COVID-19 Updates

The COVID-19 pandemic highlighted the need for rapid and accurate diagnostic tests at scales never seen before. To improve the availability of current gold standard diagnostic testing methods, Illinois Bioengineering faculty developed point-of-care devices that maintained that sensitivity while reducing the cost and providing portability. In late February 2022, Illinois researchers, including Bioengineering Professor Rashid Bashir and Research Assistant Professor Enrique Valera, reported that their portable, point-of-care testing device was capable of differentiating between the SARS-CoV-2 alpha variant and earlier strains of the virus in saliva samples. See image on page 3. The microfluidic point-of-care device uses RT-LAMP amplification technique, which is an attractive alternative to PCR-based assays, especially for its application in devices intended to be used at the pointof-care. Being an isothermal technology, RT-LAMP does not require thermocycling, which results in cost-effective, portable hardware while maintaining assay sensitivity and specificity. The researchers said the assay does not need RNA extraction and purification steps, similar to the University of Illinois saliva test. Their study shows that it is possible to test for variants of the SARS-CoV-2 virus in a single point-of-care test that takes 30 minutes using a portable handheld device. The new test is scalable to suit future pandemics, COVID-19 or otherwise, and could be used at home or other settings. The researchers confirmed the device’s effectiveness by testing 38 clinical saliva samples, including 20 samples positive for alpha variant. They may refine their method to test up to five different viruses, viral strains and variants in a single test, compatible with nasal swab and saliva mediums. The team, which also included clinical investigators from Carle Foundation Hospital, reported their findings in the journal Lab on a Chip. The title of the paper is “Microfluidic point-of-care device for detection of early strains and B.1.1.7 variant of SARS-CoV-2 virus.”

Several months earlier, Bashir and Valera combined the amplification capabilities of RT-LAMP with high-sensitivity endpoint detection of crumpled graphene field-effect transistors (cgFETs) to create a portable detection cell. This electrical detection method takes advantage of graphene’s ability to absorb single-strand DNA, but not double-strand DNA. Working with researchers from the University of Illinois College of Medicine at Peoria and OSF Healthcare in Peoria, the team demonstrated electrical devices that detected miniscule amounts of the SARS-CoV-2 virus in 20 clinical samples, achieving 100 percent accuracy in only 35 minutes of amplification. Although their device was applied and validated for the SARS-CoV-2 virus, the biosensor could easily be adapted to the detection of other pathogens when the corresponding RT-LAMP primers are available. Their paper, “Detection of SARS-CoV-2 virus amplification using a crumpled graphene field-effect transistor biosensor,” was published in a December 2021 issue of ACS Sensors.

New stochastic social activity model among first to predict COVID-19 to become endemic

Bioengineering Professor and Bliss Faculty Scholar Sergei Maslov was part of a team of researchers that developed a model in 2021 predicting that COVID-19 would become endemic, sticking around much like the flu and the common cold. They published their results in the December 2021 issue of the journal ELife. The COVID-19 pandemic had gone on much longer than many predicted in its earliest months. Unlike previous pandemics whose epidemiological models graphed large waves of infection that eventually disappeared, COVID-19 saw large waves of infection that sometimes tapered to extended plateaus.

The century-old epidemiological models represented the spread of disease in very simple terms, not accounting for any variability in a population, whether physiological or social. Over the last two decades, epidemiology has incorporated these kinds of variables, while still assuming that each variable remained constant in time.Maslov and his collaborators from the University of Illinois and Brookhaven National Lab developed a stochastic social activity (SSA) model that encompasses the randomness and dynamic variability of individual social interactions, as well as individual differences in the size of social networks.

The team reports that this newly accounted-for random dynamic factor will always produce waves or plateaus of infections—like those seen throughout the pandemic—whether or not the model also accounts for individuals’ changing their social behavior based on knowledge of current infection rates. Their SSA model built on the team’s earlier findings published in April 2021 in the Proceedings of the National Academy of Sciences. They validated the model against empirical data taken from four U.S. regions prior to the introduction of COVID-19 vaccines.