Transcutaneous and Direct Electrical Stimulation of Mouse Sciatic Nerve Accelerates Functional Recovery After Nerve Transection and Immediate Repair
Bibliographic record
Abstract
BACKGROUND: Electrical stimulation can accelerate peripheral nerve regeneration after injury and repair. Clinically, direct electrical stimulation (DES) may involve longer operating times, increasing risks of perioperative complications. Transcutaneous electrical stimulation (TCES) is a noninvasive alternative. In this study, we investigate how transcutaneous and DES compare for accelerating functional nerve recovery in a mouse sciatic nerve model. METHODS: Twenty-eight mice were divided into sham (n = 4), axotomy (n = 8), DES (n = 8), and TCES (n = 8) groups. After sciatic nerve transection and repair, the proximal nerve was subjected to DES or TCES at 20 Hz for 1 hour. Sciatic functional index was measured before the injury, and at weeks 1, 2, 4, 6, 8, 10, and 12 by walking-track analysis. Electrophysiological measures were taken at week 12. RESULTS: Kinematic studies showed significant improvement from the 8th week to the 12th week for both electrical stimulation groups compared with the axotomy group (P < 0.05), with no difference between the electrical stimulation groups. At the 12th week, both DES and TCES groups had significantly faster average conduction velocity than the axotomy group. CONCLUSIONS: Functional recovery was significantly better from 8 weeks onward in mice receiving either DES or TCES stimulation when compared with axotomy and repair alone. Transcutaneous electrical stimulation is a minimally invasive alternative treatment for accelerating functional recovery after peripheral nerve injury.
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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) | 0.002 | 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".