UWMRESEARCH

GRANTS HELP ADDRESS SYSTEMIC RACISM

When faculty members Anika Wilson and Sara Benesh organized The Self-Care Club, their aim was to foster health and restoration for UWM students of color. The workshops in the Spring and Fall 2021 semesters feature facilitators of color who lead healing and connecting activities, such as community arts programs and an in-person gardening event.

It’s one of many efforts at UWM to address the needs of students who are coming off a year of emotional and financial upheaval inflicted in part by the COVID-19 pandemic. Students of color, in particular, were harder hit by the illness and had the added stress of dealing with societal conflict over racism.

“Our students of color are carrying a lot of weight, but they also care about their communities. So they can burn themselves out,” says Wilson, associate professor of African and African Diaspora Studies. “That’s not sustainable. But we can’t just say, ‘Take better care of yourselves.’”

The workshops stem from one of 10 projects funded through UWM’s Toward an Anti-Racist Campus (TARC) Action Grant Program, which was launched in July 2020. The projects are sponsored through the Division of Global Inclusion and Engagement and the Office of Research, and they’re developed by UWM faculty and staff.

“Creating an equitable and just campus requires ideas and action,” says Chia Youyee Vang, interim chief diversity, equity

and inclusion officer. “This grant program supports efforts that foster new solutions toward achieving racial equity.” In addition to the self-care workshops, Anika Wilson the funded TARC projects include a primer course on racial equity in Milwaukee and a group story project allowing students to express what makes them feel included. There is also a collaborative that promotes the retention of faculty of color and enhances student retention in the research pipeline. For a full list of TARC projects, visit uwm.edu/global-inclusion/inclusion/ antiracist-grant-program. – Laura L. Otto

Chia Youyee Vang

CREATING AN APP TO TREAT STUBBORN WOUNDS

Wounds that are resistant to healing pose a challenge for health care providers, who must compare photos taken at each patient visit to track the healing trajectory. Two-dimensional pictures offer limited information about the wound, and care is interrupted when patients skip or cannot make regular appointments.

“When I ask colleagues who work with these patients, ‘What are the tools that you use to characterize the wound?’ they say they collect wound data using a ruler and a Q-tip,” says Sandeep Gopalakrishnan, an assistant professor in the UWM College of Nursing.

Gopalakrishnan thought clinicians could improve treatment by tapping into a resource almost everyone has – a smartphone. He and Zeyun Yu, a professor in the College of Engineering & Applied Science, teamed up to develop a digital platform that uses photos of wounds that patients take themselves at home. Images taken with an app they created are then processed using artificial intelligence (AI), providing clinicians with accurate information on the healing characteristics.

Photos contain a rich source of data. If you have enough of them, machine-learning algorithms running in the cloud could help health care providers precisely monitor a wound’s status. Gopalakrishnan and Yu participated in the UWM-administered I-Corps Program, which teaches academic researchers how to turn discoveries in the lab into products and startups. During that training, they met Milwaukee physician Jeffrey Niezgoda, a recognized wound care expert at AZH Wound and Vascular Centers. Niezgoda suggested ways to expand their initial business idea, and the three formed a startup company called MegaPerceptron to take the system to market. Yu, a professor of computer science and biomedical engineering, Zeyun Yu (left) and Sandeep Gopalakrishnan used a sizable set of different wound images from Niezgoda’s practice to train the AI program, which supports prediction and analysis functions. “With this amount of data to train the AI algorithm,” he says, “we are able to classify the wound types with more than 90% accuracy.” – Laura L. Otto