Empirical evidence that bone collagen molecules denature as a result of bone fracture
Bibliographic record
Abstract
Cortical bone tissue, primarily composed of collagen, hydroxyapatite, and water, is a strong and tough natural, structural biomaterial. The integrity of the collagenous phase (native triple helix vs. damaged/denatured coil) has previously been correlated via various means, including hydrothermal isometric tension testing and FTIR and Raman spectroscopy, with the capability of cortical bone to undergo stable fracture. Collagen is a relatively stable protein, requiring around 70 J/g to thermally denature its native triple helix structure, through the melting of hydrogen bonds. It is widely thought that bone collagen molecules denature (unravel) during fracture, acting as a molecular-scale mechanical toughening mechanism, but this has not been empirically demonstrated to date. A new technology, fluorescently-labeled collagen hybridizing peptides (F-CHP), enables imaging that specifically detects denatured collagen. This provides an opportunity to empirically test whether bone collagen molecules do denature during bone fracture. Here, F-CHP was used to stain fracture surfaces produced by transverse Mode-I fracture of chevron-notched bovine and human cortical bone beams. The fracture surfaces demonstrated increased staining, above the level of rigorous paired controls, and the staining directly correlated with the work-to-fracture (WFx) of bovine bone beams. This increased denaturation signal was also constrained to a rough textured region visible on the fracture surface, which is known to correspond with stable tearing. Similar staining was also detected on the fracture surfaces of human cortical bone. Increased staining was not detected on the fracture surfaces of specimens that were dehydrated prior to fracture, suggesting a role for water in the denaturation process. This study provides the first empirical evidence of bone collagen denaturation resulting from cortical bone fracture and extends our understanding of this mechanism towards the mechanical performance of cortical bone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".