [5]. Currently, fenestrated stent grafts are patient specific relying on the precise location of visceral arteries to custom-manufacture prior to clinical intervention. The creation of a visceral artery detection device would allow for in-situ stent-graft fabrication. However, these custom stent-grafts are more costly and require additional time to customize and prefabricate a patient-specific stent-graft for varying locations of the visceral arteries.
Due to the risk of rupture for large aneurysms, there may not be sufficient time to custom manufacture the fenestrated stent-graft. Katsargyris et al. reported that nearly 2% of patients experienced aneurysm rupture before a fenestrated stent-graft operation could take place [6]. Rupture of AAA causes 4%-5% of sudden deaths, and post rupture surgical procedures result in a roughly 50% fatality rate, highlighting the risk to patients when left untreated for extended periods. However, the fatality rate for elective repair procedures is much lower, around 1%-5%, highlighting the importance of pre-rupture treatment [7]. The need for in-situ fenestration is evident based on fatalities experienced during the custom manufacturing period. The proposed in-situ fenestration method would eliminate wait times that could potentially reduce mortality rates by offering expedient repair of largesized aneurysms. Our approach to perform in-situ fabrication of stent-grafts is not possible without the ability to accurately detect visceral arteries within the lumen of the aneurysm. This project aims to test the feasibility of the novel approach by detecting visceral arteries through the utilization of IR signals and sensors, a milestone in the realization of a medical device capable of detecting orifices. An emitter was placed inside a 3D printed visceral artery test-bench, and an IR sensor matrix was used to detect the location of the emitter inside the artery. The detection device would later be paired with a mechanical or laser puncturing device capable of creating fenestrations. In a clinical setting, a Zenith stent graft must first be prefabricated and then deployed via a catheter. Similarly, with our device a stent-graft would be first implanted, however, the fenestrations would be created in-situ. In a prior art and literature search, in-situ laser fenestration was used to repair a ruptured thoracic abdominal aortic aneurysm, but did not use our novel approach of a visceral artery detection method [8]. Through the utilization of IR sensors and emitters our device will be able to accurately identify the location of a visceral artery. The successful creation of such device would be a first step towards improving patient healthcare by reducing the time-to-intervention, presurgical fatalities, and overall costs. 75
2. METHODS
The expected and anticipated results for the experimental apparatus included the successful detection of IR wave through a stent-graft to locate an orifice within the 3D printed model. This device consists of IR signals and sensors, a printed circuit board (PCB), and the use of an Arduino (Arduino LLC, Boston, MA) for real-time visualization. The IR emitters and receivers are the primary approach for orifice detection by placing a point source into the lumen of a visceral artery. The receivers detect the IR waves from the emitter by sweeping the custom PCB probe through the 3D model. This component works in unison with an Arduino microcontroller to measure voltage changes caused by the sensors reading the IR waves and is converted to a real-time image to map out the detected orifice of the visceral artery.
A 3D model of an aorta with visceral arteries was designed using a long main cylindrical chamber with several open-ended tubes 3D-printed using Formlabs Form 3 printer (Formlabs Inc., Somerville, MA) with clear elastic resin. Figure 3: CAD rendering of model that was 3D printed to simulate an aorta with visceral arteries.
2.1. IR Sensor Matrix Creation
IR sensors are light-dependent photoresistors in which the electrical resistance changes with respect to the light received. IR sensors were arranged on a three-bythree grid, with a sensor on each corner square of the grid and a final sensor being placed in the center for a total of five sensors. The outer sensors were placed on the PCB with a three-millimeter gap between each sensor, with the last sensor being fit tightly in the middle of the outer sensors. Figure 2: A) Visual CAD of the PCB circuitry. B) PCB with IR sensor matrix used in experiment.
The model was placed within a glass jar containing a lid with a single hole at the top. A singular IR LED was then placed within a protruding visceral artery orifice. Aluminum foil was wrapped around the IR emitter to direct the transmitting waves and reduce the scattering of the IR signal. A Zenith fenestrated stent-graft was deployed into the main cylindrical chamber of the aneurysm through the hole at the top of the lid. Figure 4: Experimental set up used for sensing the orifice
The IR sensors were soldered onto a PCB in the corresponding arrangement and placed in series with 100 Ω resistors (Figure 2). Color-coded wires were connected to each IR sensor and inputted into separate analog channels on the Arduino microcontroller.
