Thrombocytopenia alters early but not late repair in a mouse model of Achilles tendon injury
Bibliographic record
Abstract
Besides their hemostatic function, platelets can express key factors involved in tissue healing. However, the role of platelets in tendon healing following acute injury is poorly understood. We investigated this role by injecting male C57BL/6 mice with an antiplatelet antibody to induce thrombocytopenia. Placebo animals received serum only. The right Achilles tendon was sectioned and sutured using the 8-strand technique that allows immediate weight bearing. Platelet depletion did not alter the accumulation of neutrophils and macrophages or cell proliferation. A slight increase in vascularization was observed 7 days postinjury in tendons from thrombocytopenic mice relative to placebo animals, but the effect had disappeared by day 14. Furthermore, collagen content had a tendency to decrease in Achilles tendons under thrombocytopenia when compared with placebo treatment at 7 days posttrauma. This was correlated with a decline in maximal stress sustained by tendons at day 14 but not after 28 days. The impact of thrombocytopenia was otherwise negligible, as force relaxation and stiffness were similar in the two groups. Our findings demonstrate that platelets modulate early tendon repair following rupture, although the effect is limited over time. Nevertheless, platelets are not essential for the recruitment of inflammatory cells, proliferation, angiogenesis, and tendon maturation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".