A role for calcitonin gene-related peptide in rabbit knee joint ligament healing
Bibliographic record
Abstract
Knee joint ligament healing has been shown to be improved when the torn ligament ends remain in contact, however, the rationale for these effects is unknown. The sensory neuropeptide calcitonin gene related peptide (CGRP) has potent trophic and vasodilatatory properties and as such is thought to be advantageous in wound repair. In ascertaining a role for CGRP in rabbit medial collateral ligament healing, the present study examined changes in CGRP-like immunoreactivity (CGRP-LI) and CGRP-mediated vasomotor responses in gap injured (non-contact), Z-plasty apposed (contact), and sham operated control medial collateral ligaments. At 6 weeks post-trauma, CGRP-LI decreased in the healing zone of gap injured and Z-plasty apposed medial collateral ligaments compared with controls, and non-contact ligament nerve fibres exhibited an abnormal morphology. Topical administration of CGRP (10-13to 10-9mol) caused a dose-dependent increase in ligament perfusion in each experimental group of knees. The CGRP-mediated vasodilatation associated with gap injured ligaments was not significantly different from controls (P = 0.06), whereas apposed medial collateral ligaments showed an augmented response to the peptide (P < 0.0005). These findings indicate that the beneficial effects of ligament interposition post-trauma may be related to an enhanced responsiveness to CGRP in conjunction with a more typical re-innervation profile. Conversely, the aberrant characteristics of CGRP-LI nerves occurring in gap injured tissue is suggestive of impaired CGRP release which may explain the poor functional recovery associated with these ligaments.Key words: blood flow, injury, knee joint, neuropeptides, wound repair.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".