2.2. Development of a 3D Aorta Model and Experimental Setup
where the custom PCB was inserted to conduct sweeps. Includes 3D printed model of simulated aneurysm and Zenith stent-graft.
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The Zenith graft material is fabricated using woven polyester fabric, and the stent is made from stainless steel. The IR LED emitter was turned on using the 3.3 volt source from the Arduino board and remained constant throughout the experiment. This LED emits with a peak wavelength of 940 nm. The PCB and the five sensors were swept vertically (z-direction) through the hole of the jar’s lid to locate the point of emission of IR light. The PCB was then rotated until all five sensors surrounded the point-source where the IR light was emitting a signal.
2.3. Imaging from IR Signals
An Arduino was interfaced with MATLAB (MathWorks Inc. Natick, MA) utilizing the VoltageRead function to read voltages of each of the five sensors. The five-volt terminus of the Arduino was utilized as the energy source for the analog IR receivers. The analog sensor range that was recorded was between 0 and 1.2 volts that were rescaled to 8-bit for relative signal intensity. The sensor voltages would increase if the IR waves were detected. Each sensor of the array was assigned a position, in which each position corresponded to a physical location. Based on the voltages at each position, a 3D surface map was generated to visualize the peak signal from a visceral artery.
These images were conducted in real-time, with the signal shown on the screen being the signal seen by the IR sensors. This test was first performed with air as the medium within the jar, with the emitter being placed in two different size visceral arteries. After confirmation that the device worked within air, deionized water was used as the medium, and the test was repeated.
3. RESULTS
The sweeps conducted through the stent-graft detected the presence of IR light and were successfully visualized as a 3D surface map. The surface map revealed the interior of the stent and test-bench quickly and accurately (Figure 5). The average voltage read for the middle sensor that detected the IR emitter was 0.32 volts. In contrast, the sensors that did not detect the presence of an IR wave were recorded as 0.034 volts. These results were consistent with the expected results. The emitter was also placed in and tested on two different diameter visceral artery sizes. The sensor matrix was capable of accurately finding the emitter’s location for both diameters. The boundary of the orifice was not detected, but rather the presence of the IR signal. It was also observed that the IR signal was capable of transmitting through the woven polyester fabric and bypassing the stainless-steel stents of the Zenith fenestrated stent-grafts. Testing confirmed that the IR signal was transmitted by physical sweeps of the sensor array conducted with stent and graft materials dividing the light and PCB. The IR signals presented themselves in concentrated white (high intensity) circles corresponding to the location of the sensor, which received the strongest signal from the IR LED.
Figure 6: Real Time Imaging Results taken from a test exemplifying visualization of IR signal using an array of sensors. Figure 5: A) Stitched together results of performed sweep. B) Color plot with area between red lines denoting detection of IR. Z-axis representing spatial location in the z direction and x-axis representing intensity.
This concentrated white light faded as the signal dissipated and failed to reach sensors further away from the point source. Upon completion of the experiment, the expected results were supported, and the functionality of the device was evident as it could detect IR light through the stent-graft in both air and water.
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4. DISCUSSION
This study demonstrated that IR light and sensors are a viable method of penetrating stent-grafts for visceral artery detection. An IR sensor matrix paired with an IR emitter is capable of real-time signal detection in air and water that can be converted into an internal realtime map of the orifice. The IR spectrum was chosen after previous unsuccessful testing of intravascular ultrasound that failed to penetrate the stent-graft material. The IR wavelengths were specifically chosen for their ability to penetrate tissue and blood in order to minimize attenuation of the signal. This approach shows potential for visceral artery detection that can later be applied to developing a device to perform insitu fabrication of fenestrated stent-grafts. Current approaches to perform in-situ fenestration of stent-grafts are limited, however, some show promise. In one study, emergent total endovascular repair was conducted using an antegrade in-situ laser fenestration technique [8]. In this case, a laser probe combined with a steerable sheath had been utilized to rapidly seal and repair a ruptured Thoracoabdominal Aortic Aneurysm (TAAA). Although the paper mentions this technique is uniquely suited for ruptured TAAA, a similar process could be utilized for AAA repair. This procedure not only rapidly sealed the ruptured aneurysm but also successfully preserved visceral and renal branch perfusion. Most importantly, this study highlighted the effect and use of an off-the-shelf device to repair aneurysms and create fenestrated stent-grafts. To our knowledge, our device is the first device capable of visceral artery detection through a stent. This study has several limitations: the lack of testing in blood, with tests being conducted solely in air and deionized water. Artery size was not detectable as an IR LED was used to indicate the opening of the visceral artery. This emitter had a constant output; therefore the strength and size of the signal remained constant throughout testing. There were also spatial resolution limitations to the experimental set-up that required additional interpolation for intermediate points in the array that were not measured by sensors. Interpolation does not accurately measure intermediate points. Future testing will consist of more sensors to capture the boundary of the orifice and minimize interpolation smoothing effects.
