Relative contributions of mechanical degradation, enzymatic degradation, and repair of the extracellular matrix on the response of tendons when subjected to under‐ and over‐ mechanical stimulations in vitro
Bibliographic record
Abstract
Tendon response to mechanical loading results in either homeostasis, improvement, or degeneration of tissue condition. In an effort to better understand the development of tendinopathies, this study investigated the mechanical and structural responses of tendons subjected to under- and over-stimulations (1.2% and 1.8% strain respectively, 1 Hz). The objective was to examine three sub-processes of tendon response: mechanical degradation, enzymatic degradation, and repair of the extracellular matrix. We subjected rat tail tendons to a 10-day stimulation protocol with four periods of 6 h each day: 30 min of stimulation and 5 h 30 min of rest. To investigate the contribution of the three sub-processes, we controlled the contribution of the cells through variations in the nutrient and protease inhibitor content in the in vitro solutions. Using nondestructive cyclic tests, we evaluated the daily changes in the peak stress. To assess structural changes, we carried out microscopic analyses at the end of the study period. We observed that the relative contributions of the sub-processes differed according to the stimulation amplitude. With over-stimulation of tendons immersed in DMEM, we succeeded in reducing enzymatic degradation and increasing peak stress. In under-stimulation, the addition of protease inhibitors was required to obtain the same result.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".