Document Type
Honors Thesis
Abstract
Quantitative gait analysis is a standard tool for assessing functional recovery in preclinical musculoskeletal research; however, the specific contributions of individual parameters —including stride length, step length, paw area ratio, and paw angle—remain poorly defined. This lack of clarity makes it difficult to determine whether therapeutic improvements reflect reduced pain-related compensation or true structural regeneration. This study established a time-sensitive, standardized framework to decouple these mechanisms in a preclinical labral tear model. To minimize individual variation and baseline noise, all gait data were normalized to a 1–10 scale. These processed parameters were subsequently correlated with key histological markers of inflammation and tissue repair.
Our analysis revealed that paw area ratio and paw angle strongly correlated with mast cell activation (a pro-inflammatory biomarker) at 3 weeks, identifying them as indicators of early-stage analgesia and reduced guarding. Conversely, stride and step length were significantly associated with extracellular matrix (ECM) production (a regenerative biomarker) at 6 weeks, indicating a return of mechanical stability. These divergent temporal and histological profiles enabled the development of a standardized gait scoring system that quantitatively distinguishes early analgesic effects from late-stage regenerative recovery. By validating these parameters as distinct, non-invasive functional biomarkers, this framework provides a rigorous, high-throughput tool to complement histology in evaluating the efficacy of regenerative therapies.
Disciplines
Biological Engineering
Publication Date
5-2026
Language
English
Faculty Mentor of Honors Project
Dr. Liping Tang
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Rifat, Sumaiya, "Quantitative Gait Analysis Distinguishes the Functional Effects of Inflammatory Modulation and Tissue Regeneration in an In Vivo Labral Tear Model" (2026). 2026 Spring Honors Capstones Projects. 8.
https://mavmatrix.uta.edu/honors_spring2026/8
Comments
I would like to express my sincere gratitude to Dr. Liping Tang, Professor in the Department of Bioengineering at The University of Texas at Arlington, for his guidance, mentorship, and continued support throughout this project. I am especially thankful for the opportunity and resources he provided to conduct this research in his laboratory, as well as for his encouragement, which motivated me to pursue and develop this work with confidence and independence.
I would like to thank Uyen Pham for her assistance in collecting both gait and histological data, which contributed significantly to this study. I also extend my deepest appreciation to Le Hoang and Bhavya Vaish, Ph.D., for their invaluable training in gait analysis and histological analysis, respectively. Their expertise and guidance were essential to the development of this work.