Brief Electrical Stimulation Promotes the Speed and Accuracy of Motor Axonal Regeneration
Bibliographic record
Abstract
Functional recovery is often poor despite the capacity for axonal regeneration in the peripheral nervous system and advances in microsurgical technique. Regeneration of axons in mixed nerve into inappropriate pathways is a major contributing factor to this failure. In this study, we use the rat femoral nerve model of transection and surgical repair to evaluate (1) the effect of nerve transection on the speed of regeneration and the generation of motor-sensory specificity, (2) the efficacy of electrical stimulation in accelerating axonal regeneration and promoting the reinnervation of appropriate muscle pathways by femoral motor nerves, and (3) the mechanism of action of electrical stimulation. Using the retrograde neurotracers fluorogold and fluororuby to backlabel motoneurons that regenerate axons into muscle and cutaneous pathways, we found the following. (1) There is a very protracted period (10 weeks) of axonal outgrowth that adds substantially to the delay in axonal regeneration (staggered regeneration). This process of staggered regeneration is associated with preferential motor reinnervation (PMR). (2) One hour to 2 weeks of 20 Hz continuous electrical stimulation of the parent axons proximal to the repair site dramatically reduces this period (to 3 weeks) and accelerates PMR. (3) The positive effect of short-term electrical stimulation is mediated via the cell body, implicating an enhanced growth program. The effectiveness of such a short-period low-frequency electrical stimulation suggests a new therapeutic approach to accelerate nerve regeneration after injury and, in turn, improve functional recovery.
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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) | 0.000 | 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 | 0.000 |
| 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".