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Chronic hepatitis C virus (HCV) infection is a leading cause of cirrhosis worldwide and kills more Americans than 59 other infections, including HIV and tuberculosis, combined. While direct-acting antiviral (DAA) treatments are effective, limited uptake of therapy, particularly in high-risk groups, remains a substantial barrier to eliminating HCV. We developed a long-acting DAA system (LA-DAAS) capable of prolonged dosing and explored its cost-effectiveness. We designed a retrievable coil-shaped LA-DAAS compatible with nasogastric tube administration and the capacity to encapsulate and release gram levels of drugs while resident in the stomach. We formulated DAAs in drug-polymer pills and studied the release kinetics for 1 mo in vitro and in vivo in a swine model. The LA-DAAS was equipped with ethanol and temperature sensors linked via Bluetooth to a phone application to provide patient engagement. We then performed a cost-effectiveness analysis comparing LA-DAAS to DAA alone in various patient groups, including people who inject drugs. Tunable release kinetics of DAAs was enabled for 1 mo with drug-polymer pills in vitro, and the LA-DAAS safely and successfully provided at least month-long release of sofosbuvir in vivo. Temperature and alcohol sensors could interface with external sources for at least 1 mo. The LA-DAAS was cost-effective compared to DAA therapy alone in all groups considered (base case incremental cost-effectiveness ratio $39,800). We believe that the LA-DAA system can provide a cost-effective and patient-centric method for HCV treatment, including in high-risk populations who are currently undertreated., NIH (Grants EB000244 and 5T32DK007191-45)

Physical interaction with tools is ubiquitous in functional activities of daily living. While tool use is considered a hallmark of human behavior, how humans control such physical interactions is still poorly understood. When humans perform a motor task, it is commonly suggested that the central nervous system coordinates the musculo-skeletal system to minimize muscle effort. In this paper, we tested if this notion holds true for motor tasks that involve physical interaction. Specifically, we investigated whether humans minimize muscle forces to control physical interaction with a circular kinematic constraint. Using a simplified arm model, we derived three predictions for how humans should behave if they were minimizing muscular effort to perform the task. First, we predicted that subjects would exert workless, radial forces on the constraint. Second, we predicted that the muscles would be deactivated when they could not contribute to work. Third, we predicted that when moving very slowly along the constraint, the pattern of muscle activity would not differ between clockwise (CW) and counterclockwise (CCW) motions. To test these predictions, we instructed human subjects to move a robot handle around a virtual, circular constraint at a constant tangential velocity. To reduce the effect of forces that might arise from incomplete compensation of neuro-musculoskeletal dynamics, the target tangential speed was set to an extremely slow pace (~1 revolution every 13.3 seconds). Ultimately, the results of human experiment did not support the predictions derived from our model of minimizing muscular effort. While subjects did exert workless forces, they did not deactivate muscles as predicted. Furthermore, muscle activation patterns differed between CW and CCW motions about the constraint. These findings demonstrate that minimizing muscle effort is not a significant factor in human performance of this constrained-motion task. Instead, the central nervous system likely priori, National Institutes of Health (U.S.) (R01-HD087089), National Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637824), National Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER-1548501), Eric P. and Evelyn E. Newman Fund, Gloria Blake Endowment Fund, National Institutes of Health (U.S.) (R01-HD081346), National Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637854), National Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER-1548514)