5. CONCLUSION
The proposed visceral artery detection device could effectively locate an IR point source within a visceral artery to aid in creating an in-situ fenestrated stent graft. There is a need for the proposed in-situ approach as it would allow for rapid identification of orifices when fabricating a stent-graft while giving surgeons an internal view of the aneurysm. The visceral artery detection device is the first step in creating a much more complex and functional medical device. Next developments for the medical device include testing and implementing fiberoptic wires that receive an IR signal from the point source and transmit to a sensor array outside a catheter. These wires would enable the use of more sensors as each wire could transmit to a sensor decreasing the amount of data interpolation and increase imaging accuracy. Testing the ability to implant an IR emitter into a visceral artery via a guidewire. Finally, developing a mechanical device capable of both piercing and inserting guidewires to deploy branching steps would enable the in-situ creation of a fenestrated stent-graft. Combining current achievements and future improvements makes the creation of a device to perform in-situ fenestration. Patients would be eligible to receive immediate AAA repair and significantly lessen the financial expenses associated with customized stent-grafts.
6. ACKNOWLEDGMENTS
I would like to acknowledge the Pitt Outside the Classroom Curriculum (OCC) for funding this research with the Vascular Bioengineering Laboratory. We would also like to thank Michael G. Wells award that allowed for the further development of this medical device.
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REFERENCES [1] S. Aggarwal, A. Qamar, V. Sharma, and A. Sharma, “Abdominal aortic aneurysm: A comprehensive review,” (in eng), Exp Clin Cardiol, vol. 16, no. 1, pp. 11-5, 2011. [2] N. Armstrong et al., “The use of fenestrated and branched endovascular aneurysm repair for juxtarenal and thoracoabdominal aneurysms: a systematic review and cost-effectiveness analysis,” (in eng), Health Technol Assess, vol. 18, no. 70, pp. 1-66, Dec 2014, doi: 10.3310/hta18700. [3] “Abdominal Aneurysm.” John Hopkins University. www.hopkinsmedicine.org (Accessed 11/1/2021, 2021). [4] A. N. Assar and C. K. Zarins, “Ruptured abdominal aortic aneurysm: a surgical emergency with many clinical presentations,” (in eng), Postgrad Med J, vol. 85, no. 1003, pp. 268-73, May 2009, doi: 10.1136/ pgmj.2008.074666. [5] Z. Sun, “Helical CT angiography of fenestrated stent-grafting of abdominal aortic aneurysms,” (in eng), Biomed Imaging Interv J, vol. 5, no. 2, p. e3, Apr 2009, doi: 10.2349/biij.5.2. e3. [6] A. Katsargyris, V. Uthayakumar, P. Marques de Marino, B. Botos, and E. L. Verhoeven, “Aneurysm Rupture and Mortality During the Waiting Time for a Customized Fenestrated/Branched Stent-graft in Complex Endovascular Aortic Repair,” (in eng), Eur J Vasc Endovasc Surg, vol. 60, no. 1, pp. 44-48, Jul 2020, doi: 10.1016/j.ejvs.2020.03.003. [7] J. Manunga et al., “Technical approach and outcomes of failed infrarenal endovascular aneurysm repairs rescued with fenestrated and branched endografts,” (in eng), CVIR Endovasc, vol. 2, no. 1, p. 34, Oct 27, 2019, doi: 10.1186/s42155-019-0075-z. [8] L. L. Zhang, F. A. Weaver, V. L. Rowe, K. R. Ziegler, G. A. Magee, and S. M. Han, “Antegrade in situ fenestrated endovascular repair of a ruptured thoracoabdominal aortic aneurysm,” (in eng), J Vasc Surg Cases Innov Tech, vol. 6, no. 3, pp. 416-421, Sep 2020, doi: 10.1016/j.jvscit.2020.05.008. [9] D. A. Vorp, “Biomechanics of abdominal aortic aneurysm,” (in eng), J Biomech, vol. 40, no. 9, pp. 1887-902, 2007, doi: 10.1016/j.jbiomech.2006.09.003.
