A Nonlinear Rheological Assessment of Muscle Recovery from Eccentric Stretch Injury
Bibliographic record
Abstract
PURPOSE: To better understand the mechanical behavior of healing skeletal muscle; specifically the tissue's response after acute eccentric stretch injury. METHODS: Rabbit tibialis anterior (TA) muscle tendon units were subjected to an in vivo single stretch (eccentric) injury and mechanically evaluated (constant rate elongation to failure) at 1, 3, and 7 d postinjury. In addition to a traditional linear analysis (linear stiffness and failure load), an existing nonlinear rheological model was modified to interpret the experimental load-to-failure data. The models' performance were evaluated and discussed. RESULTS: No significant injury effect was observed, either within or between groups, across the 7-d healing interval, using the linear analysis. However, interpretation of the data using our nonlinear phenomenological model identified significant changes in mechanical behavior that went undetected by linear analyses. Percent differences, between injured and contralateral control limbs, of model parameter estimates were analyzed. Nonparametric statistical analysis illustrated significant changes in the first-order stiffness (k1) throughout the 7-d healing interval. Model simulations using mean values of each parameter revealed increased low-load tissue compliance after injury, with a decrease in linear slope that recovered steadily toward control values by day 7. At 7 d postinjury, virtually no differences were observed between injured and sham control tissues. CONCLUSIONS: Our findings suggest that acute eccentric injury increases the muscle's compliance 24 h after injury, with a steady recovery to uninjured values by the 7th day, yet these changes went undetected by linear analysis. Therefore, nonlinear analysis is necessary to recognize valuable information contained in the low-load region and to quantify important biomechanical phenomena of stretch-injured healing skeletal muscle.
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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 | 0.001 |
| 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".