Plasticity of peptidergic innervation in healing rabbit medial collateral ligament.
Bibliographic record
Abstract
BACKGROUND: Denervation substantially impairs healing of the medial collateral ligament (MCL). Because normal ligaments are sparsely innervated, we hypothesized that neuropeptide-containing neurons would sprout or proliferate after ligament transection, followed by later regression with healing, in a manner analogous to blood vessels. METHODS: We transected the right MCL in 9 mature female New Zealand white rabbits and killed 3 rabbits at 2, 6 or 14 weeks. Alternate sets of 12-mm serial sections of healing MCL scars were examined by fluorescent immunohistochemistry for substance P (SP), calcitonin gene-related peptide (CGRP), neuropeptide Y (NPY) and pan-neuronal marker PGP9.5. RESULTS: Normal MCLs had few peptidergic fibres located in the epiligament in a perivascular pattern. At 2 weeks, PGP9.5-, SP-and CGRP-positive fibres had increased in the epiligament adjacent to the injury. By 6 weeks, there were increases in CGRP-and PGP9.5-positive fibres in epiligament and scar, with similar but less marked increases in SP-positive fibres. At 14 weeks, there was notable regression of immunostained peptidergic nerve fibres in the scar. CONCLUSION: This experiment shows evidence for a remarkable plasticity of ligament innervation after injury, supporting the idea that neuronal factors play a fundamental role in wound 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".