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Magnetic guidance of fibrin gel encapsulated adipose-derived Jason L. Zhenga, Ande X. Marinia, Timothy K. Chunga, Justin S. Weinbauma,c,g, and David A. Vorpa,b,d,e,f,g,h University of Pittsburgh Departments of a Bioengineering, bSurgery, cPathology, dCardiothoracic Surgery, eMechanical Engineering & Materials Science, and fChemical & Petroleum Engineering, gMcGowan Institute for Regenerative Medicine, hCenter for Vascular Remodeling & Regeneration, Pittsburgh, PA, USA Jason was born and raised in Pittsburgh, PA. He is highly enthusiastic about new innovations in the field of medicine and plans on attending medical school to further pursue his interests through direct patient care and academic research. Jason Zheng
Ande Marini, a local of the Pittsburgh area, is passionate in her work in tissue engineering, regenerative medicine, and cardiovascular disease. She hopes to continue this research as a university professor. Ande Marini
Justin Weinbaum
David Vorp
Justin Weinbaum is a research assistant professor of Bioengineering and the associate director of the Vascular Bioengineering Laboratory. His research interests focus on the dynamic nature of the vascular extracellular matrix in regeneration and disease. David A. Vorp, PhD, is the associate dean for research at the Swanson School of Engineering. He is also a professor of Bioengineering, with secondary appointments in the Departments of Cardiothoracic Surgery, Chemical and Petroleum Engineering, and Mechanical Engineering and Materials Science at the University of Pittsburgh.
Significance Statement
Abdominal aortic aneurysm (AAA) ruptures are high mortality events. Stem cell-based therapeutics are currently being studied for treatment of AAAs. However, an effective method for localized stem cell delivery to the AAA does not exist. This proof-ofconcept study examines the feasibility of a novel stem cell delivery technique in vitro.
Category: Experimental Research 80
Keywords: abdominal aortic aneurysms, stem cells, fibrin gels, magnetic guidance
ABSTRACT
Rupture of an abdominal aortic aneurysm (AAA) is a devastating event with high mortality. Surgical repair is the only treatment for rupture prevention but carries many risks. Early treatment of AAAs can help delay or eliminate the need for these high-risk procedures. The therapeutic efficacy of adipose-derived mesenchymal stem cells (ADMSCs) as an early intervention strategy has recently been studied in animal models; however, an effective method for stem cell delivery does not currently exist. Therefore, the primary objective of this proof-of-concept study was to test a novel stem cell delivery technique. This approach makes use of diametric magnets for guiding iron nanoparticle-loaded ADMSCs inside a fibrin gel construct. Unloaded and iron nanoparticle-loaded ADMSC fibrin gel constructs were seeded onto cork-bored well plates and allowed to solidify with or without exposure to an external magnetic field. Images of fibrin gel constructs were analyzed using custom MATLAB code, which revealed ADMSC guidance only occurred when both iron nanoparticles and diametric magnets were used. These findings necessitate future in vivo studies to fully understand the feasibility and therapeutic efficacy of this technique.
