Tendon mineralization is accelerated bilaterally and creep of contralateral tendons is increased after unilateral needle injury of murine achilles tendons
Bibliographic record
Abstract
Heterotopic mineralization may result in tendon weakness, but effects on other biomechanical responses have not been reported. We used a needle injury, which accelerates spontaneous mineralization of murine Achilles tendons, to test two hypotheses: that injured tendons would demonstrate altered biomechanical responses; and that unilateral injury would accelerate mineralization bilaterally. Mice underwent left hind (LH) injury (I; n = 11) and were euthanized after 20 weeks along with non-injured controls (C; n = 9). All hind limbs were examined by micro computed tomography followed by biomechanical testing (I = 7 and C = 6). No differences were found in the biomechanical responses of injured tendons compared with controls. However, the right hind (RH) tendons contralateral to the LH injury exhibited greater static creep strain and total creep strain compared with those LH tendons (p ≤ 0.045) and RH tendons from controls (p ≤ 0.043). RH limb lesions of injured mice were three times larger compared with controls (p = 0.030). Therefore, despite extensive mineralization, changes to the responses we measured were limited or absent 20 weeks postinjury. These results also suggest that bilateral occurrence should be considered where tendon mineralization is identified clinically. This experimental system may be useful to study the mechanisms of bilateral new bone formation in tendinopathy and other conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".