1159 - Characterization Of Golden Retriever Muscular Dystrophy Biomechanics With Motion Capture During A 6-month Longitudinal Study
Bibliographic record
Abstract
INTRODUCTION: Muscular Dystrophies (MD) are incurable diseases that progressively weaken voluntary muscles [1]. The most severe type of MD is Duchenne Muscular Dystrophy (DMD) which affects 1 in 4000-6000 boys [2]. Patients are wheelchair bound in their teens and typically die from respiratory failure or cardiomyopathy with an average life expectancy of 26 years [3]. Golden Retriever Muscular Dystrophy (GRMD) is considered a good animal model for DMD due to its genetic homology and is used to study DMD and evaluate treatments [2]. The purpose of this study is to develop a non-invasive motion capture method based on gait analysis parameters to characterize GRMD biomechanics and ultimately assess treatments proposed to improve muscular control.METHODS: Twelve dogs were divided into a control group of 5 healthy dogs, a treatment group of 5 affected dogs treated with an adeno-associated virus gene therapy and a sham control of 2 untreated affected dogs. They were studied at 2, 4 and 6 months of age. Each dog was equipped with 18 reflective markers placed on anatomical landmarks on their hind limbs. Dogs were walked at a self selected speed within a volume covered by 8 near infrared motion capture cameras recording at 100Hz. Data was collected for several consecutive steps in the middle of the capture volume. Steps from the beginning and the end of the walk were excluded to avoid acceleration impact and to make sure collected data was representative of the dogu2019s gait. After gathering data according to walking speed, joint range of motion (ROM), stride length and duration, paw elevation, and movement correlation were calculated for each dog and compared at different time points. Studentu2019s T-tests were performed to assess the statistical significance of results between the three groups.. The analysis was blinded to minimize bias. The study was conducted according to institutional animal care guidelines and was approved by the Animal Use Protocol AUP 081065.RESULTS: GRMD dogs (treated and untreated) showed lower and decreasing ROM for the hock angle compared to healthy dogs. They had a similar ROM but with more extended limbs for the stifle angle and the pelvic tilt. Paw elevation and stride length ratios for all GRMD dogs decreased with age while it was stable for their counterparts. Step duration was on average 44u00b121% longer for healthy dogs and 19u00b114% longer for affected and treated dogs compared to affected and non-treated dogs. Other parameters did not show significant differences between the groups.DISCUSSION: This motion capture method has been able to identify significant differences between affected and non-affected subjects based on their ROM, paw elevation and stride length. The slight improvement trend seen on step duration for treated dogs could be attributed to gene therapy efficacy but further studies with more subjects have to be conducted to confirm this assessment.SIGNIFICANCE/CLINICAL RELEVANCE: Golden Retrievers are considered to be the most relevant model for Duchenne Muscular Dystrophy, and studying the first 6 months of life of GRMD dogs is critical as it is considered to correspond to the first 10 years of life of DMD children. The current biomechanical characterization of the disease evolution in the dog model suggests the possibility of a similar protocol adapted for humans. Our laboratory is currently going through IRB approval to start a DMD biomechanics research study at the Orthopedic Biomechanics Research Laboratory (Houston) in collaboration with Houston Methodist Research Institute.REFERENCES: [1] Dubowitz, V. (1977). Muscular Dystrophy (pp. 5-8). Karger Publishers.[2] Kornegay, J.N. (2017). Skeletal muscle, 7(1), 9.[3] Eagle, M. et al., (2002). Neuromuscular disorders, 12(10), 926-929.ACKNOWLEDGEMENTS: Texas A&M College of Veterinary Medicine provided the dogs and funding for this research project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".