Strategies to promote peripheral nerve regeneration: electrical stimulation and/or exercise
Bibliographic record
Abstract
Enhancing the regeneration of axons is often considered to be a therapeutic target for improving functional recovery after peripheral nerve injury. In this review, the evidence for the efficacy of electrical stimulation (ES), daily exercise and their combination in promoting nerve regeneration after peripheral nerve injuries in both animal models and in human patients is explored. The rationale, effectiveness and molecular basis of ES and exercise in accelerating axon outgrowth are reviewed. In comparing the effects of ES and exercise in enhancing axon regeneration, increased neural activity, neurotrophins and androgens are considered to be common requirements. Similarly, there are sex-specific requirements for exercise to enhance axon regeneration in the periphery and for sustaining synaptic inputs onto injured motoneurons. ES promotes nerve regeneration after delayed nerve repair in humans and rats. The effectiveness of exercise is less clear. Although ES, but not exercise, results in a significant misdirection of regenerating motor axons to reinnervate different muscle targets, the loss of neuromuscular specificity encountered has only a very small impact on resulting functional recovery. Both ES and exercise are promising experimental treatments for peripheral nerve injury that seem to be ready to be translated to clinical use.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".