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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".