Denervation alters mRNA levels of repair-associated genes in a rabbit medial collateral ligament injury model
Bibliographic record
Abstract
Previous experiments revealed that denervation impairs healing of the MCL. This suggested the hypothesis that denervation would decrease repair-associated mRNA levels in the injured MCL when compared with normally innervated injured MCL. 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 rabbitspecific primers. 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 injury group (p < or = 0.01). An increased level of TSP-1 mRNA was also detected in the denervated injured group at sixteen weeks post injury (p < or = 0.01). 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 may potentially contribute to the decreased quality of the scar tissue, particularly molecules such as TSP-1. Neuronal derived factors strongly influence the in vivo metabolic activity of ligament and scar fibroblasts in the initial phases of healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".