Side-to-Side Nerve Grafts Sustain Chronically Denervated Peripheral Nerve Pathways During Axon Regeneration and Result in Improved Functional Reinnervation
Bibliographic record
Abstract
BACKGROUND: Progressive atrophy of Schwann cells in denervated nerve stumps is a major reason for progressive failure of functional recovery after peripheral nerve injury and surgical repair. OBJECTIVE: To examine whether side-to-side nerve bridges between an intact donor nerve and a recipient denervated distal nerve stump promote nerve growth and in turn, protect distal nerve stumps to improve axon regeneration after delayed surgical repair. METHODS: In Sprague-Dawley rats, 1 or 3 side-to-side common peroneal (CP) nerve bridges were used to bridge between the donor intact tibial (TIB) nerve and a recipient denervated CP distal nerve stump in the contralateral hind limb. No bridges were placed in control animals. After 4 months, either a fluorescent retrograde dye was applied to back-label TIB motoneurons with axons that had grown into the CP nerve stump or the proximal and distal CP nerve stumps were resutured in experimental and control animals to encourage CP nerve regeneration for 5 months. Retrograde dyes were again applied to count CP motoneurons that regenerated their axons through protected and unprotected nerve stumps. RESULTS: Significantly more donor TIB motoneurons regenerated axons into the recipient denervated CP nerve stump through 3 side-to-side CP nerve bridges compared with 1 bridge. This TIB nerve protection significantly increased the number of CP motoneurons regenerating axons through the denervated CP nerve stumps, the number of regenerated axons, and the weight of the reinnervated muscles. CONCLUSION: Multiple side-to-side nerve bridges protect chronically denervated nerve stumps to improve axon regeneration and target reinnervation after delayed nerve 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".