Neuronal Regulation of Repair-Associated Gene mRNA Levels in Healing Rabbit Medial Collateral Ligament
Bibliographic record
Abstract
Rabbit medial collateral ligament healing is impaired by denervation. We found that blood flow was decreased by two-thirds, angiogenesis was decreased by a third and tensile strength was decreased by a third in denervated compared to innervated ligaments at six weeks after injury. PURPOSE: To determine the effect of denervation on mRNA levels for repair-associated molecules in the injured medial collateral ligament (MCL). The hypothesis to be tested was that denervation would decrease repair-associated mRNA levels in the injured MCL when compared with normally innervated injured MCL. METHOD: Adult, skeletally mature female rabbits were assigned to one of four groups: unoperated control, femoral nerve transection alone (denervated controls), MCL partial tear or denervated MCL partial tear. At three days, two weeks, six weeks or sixteen weeks post-surgery, cohorts of 6 rabbits from each experimental group were killed. Ligaments were harvested, RNA extracted and RT-PCR was performed using rabbit-specific primers. Results: In the denervated injury group, mRNA levels for the angiogenesis-associated gene MMP-13, matrix components Collagen I and III, growth factor TGF-beta and angiogenesis inhibitors TIMP-3 and TSP-1 had all increased by two-weeks post-injury, in comparison to the non-denervated injured group (p≤0.01). An increased level of TSP-1 mRNA was also detected in the denervated injured group at sixteen weeks post-injury (p≤0.01). CONCLUSION: Contrary to the initial hypothesis, denervation led to increased mRNA levels for many relevant molecules during the early stages of MCL healing. Thus, inappropriate timing of over-expression of some molecules, particularly molecules such as TSP-1 and TIMP-3, may potentially contribute to the decreased quality of the denervated scar tissue. Neuronal derived factors strongly influence the in vivo metabolic activity of ligament and scar fibroblasts in the initial phases of healing.
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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) | 0.001 | 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".