Sensory Nerve Cross-Anastomosis and Electrical Muscle Stimulation Synergistically Enhance Functional Recovery of Chronically Denervated Muscle
Bibliographic record
Abstract
BACKGROUND: Long-term muscle denervation leads to severe and irreversible atrophy coupled with loss of force and motor function. These factors contribute to poor functional recovery following delayed reinnervation. The authors' previous work demonstrated that temporarily suturing a sensory nerve to the distal motor stump (called sensory protection) significantly reduces muscle atrophy and improves function following reinnervation. The authors have also shown that 1 month of electrical stimulation of denervated muscle significantly improves function and reduces atrophy. In this study, the authors tested whether a combination of sensory protection and electrical stimulation would enhance functional recovery more than either treatment alone. METHODS: Rat gastrocnemius muscles were denervated by cutting the tibial nerve. The peroneal nerve was then sutured to the distal tibial stump following 3 months of treatment (i.e., electrical stimulation, sensory protection, or both). Three months after peroneal repair, functional and histologic measurements were taken. RESULTS: All treatment groups had significantly higher muscle weight (p<0.05) and twitch force (p<0.001) compared with the untreated group (denervated), but fiber type composition did not differ between groups. Importantly, muscle weight and force were significantly greater in the combined treatment group (p<0.05) compared with stimulation or sensory protection alone. The combined treatment also produced motor unit counts significantly greater than sensory protection alone (p<0.05). CONCLUSIONS: The combination treatment synergistically reduces atrophy and improves reinnervation and functional measures following delayed nerve repair, suggesting that these approaches work through different mechanisms. The authors' research supports the clinical use of both modalities together following 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) | 0.001 | 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".