In tendons, differing physiological requirements lead to distinct patterns of MMP-1 degradation
Bibliographic record
Abstract
Collagen fibrils from high-stress, energy-storing tendons critical to locomotion are smaller in diameter with increased intermolecular crosslinking compared to fibrils from low-stress, positional tendons. This results in distinct loading mechanics thought to limit fatigue damage in energy-storing tendons. However, there appears to be a functional trade-off with energy-storing tendons also having reduced remodeling ability. Energy-storing tendons have lower collagen turnover and increased injury rates compared to positional tendons. In a recent study, a causative factor for this lower collagen turnover was suggested: resistance to degradation by MMP-1. To validate the prior study's results obtained from single fibrils, the current study undertook population level assessment of fibril degradation by MMP-1. Predictive degradation models were created to assess fibril diameter distribution changes. Positional and energy-storing tendon sections were incubated for 24 h with buffer or MMP-1, imaged with scanning electron microscopy, and analysed with a custom pipeline for piece-wise fibril measurement. Enzyme treated sections showed evidence of degradation with reduced fibril diameter, decreased alignment, increased curvature, and decreased D-band length. Energy-storing tendon fibrils were more resistant to enzymolysis, with only the large diameter fibril subpopulation affected by MMP-1 (15% diameter reduction compared to control), while the entire population of positional tendon fibrils decreased in diameter (41%). Comparison to model predictions confirmed a linear relationship of degradation with fibril size. Larger fibrils experienced greater diameter decreases combined with increased longitudinal diameter variation and D-band decreases. Crosslinking is thought to be responsible for both fibril type and size findings, the latter suggesting higher density crosslinking in the fibril core.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".