Inferior Reinnervation of Reverse End-to-Side Nerve Transfer in a Delayed Nerve Repair Rat Model
Bibliographic record
Abstract
Abstract Objective Reverse end-to-side (RETS) nerve transfer is a recent surgical technique to augment injured nerve function by supplying a dispensable donor motor nerve to the side of the distal injured nerve. Although clinical studies have suggested advantages of RETS transfer for upper extremity repairs, uncertainties remain regarding its underlying mechanism. Furthermore, our recent clinical studies using electrophysiological examinations revealed no contribution from the donor nerve. Given that most experimental studies were conducted on acutely injured nerves, our objective is to 1) reassess the effectiveness RETS nerve transfer in a rat model of chronic nerve injury and repair; 2) investigate the potential nerve-babysitting effect; and 3) investigate how availability of regenerating tracks, i.e. bands of Büngner, of recipient nerve affects donor nerve regeneration. Methods Obturator and femoral nerve were used as donor and recipient nerves, respectively. Electromyogram, retrograde labeling of regenerated motoneurons and neuromuscular junction (NMJ) formation were used to compare regenerative ability of donor nerve in acute and delayed RETS transfer where the femoral nerve in the latter group was injured by double ligations 8 weeks prior. Nerve-babysitting effect on injured nerve was investigated by 1) no intervention; 2) perineurial window creation; and 3) RETS transfer to femoral nerve in delayed repair model. The effects of availability of regeneration tracks were investigated by severing proximal femoral nerve, allowing complete denervation compared partial denervation in double ligations, followed by acute and delayed repairs. Results EMG and motoneuron quantification confirmed inferiority of donor nerve regeneration into recipient nerve in delayed RETS transfer compared to acute repair, yet donor axons reached target muscle and formed NMJs in both conditions. Same functional assessments revealed nerve baby-sitting effects did not significantly contribute to repair success but availability of regeneration tracks in the recipient nerve may influence the final outcomes. Conclusions Our study offered insights into the effectiveness of RETS nerve transfer in clinically relevant settings, underscoring the compounded impact of delayed intervention and native nerve regeneration which both negatively affect the efficacy of RETS nerve transfer.
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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 | 0.000 |
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".