1. INTRODUCTION
Abdominal aortic aneurysms (AAAs) are characterized by the gradual enlargement of the aorta due to structural deterioration of the aortic wall and degradation of the native surrounding extracellular matrix (ECM). If untreated, early-stage AAAs can expand and progress towards increased likelihood of AAA rupture. Rupture of the AAA is a high mortality event that contributes over 4,000 deaths annually in the United States [1]. The complex etiology of AAA results in a lack of reliable diagnostic markers, creating a challenge for AAA management and rupture prediction. Surgical repair is usually recommended for patients with larger sized AAAs (>5.5 cm in diameter); however, these operations are associated with high mortality where roughly 40% of patients die within 10 postoperative years [1], [2]. Additionally, surgical repair is often contraindicated or pre-emptively performed, causing great risk to patients. Therefore, early intervention strategies are critical for slowing AAA growth and delaying high-risk surgical intervention. Adipose-derived mesenchymal stem cells (ADMSCs) have recently been investigated as an early treatment for AAA. ADMSCs are multipotent progenitor cells responsible for the maintenance of wounded tissue through the secretion of various cytokines [3]–[5]. These cytokines may help remodel the damaged aortic ECM by suppressing protease activity, decreasing inflammation, and promoting tissue revascularization and synthesis of structural ECM proteins such as collagen and elastin [3]–[5]. ADMSCs can also differentiate and replenish the supply of functional smooth muscle cells in the aorta [5], [6]. A recent study
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assessed the therapeutic efficacy of ADMSCs in treating elastase-induced AAA mice models [5]. The results from this study demonstrated that ADMSCs halted both AAA growth and ECM degradation during a 9-day treatment period [5]. Thus, there is great potential in using ADMSCs as an early treatment strategy. One major drawback of stem cell-based therapies is the lack of an effective method for stem cell delivery. Injection of stem cells directly into the bloodstream may appear to be a convenient method for delivering stem cells to the aortic lumen [5], [7]. However, presence of an intraluminal thrombus (ILT), a mass of cells and ECM components commonly found in AAAs, limits the penetration depth of stem cells in the lumen [5], [7]. Periadventitial delivery of stem cells have also been explored as an alternative to systemic delivery. Although there are no physical barriers to stem cell entry, direct injection into the weakened aortic wall may result in irreversible damage and fatal outcomes [5]. These challenges necessitate the development of new stem cell delivery methods. Therefore, the Vascular Bioengineering Laboratory is developing a novel stem cell delivery technique using ADMSCs. In this approach, ADMSCs are loaded with iron nanoparticles and suspended in a fibrin gel mixture, allowing for magnetic guidance and adherence to the aorta, respectively. Our group was highly motivated by a review paper from Silva et al., which outlined recent attempts at magnetic guidance of iron nanoparticleloaded mesenchymal stem cells in vivo [8]–[10]. Specifically, iron nanoparticle-loaded mesenchymal stem cells have been successfully guided to the site of spinal cord injuries in rat models through both external and internal magnetization [8]–[10]. Based on the results from these studies and our past work, we believe that our newly proposed method of periadventitial delivery does not pose any foreseeable risks for damaging the weakened AAA and avoids encounters with the ILT. In this current work, an in vitro proof-of-concept study was conducted to assess the feasibility of this technique.
2. METHODS
2.1 Cell Culture & Fibrin Gel Fabrication
ADMSCs (RoosterBio, Inc, Frederick, MD) were cultured using supplemented basal media (RoosterBio, Inc, Frederick, MD) under standard cell culture conditions (37 °C and 5% CO2). For loading, passage 3-6 ADMSCs were cultured in basal media containing 100 nm iron nanoparticles (ChemiCell, Berlin, Germany) for 24 hrs. Fibrin gel solutions were fabricated by mixing solutions of fibrinogen (3.7 mg/mL), thrombin (0.21 U/ mL), and ADMSCs (3 mil cells/mL) in a 4:1:1 ratio [11]. The fibrin gel solution was added to cork-bored well plates (200 µL per well), which contain heat-stamped circular molds at the bottom of each well that allow for creation of hemisphere-shaped fibrin gel constructs (0.6 mil ADMSCs/gel). Fibrin gel constructs were allowed
to solidify under the fume hood and cell incubator for approximately 10 and 45 minutes, respectively. Some constructs were also exposed to diametric magnets (surface field ~0.71 T) (K&J Magnetics Inc., Jamison, PA) during this 55-minute gelation period to allow for magnetic guidance of ADMSCs within the fibrin gels. For qualitative assessment of cell viability and localization, 100 µL of CellTiter 96® (MTS) (Promega, Madison, WI) and 500 µL of basal media were added to each fibrin gel construct. This process required an additional incubation period of 2 hours, after which a smart phone was used to capture images of the MTS-stained gels. In total, fibrin gel constructs were fabricated with 2 cell types (unloaded ADMSCs or iron nanoparticleloaded ADMSCs) and 2 gelation environments (with or without exposure to diametric magnets). As shown in Figure 1, fibrin gel constructs were seeded into only 2 rows of a cork-bored 24-well plate for a given trial. This set-up allowed for investigation into the distancedependent strength of magnetic attraction. Specifically, the distance between the diametric magnets and the 2 rows were ~7.25 mm and ~26.55 mm, respectively.
