The 2024 Institute Seed Funding Awardees
The review of the five proposals for seed funding submitted to the institute has completed in October 2024, and the awardee teams have promptly received the funds. Please note the abstracts below reflect each team’s plan for an R21/R01/SCH submission. The seed fund awards will support the generation of preliminary results towards such submissions for federal funding. We are also happy to announce that we were able to secure funding for three awards instead of two.
We are thrilled to announce the three winning teams, which span five colleges:
Rational Evaluation of Nanomaterial Antimicrobials through Integrated Statistical Studies and Advanced Network-based Computational Engineering (R.E.N.A.I.S.S.A.N.C.E.)
Lisa M. Stabryla, PhD - College of Engineering
Jida Huang, PhD - College of Engineering
Kyunghee Han, PhD - College of Liberal Arts and Sciences
William E. Ackerman IV, MD - College of Medicine
Wound infections are a common complication of hospital care and can be especially dangerous when associated with bacteria that are resistant to multiple commonly used antibiotics (often called superbugs). Antimicrobial nano-materials (materials having dimensions less than one thousandth the size of a single human hair) are among next-generation antimicrobials that hold promise in preventing dangerous wound infections due to their unique small size, but even their long-term use can lead to resistance. Since nanomaterials can be custom-made in a virtually unlimited number of varieties based on their size, shape, and chemical composition, this study will use computational modeling and laboratory experiments to synthesize new designs that can be used in combination long-term without causing resistance, leading to more effective ways to prevent these infections.
Cardio-REACT (Reducing Exposure to Antipsychotic-induced Cardiometabolic Threats)
Loretta Hsueh, PhD - College of Liberal Arts and Sciences
Negar Soheili, PhD - College of Business Administration
Vanessa Oddo, PhD - College of Applied Health Sciences
Sabrin Rizk, PhD, OTR/L - College of Applied Health Sciences
Second-generation antipsychotics are increasingly prescribed on- and off-label to U.S. youth to treat a wide breadth of mental and behavioral disorders, including autism spectrum/developmental disorders, bipolar disorders, and aggressive/irritable behavioral symptoms. Despite their mental health benefits, antipsychotic medications carry significant and severe cardiometabolic risks to youth, including weight gain, dyslipidemia and dysglycemia, increasing the likelihood of future cardiometabolic disorders and early mortality. We propose Cardio-REACT, a data-driven, stakeholder-informed, mixed-methods approach to determine the primary variables contributing to cardiometabolic screening noncompletion. Cardio-REACT will be the first to use advanced data-driven techniques to identify characteristics of those at-risk of noncompletion via Cosmos data (Aim 1) and optimize models to identify the highest reward actionable targets (Aim 2), while contextualizing findings by soliciting input from diverse stakeholders (Aim 3). In combination, these aims will inform our understanding of the at-risk population and identify actionable targets for future intervention.
Equitable Trials
Mohan Zalake, PhD - College of Applied Health Sciences
Zisu Wang, PhD – College of Business Administration
Lu Cheng, PhD – College of Engineering
Janice Krieger - Mayo Clinic Comprehensive Cancer Center Florida
The overarching goal of this project is to increase participation among underrepresented Black adults in colon cancer clinical trials by leveraging Artificial Intelligence (AI)-driven social media recruitment strategies. Black adults face disproportionately higher rates of colon cancer morbidity and mortality compared to other groups. Despite this burden, Black adults are significantly underrepresented in colon cancer clinical trials, only 6.6% compared to 77% for White patients. This underrepresentation limits the generalizability of clinical trial results and perpetuates health disparities. Our study aims to determine how advances in large language models (LLMs) and virtual agents can enhance existing social media recruitment strategies through a two-fold approach: (a) improve awareness: developing LLM-based algorithms that infer target populations by combining social media data with clinical trial eligibility criteria to improve recruitment efficiency, and (b) provide education and address distrust: utilizing virtual agents tailored to provide education, address concerns, and build trust by engaging potential participants with relevant information.