Dr. John Schneider, Director of the Third Coast CFAR Developmental Core, is pleased to announce the winners of the Winter 2017 Pilot competition. These projects represent new and innovative ideas within the Third Coast CFAR and will help the selected investigators as they apply for HIV research funds from NIH in the future.
Director of Research, Center for Data Science and Public Policy
The University of Chicago Harris School of Public Policy
This work seeks to advance predictive modeling for implementing healthcare delivery, particularly predicting whether an HIV-positive individual will retain in care. Despite the importance of retaining patients in care little it known about the factors – geographic, economic, social – that lead a patient to drop out of care. There is a great opportunity for innovation to use machine learning to uncover the factors that lead to a person to drop out of care and providing a personalized intervention to bring them back into care.
Joshua L. Leonard, PhD
Associate Professor of Chemical and Biological Engineering
Northwestern University McCormick School of Engineering
Nuclease proteins that degrade genomically integrated HIV DNA have been developed, but no strategy has yet been identified for delivering such proteins to infected cells in a patient. To potentially address this need, this project will harness a recently-appreciated mechanism by which cells transfer their contents to other cells via the secretion and subsequent uptake of nano-scale extracellular vesicles. If successful, this project could ultimately establish a novel therapeutic strategy for addressing the persistent challenge of eliminating HIV reservoirs.
Carlos Gallo, PhD
Research Assistant Professor of Psychiatry and Behavioral Sciences
Faculty Affiliate, Center for Prevention Implementation Methodology (Ce-PIM)
Northwestern University Feinberg School of Medicine
Mobile and text-based interventions provide unique opportunities to engage adolescent MSM in HIV prevention. However, mHealth interventions suffer from low usage which mitigates their efficacy. This project will use computational linguistics methods to analyze users’ demonstrated linguistic preferences with the goal of improving future mHealth interventions. Mobile and text-based programs with high uptake and adherence have the potential to promote HIV testing, harm reduction, and engagement in health care.