Figure 1. Schematic of the “2-row” experimental set-up. (A) Unloaded ADMSCs with magnet exposure (-NP/+M). (B) Iron nanoparticle-loaded ADMSCs with magnet exposure (+NP/+M). (C) Unloaded ADMSCs without magnet exposure (-NP/-M). (D) Iron nanoparticle-loaded ADMSCs without magnet exposure (+NP/-M).
2.2 Image Processing
Images of fibrin gel constructs were analyzed using a custom MATLAB code (version 2017a, Natick, MA) to study ADMSC localization. To do this, ADMSC density gradients were examined by quantifying changes in image intensity where ADMSC density is inversely correlated with image intensity. All fibrin gel images were normalized to a single fibrin gel image prior to MATLAB image processing to account for inconsistencies due to lighting. Normalized images were then converted to greyscale and subsequently used to create binary masks (Figure 2a-b). Image multiplication between the greyscale image and its respective binary mask allowed for gel construct isolation and background elimination (Figure 2c). The isolated image was analyzed line-by-line to measure average pixel
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intensity of each horizontal segment of the image (Figure 2d). A two-way analysis of variance (ANOVA) and Tukey’s multiple comparisons test were used to assess 3 segments of interest with the “Top Line” segment representing the region closest in proximity to the magnet, thus experiencing the greatest magnetic attraction. The intensity data was also used to generate heat maps to provide a qualitative assessment of intensity distribution within each fibrin gel construct.
lack of statistical significance between “Top Line” measurements in row 2 constructs indicated uniform intensity distributions with no ADMSC localization (Figure 4B).
Figure 4. Statistical analysis. Intensity profiles for 3 segments of interest created for both (A) row 1 and (B) row 2 fibrin gel constructs. * and ** correspond to p < 0.001 and p < 0.05, respectively.
4. DISCUSSION Figure 2. MATLAB image processing. (A) Greyscale image. (B) Binary mask image. (C) Isolated fibrin gel construct. (D) Segmental intensity profile analysis.
3. RESULTS
A decreasing intensity gradient (i.e., increasing ADMSC density gradient) toward the direction of magnet pull was only observed within row 1 +NP/+M constructs (Figure 3D). All other constructs demonstrated either homogeneous (Figure 3C, G-H) or heterogeneous (Figure 3A-B, E-F) intensity distribution with no established gradients.
Figure 3. Heat maps of intensity distribution. (A)-(D) Row 1 fibrin gel constructs. (E)-(H) Row 2 fibrin gel constructs. (A)(E) -NP/-M constructs. (B)(F) -NP/+M constructs. (C)(G) +NP/-M constructs. (D)(H) +NP/+M constructs. Note: lower intensity values (red regions) correspond to regions of higher ADMSC density.
