Cross‐link stabilization does not affect the response of collagen molecules, fibrils, or tendons to tensile overload
Bibliographic record
Abstract
We investigated whether immature allysine-derived cross-links provide mechanically labile linkages by exploring the effects of immature cross-link stabilization at three levels of collagen hierarchy: damaged fibril morphology, whole tendon mechanics, and molecular stability. Tendons from the tails of young adult steers were either treated with sodium borohydride (NaBH₄) to stabilize labile cross-links, exposed only to the buffer used during stabilization treatment, or maintained as untreated controls. One-half of each tendon was then subjected to five cycles of subrupture overload. Morphologic changes to collagen fibrils resulting from overload were investigated using scanning electron microscopy, and changes in the hydrothermal stability of collagen molecules were assessed using hydrothermal isometric tension testing. NaBH4 cross-link stabilization did not affect the response of tendon collagen to tensile overload at any of the three levels of hierarchy studied. Cross-link stabilization did not prevent the characteristic overload-induced mode of fibril damage that we term discrete plasticity. Similarly, stabilization did not alter the mechanical response of whole tendons to overload, and did not prevent an overload-induced thermal destabilization of collagen molecules. Our results indicate that hydrothermally labile cross-links may not be as mechanically labile as was previously thought.
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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".