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| Funder | NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES |
|---|---|
| Recipient Organization | Washington University |
| Country | United States |
| Start Date | Sep 01, 2024 |
| End Date | Jul 31, 2029 |
| Duration | 1,794 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10979895 |
PROJECT SUMMARY Post-traumatic joint contracture (PTJC) causes debilitating loss of motion following joint injury and is particularly impactful in the elbow. Clinical treatment is limited due to a poor understanding of key mechanisms leading to motion loss, making treatment targets elusive. This study will use a validated preclinical animal model of PTJC
to identify the key aspects of physical- and biological-based interventional strategies that best limit PTJC following injury. Early joint remobilization improves range-of-motion (ROM); however, clinical practice requires a period of joint immobilization following injury to reduce instability and prevent joint overloading. The parameters
of active therapy (i.e., initiation, duration, intensity) that best limit PTJC after an initial immobilization period without destabilizing or overloading the healing joint remain unknown. In addition, while studies have shown that modulation of the inflammatory response can improve healing after joint injury, and that T-cell-mediated signaling
might represent a particularly effective target, protocols guiding inflammation-based therapeutic approaches for PTJC remain poorly defined. Overall objective: identify fundamental aspects of physical and biological treatment strategies (i.e., initiation, duration, intensity, synergy) that prevent the development of PTJC using a preclinical
animal model and multi-modal, machine learning (ML)-based analyses. Aim 1: Identify parameters of voluntary active physical therapy that are most critical to minimizing PTJC while promoting healing after joint injury. This study will determine the optimal implementation of active physical therapy protocols to best
preserve ROM yet limit load-induced damage. Image-based ML algorithms will be used to automate/accelerate spatial analysis of joint tissues and advance clustering analyses to elucidate cell- and tissue-level responses to physical treatments. Hypothesis: moderate intensity/duration physical therapy will maximize motion and limit
joint damage, with additional benefit achieved by implementing a slightly staged increase in intensity after joint remobilization. Aim 2: Develop biological strategies to reduce PTJC using anti-inflammatory intervention and targeted modulation of the T cell mediated immune response following joint injury. Anti-inflammatory
prevention strategies will be developed and strategically combined with physical therapy to target multiple phases of immune-mediated biological activity. ML algorithms will combine multi-modal experimental data to explore spatial relationships in PTJC pathophysiology. Hypotheses: (i) reducing inflammation in the post-injury
and post-remobilization periods will help preserve ROM; (ii) improved outcomes from blocking T cell activity will demonstrate a key mechanism of PTJC etiology; (iii) ML-driven data analysis will determine that abrogation of capsule fibrosis, reduced remobilization-induced ligament hypertrophy, and limited T cell activity will be most
predictive of preserved joint function. While results obtained using an animal model aren’t directly translatable to human care, this study will greatly advance understanding of PTJC pathophysiology and elucidate key principles of physical and biological interventional strategies that can be leveraged to inform future treatment of PTJC.
Washington University
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