Statistical analysis confirmed ADMSC localization within row 1 +NP/+M constructs as indicated by the decreasing intensity gradient (from “Center Line” segment to the “Top Line” segment) and statistical significance between “Top Line” measurements (Figure 4A). The
The absence of ADMSC localization within row 2 +NP/+M constructs can be explained by the decreased strength of magnetic attraction due to increased distance between the fibrin gel constructs and the diametric magnet. The force of magnetic attraction is inversely proportional to the distance squared, indicating that the force of attraction decreases by >4x, >13x, and >20x when moving from row 1 “Top Line” to row 1 “Center Line,” row 2 “Top Line,” and row 2 “Center Line,” respectively. In all other fibrin gel constructs, the absence of ADMSC localization was likely due to the exclusion of iron nanoparticles and/or magnet exposure. The sole observation of an intensity gradient in row 1 +NP/+M constructs indicates that both iron nanoparticles and magnetic exposure are required for ADMSC localization. Moreover, distance between iron nanoparticle-loaded ADMSCs and the diametric magnet must allow for magnetic attraction of sufficient strength. In this study, “sufficient magnetic attraction” is only achieved with iron nanoparticle-loaded ADMSCs in row 1. Another key finding in row 1 +NP/+M constructs was that the 55-minute gelation period (10min initial gelation and 45-min incubation) provided adequate time for ADMSC localization prior to complete gelation of the fibrin gel. This finding is significant as uncoupling of ADMSC movement and fibrin gel gelation may result in partial localization. As previously mentioned, current stem delivery techniques are associated with potential complications such as low cell yield and tissue damage [5], [7]. This in vitro proof-of-concept study demonstrated that periadventitial stem cell delivery to the AAA may be possible through magnetic guidance of iron nanoparticle-loaded ADMSCs using an external diametric magnet. We were also able to demonstrate through MTS-staining that the presence of iron nanoparticles does not significantly affect ADMSC viability (results are not shown). However, there are
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limitations and concerns with this approach. Despite the success of this approach in rat models, it has not yet been thoroughly investigated for delivery to the AAA. One foreseeable challenge is magnet placement because external magnetization may prove ineffective due to the distance between the AAA and magnet while internal magnetization is high-risk and difficult to implement. Moreover, it remains unclear how physiologically relevant conditions will affect both the gelation rate and degradation rate of fibrin gel constructs. Another limitation is that the current gelation time of the fibrin gels is ~55-min; thus, methods for increasing the gelation rate, while maintaining adequate time for ADMSC localization, must be explored for this approach to be clinically relevant.
5. CONCLUSIONS
In this work, the feasibility of a novel stem cell delivery technique was examined in vitro. Results suggested that ADMSCs can be magnetically guided inside a fibrin gel construct through the combined use of iron nanoparticles and diametric magnets as evidenced by the resulting ADMSC density gradients. These findings were further supported using a custom MATLAB image processing tool, which allowed for quantification of ADMSC density by means of image intensity analysis. Nonetheless, future studies are needed to fully understand the feasibility and therapeutic efficacy of this novel technique in vivo.
6. ACKNOWLEDGEMENTS
Funding for this project was provided by the Swanson School of Engineering Summer Undergraduate Research Internship, the Vascular Bioengineering Laboratory, and Kennametal.
REFERENCES [1] S. Aggarwal, A. Qamar, V. Sharma, and A. Sharma, “Abdominal aortic aneurysm: A comprehensive review,” Experimental and Clinical Cardiology, vol. 16, no. 1. Pulsus Group, pp. 11–15, Mar. 2011, Accessed: Apr. 02, 2021. [Online]. Available: /pmc/ articles/PMC3076160/. [2] T. TK, van H. JA, de B. GJ, M. FL, and L. LP, “Longterm survival and quality of life after open abdominal aortic aneurysm repair,” World J. Surg., vol. 37, no. 12, pp. 2957–2964, Dec. 2013, doi: 10.1007/S00268-013-2206-3.
[3] T. Kinnaird et al., “Marrow-Derived Stromal Cells Express Genes Encoding a Broad Spectrum of Arteriogenic Cytokines and Promote In Vitro and In Vivo Arteriogenesis Through Paracrine Mechanisms,” Circ. Res., vol. 94, no. 5, 2004, doi: 10.1161/01.RES.0000118601.37875.AC. [4] J. Rehman et al., “Secretion of Angiogenic and Antiapoptotic Factors by Human Adipose Stromal Cells,” Circulation, vol. 109, no. 10, 2004, doi: 10.1161/01.CIR.0000121425.42966.F1. [5] K. J. Blose, T. L. Ennis, B. Arif, J. S. Weinbaum, J. A. Curci, and D. A. Vorp, “Periadventitial adiposederived stem cell treatment halts elastase-induced abdominal aortic aneurysm progression,” Regen. Med., vol. 9, no. 6, 2014, doi: 10.2217/rme.14.61. [6] A. N et al., “Smooth muscle cells from abdominal aortic aneurysms are unique and can independently and synergistically degrade insoluble elastin,” J. Vasc. Surg., vol. 60, no. 4, pp. 1033-1042.e5, Oct. 2014, doi: 10.1016/J.JVS.2013.07.097. [7] H. SS, J. O, B. M, and Z. GB, “Size and location of thrombus in intact and ruptured abdominal aortic aneurysms,” J. Vasc. Surg., vol. 41, no. 4, pp. 584– 588, 2005, doi: 10.1016/J.JVS.2005.01.004. [8] L. H. A. Silva, F. F. Cruz, M. M. Morales, D. J. Weiss, and P. R. M. Rocco, “Magnetic targeting as a strategy to enhance therapeutic effects of mesenchymal stromal cells,” Stem Cell Res. Ther., vol. 8, no. 1, pp. 1–8, Mar. 2017, doi: 10.1186/S13287-017-0523-4/ FIGURES/2. [9] V. Vaněček et al., “Highly efficient magnetic targeting of mesenchymal stem cells in spinal cord injury,” Int. J. Nanomedicine, vol. 7, p. 3719, 2012, doi: 10.2147/ IJN.S32824. [10] D. Tukmachev et al., “An effective strategy of magnetic stem cell delivery for spinal cord injury therapy,” Nanoscale, vol. 7, no. 9, pp. 3954–3958, Feb. 2015, doi: 10.1039/C4NR05791K. [11] A. K. Ramaswamy et al., “Adipose-derived stromal cell secreted factors induce the elastogenesis cascade within 3D aortic smooth muscle cell constructs,” Matrix Biol. Plus, vol. 4, 2019, doi: 10.1016/j.mbplus.2019.100014.
