Department of Surgery

Congratulations to Gary An, MD and Chase Cockrell, PhD of the An-Cockrell Lab at UVM Larner College of Medicine on being a participant of a multi-institution research team led by the University of Pittsburgh that recently secured a $22 million grant from the Defense Advanced Research Projects Agency (DARPA) Bioelectronics for Tissue Regeneration (BETR) program. The  project is entitled: “REPAIR: Regenerative Electronic Patch through Advanced Intelligent Regulation”. The An-Cockrell lab will receive $2.8 million over the term of the award (more details regarding the UVM team’s role for the project is below). 

“REPAIR: Regenerative Electronic Patch through Advanced Intelligent Regulation”.

The project’s focus is to develop a device combining artificial intelligence, bioelectronics and regenerative medicine to regrow muscle tissue, especially after combat injuries. This team also includes researchers from Carnegie Mellon, Northwestern, Rice, University of Vermont, University of Wisconsin and Walter Reed National Military Medical Center as part of this four-year initiative. The goal of this project is to enhance the functional recovery of patients who suffer large muscle wounds (>20% of their volume) with a “smart device” that will be implanted into the wound. When more than 20% of a muscle is damaged, as common for soldiers wounded in recent overseas conflicts, the tissue can’t regenerate and a stiff scar forms in place of the missing muscle, which often leads to significant disability. The device will monitor key molecular signals at each stage of healing – from the first hours after injury to the days and weeks that follow – and deliver specific molecules at specific times under the direction of artificial intelligence.

The UVM team, led by Gary An (Division of Acute Care Surgery) and Chase Cockrell (Division of Surgical Research), will be the primary development group for the artificial intelligence controller for the device, and will receive $2.8 million over the term of the award. This work will involve integrating sophisticated mechanistic simulations with cutting edge machine learning methods and utilize high-performance computing resources, including those at the Vermont Advanced Computing Center (VACC).