Mitra, Kunal

ABSTRACT Traumatic brain injury (TBI), particularly among military personnel, poses significant challenges in acute management and long-term outcomes. Difficult-to-diagnose autoregulatory and inflammatory changes lead to neurocognitive impairment, anxiety, sleep disturbance, and chronic pain. To optimize readiness and test therapeutics for both diagnostic and future TBI treatments, this feasibility study introduces a novel wearable multimodal platform that integrates photoplethysmography and infra-red (IR) sensing from a camera and a wearable sensor to detect vital signs and then leverages machine learning (ML) to predict the need for acute treatment. This prospective observational study involving 28 healthy volunteers simulated a triage emergency room workflow to test sensor accuracy and mobile app functionality. The wearable device demonstrated high accuracy in pulse rate (mean error 7.3%) and oxygen saturation (mean error −1.2%) compared to standard-of-care devices. Camera-based pulse and respiratory rate, and wearable pulse, oxygen, and temperature sensing were tested in a mock triage setting to simulate activation of light-based treatment upon detection of physiological changes of concern after acute TBI. Preliminary ML models to predict hypotension and pain based on retrospective data achieved 98.4% and 93% accuracy but showed limitations in prospective real-time applications. This study provided insights to prepare for future iterations that will integrate near-IR spectroscopy to detect cerebral oximetry, and quantum dot light-emitting diodes to activate light-based treatment (photobiomodulation) from within a wearable head dressing. The findings underscore the potential of this platform to bridge critical gaps in acute TBI care through early detection and future therapeutic intervention. Further development is needed to address environmental challenges, improve accuracy across diverse populations, and refine the device for real-time clinical use. This innovative approach has the potential to revolutionize TBI care by bridging current gaps in diagnostic and therapeutic management.

Limited scalability and restricted affordability impede the equitable deployment of curative models of care despite advances achieved with regenerative medicine therapeutics. Mitigating the risk of widening health disparities mandates actions that would improve the availability and accessibility of new classes of biotherapeutics. Namely, the use of Smart Manufacturing empowered by artificial intelligence to increase therapeutic production capacity while reducing cost is an emerging strategy central to the future of the regenerative care economy. Establishing an efficient and effective biomanufacturing ecosystem is essential to building regenerative pipelines into broadly available regenerative therapeutics options. This ecosystem must provide not only necessary cell environment controls and computer process power, but also the sensing technologies to feed critical process parameters to the algorithms in real-time. Here we outline key elements that are in development to support Smart Biomanufacturing, such as machine learning, advanced analytics, digital twins, and modular cytocentric production. The aim of integrating these technologies is to drive down costs, improve access to new regenerative medicine therapeutics, address regulatory science expectations, and in doing so, improve health equity.