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Index Category: Computational Research
Category: Experimental Research
Jacob C. McDonald, Ioannis K. Zervantonakisa Statistical modeling of drug-induced proteomic adaptation to overcome fibroblast-mediated HER2 therapy resistance....................... 11
Sarah-Jane Haig PhD, Leanne M. Gilbertson PhD Assessing the impact of showerheads on effluent drinking water chemistry............................................................................................. 7
Spencer C. Conaway, Christopher Wilmer, Brian Day Designing metal organic framework-based e-noses to detect lung cancer via volatile organic compounds emitted in breath............ 26
Mark F. Ciora, Jijun Yin, Zhi-Hong Mao Classification of shallow and deep sleep using electroencephalogram signals in real time....................................................................................... 23
Joshua Dewald, Wai Lam Loh VOF modeling of annular gas-liquid flow regimes in horizontal pipes........................................................................................... 32
Katelyn E. Lipa, Hang Lin Generation of obese adipose tissues from induced pluripotent stem cells (iPSCs).......................................................................................... 43
Joseph Mockler, Thomas Hinds, Nikhil Bajaj Effects of a feedback time delay in a megahertz-frequency, nonlinear resonator.................................................................................... 61
Andrew E. Toader, Alberto L. Vazquez Difference in excitatory and inhibitory neuron oxygen metabolism elucidated by intrinsic optical imaging and optogenetics in awake and anesthetized mice................................................................................ 66
Category: Device Design Adnan Alagic, In Hee Lee FPGA programming and PCB design for quantum dot singlephoton emitter.................................................................................. 15 Taylor Brightman, Neharika Chodapaneedi, Zachary Roy, Goeran Fiedler Concept methodology and strength evaluation testing of a woven socket and socket fitting system................................................... 19 Ryan J. MacElroy and Sachin S. Velankar Simultaneous local and bulk polymer crystallization analysis using microfluidic dilatometry................................................................... 48
Jason L. Zheng, Ande X. Marini, Timothy K. Chunga, Justin S. Weinbaum, David A. Vorp Magnetic guidance of fibrin gel encapsulated adipose-derived mesenchymal stem cells using iron nanoparticles................................. 80
Category: Methods Jared Lawrence, Jourdain Lamperski New perspectives on clustered linear regression................................... 38
Anna L. Maywar, Ryan A. Orizondoa, Nahmah Kim-Campbell Preliminary development of a hemoadsorption device for removal of cell-free plasma hemoglobin.................................................. 52 Nathaniel Mitrik, Alexandra Delazio, Goeran Fiedler, David Brienza Development and testing of a novel biomechatronic balance aid and rehabilitation device..................................................................... 57 Kristyn Usilton, Chueh Loo Poh Development of a Graphical User Interface to Facilitate Automated BioModel Selection for Synthetic Biology Gene Circuit Design............................................................................................... 70 Oldrich Virag, Mohammad H. Eslami, David A. Vorp, Timothy K. Chung Development of an infra-red imaging device to detect visceralarteries............................................................................................. 74
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