Nerve Cross-Bridging to Enhance Nerve Regeneration in a Rat Model of Delayed Nerve Repair
Bibliographic record
Abstract
There are currently no available options to promote nerve regeneration through chronically denervated distal nerve stumps. Here we used a rat model of delayed nerve repair asking of prior insertion of side-to-side cross-bridges between a donor tibial (TIB) nerve and a recipient denervated common peroneal (CP) nerve stump ameliorates poor nerve regeneration. First, numbers of retrogradely-labelled TIB neurons that grew axons into the nerve stump within three months, increased with the size of the perineurial windows opened in the TIB and CP nerves. Equal numbers of donor TIB axons regenerated into CP stumps either side of the cross-bridges, not being affected by target neurotrophic effects, or by removing the perineurium to insert 5-9 cross-bridges. Second, CP nerve stumps were coapted three months after inserting 0-9 cross-bridges and the number of 1) CP neurons that regenerated their axons within three months or 2) CP motor nerves that reinnervated the extensor digitorum longus (EDL) muscle within five months was determined by counting and motor unit number estimation (MUNE), respectively. We found that three but not more cross-bridges promoted the regeneration of axons and reinnervation of EDL muscle by all the CP motoneurons as compared to only 33% regenerating their axons when no cross-bridges were inserted. The same 3-fold increase in sensory nerve regeneration was found. In conclusion, side-to-side cross-bridges ameliorate poor regeneration after delayed nerve repair possibly by sustaining the growth-permissive state of denervated nerve stumps. Such autografts may be used in human repair surgery to improve outcomes after unavoidable delays.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".