Ageing Changes in the Tensile Properties of Tendons: Influence of Collagen Fibril Volume Fraction
Bibliographic record
Abstract
Connective tissues are biological composites comprising of collagen fibrils embedded in (and reinforcing) the hydrated proteoglycan-rich (PG) gel within the extracellular matrices (ECMs). Age-related changes to the mechanical properties of tissues are often associated with changes to the structure of the ECM, namely, fibril diameter. However, quantitative attempts to correlate fibril diameter to mechanical properties have yielded inconclusive evidence. Here, we described a novel approach that was based on the rule of mixtures for fiber composites to evaluate the dependence of age-related changes in tendon tensile strength (sigma) and stiffness (E) on the collagen fibril cross-sectional area fraction (rho), which is related to the fibril volume fraction. Tail tendons from C57BL6 mice from age groups 1.6-35.3 months old were stretched to failure to determine sigma and E. Parallel measurements of rho as a function of age were made using transmission electron microscopy. Mathematical models (rule of mixtures) of fibrils reinforcing a PG gel in tendons were used to investigate the influence of rho on ageing changes in sigma and E. The magnitudes of sigma, E, and rho increased rapidly from 1.6 months to 4.0 months (P-values <0.05) before reaching a constant (age independent) from 4.0 months to 29.0 months (P-values >0.05); this trend continued for E and rho (P-values >0.05) from 29.0 months to 35.3 months, but not for sigma, which decreased gradually (P-values <0.05). Linear regression analysis revealed that age-related changes in sigma and E correlated positively to rho (P-values <0.05). Collagen fibril cross-sectional area fraction rho is a significant predictor of ageing changes in sigma and E in the tail tendons of C57BL6 mice.
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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) | 0.000 | 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 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".