Brief Transvertebral Electrical Stimulation of the Spinal Cord Improves the Specificity of Femoral Nerve Reinnervation
Bibliographic record
Abstract
BACKGROUND: Functional outcomes are generally poor following peripheral nerve injury (PNI). The reason is multifactorial but includes the misdirection of regenerating axons to inappropriate end organs. It has been shown that brief electrical stimulation (Estim) of nerves has the potential to improve the accuracy and rate of peripheral axon regeneration. OBJECTIVE: The present study explores a novel percutaneous transvertebral approach to Estim, which was tested in the mouse femoral nerve model. METHODS: Inspired by the protocol of Gordon and colleagues (ie, 20 Hz, for 1 hour), we applied Estim to the cervicothoracic spinal cord (SC-Estim) to remotely activate lumbar motor neurons following transection and repair of the femoral nerve. Fluorescent dyes were applied to the distal nerve to label reinnervating cells. Sections of nerve were taken to quantify the numbers of reinnervating axons as well as to stain for a known femoral axon guidance molecule-polysialylated neural cell adhesion molecule (PSA-NCAM). RESULTS: In comparison to sham treatment, SC-Estim led to significantly greater expression of PSA-NCAM as well as improved the specificity of motor reinnervation. Interestingly, although SC-Estim did not alter the number of early reinnervating (ie, pioneer) axons, there was a reduction in the number of retrogradely labeled neurons at 2 weeks postrepair. However, by 6 weeks postrepair, there was no difference in the number of neurons that had reinnervated the femoral nerve. CONCLUSIONS: The present findings support the development of SC-Estim as a novel approach to enhance the specificity of reinnervation and potentially improve functional outcomes following PNI.
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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 | 0.001 |
| 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".