Augmenting nerve regeneration with electrical stimulation
Bibliographic record
Abstract
OBJECTIVE: Poor functional recovery after peripheral nerve injury is generally attributed to irreversible target atrophy. In rats, we addressed the functional outcomes of prolonged neuronal separation from targets (chronic axotomy for up to 1 year) and atrophy of Schwann cells (SCs) in distal nerve stumps, and whether electrical stimulation (ES) accelerates axon regeneration. In carpal tunnel syndrome (CTS) patients with severe axon degeneration and release surgery, we asked whether ES accelerates muscle reinnervation. METHODS: Reinnervated motor unit (MUs) and regenerating neuron numbers were counted electrophysiologically and with dye-labeling after chronic axotomy, chronic SC denervation and after immediate nerve repair with and without trains of 20 Hz ES for 1 hour to 2 weeks in rats and in CTS patients. RESULTS: Chronic axotomy reduced regenerative capacity to 67% and was alleviated by exogenous growth factors. Reduced regeneration to approximately 10% by SC denervation atrophy was ameliorated by forskolin and transforming growth factor-beta SC reactivation. ES (1 h) accelerated axon outgrowth across the suture site in association with elevated neuronal neurotrophic factor and receptors and in patients, promoted the full reinnervation of thenar muscles in contrast to a non-significant increase in MU numbers in the control group. DISCUSSION: The rate limiting process of axon outgrowth, progressive deterioration of both neuronal growth capacity and SC support, but not irreversible target atrophy, account for observed poor functional recovery after nerve injury. Brief ES accelerates axon outgrowth and target muscle reinnervation in animals and humans, opening the way to future clinical application to promote 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) | 0.002 | 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".