Molecular events surrounding collagen fibril assembly in the early healing rabbit medial collateral ligament—failure to recapitulate normal ligament development
Bibliographic record
Abstract
??Although injuries to the medial collateral ligament (MCL) can heal functionally without surgical intervention, the collagen fibers in the healing tissue remain compromised. The molecular basis for this poor healing potential was investigated by examining extracellular matrix-modifying molecules such as bone morphogenetic protein 1 (BMP-1), procollagen C proteinase enhancer (PCOLCE), lysyl oxidase (LOX), and transforming growth factor beta 1 (TGF-β1) involved in collagen fibrillogenesis during normal early postnatal ligament maturation and at comparable intervals after MCL injury. Samples of midsections of rabbit MCLs were collected from 3-, 6-, 14-, and 52-week-old normal animals and at 3, 6, and 14 weeks postinjury. Harvested midsubstance tissues were analyzed for collagen fibril diameter by transmission electron microscopy (TEM), and mRNA levels were assessed by reverse transcription-polymerase chain reaction (RT-PCR). Results showed different patterns of expression between normal MCL maturation and during scar maturation. BMP-1 and PCOLCE mRNA levels were upregulated in the 3?14-week period during maturation of normal ligaments but decreased at skeletal maturity. The scar tissue exhibited a 3.5-fold increase in PCOLCE mRNA levels during the early healing phase, but these decreased with time. After injury, BMP-1 mRNA levels in scars were low and did not change during healing. Both LOX and TGF-β1 mRNA levels were low during normal MCL development compared with levels at maturity and exhibited elevated mRNA levels during early healing that decreased with time postinjury. These results suggest that gene expression in scars during MCL healing does not recapitulate expression in normal ligament fibroblasts during 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) | 0.000 | 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".