Repeated subrupture overload causes progression of nanoscaled discrete plasticity damage in tendon collagen fibrils
Bibliographic record
Abstract
A critical feature of tendons and ligaments is their ability to resist rupture when overloaded, resulting in strains or sprains instead of ruptures. To treat these injuries more effectively, it is necessary to understand how overload affects the primary load-bearing elements of these tissues: collagen fibrils. We have investigated how repeated subrupture overload alters the collagen of tendons at the nanoscale. Using scanning electron microscopy to examine fibril morphology and hydrothermal isometric tension testing to look at molecular stability, we demonstrated that tendon collagen undergoes a progressive cascade of discrete plasticity damage when repeatedly overloaded. With successive overload cycles, fibrils develop an increasing number of kinks along their length. These kinks-discrete zones of plastic deformation known to contain denatured collagen molecules-are accompanied by a progressive and eventual total loss of D-banding along the surface of fibrils, indicating a loss of native molecular packing and further molecular denaturation. Thermal analysis of molecular stability showed that the destabilization of collagen molecules within fibrils is strongly related to the amount of strain energy dissipated by the tendon after yielding during tensile overload. These novel findings raise new questions about load transmission within tendons and their fibrils and about the interplay between crosslinking, strain-energy dissipation ability, and molecular denaturation within these structures.
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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".