Investigation of Skeletal Muscle Denervation and Reinnervation Using Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
OBJECTIVE: To determine changes in skeletal muscle metabolism after nerve transection and repair and to correlate metabolic changes with functional recovery. STUDY DESIGN AND SETTING: Male Wistar rats were divided into 6 experimental groups plus a control group. The posterior tibial nerve was transected and reapproximated. At varying times after surgery (1, 2, 4, 6, or 8 weeks) animals were sacrificed, the gastrocnemius muscle was harvested, and proton nuclear magnetic resonance (NMR) spectroscopy was performed. Functional recovery was measured using the sciatic function index. RESULTS: Animals undergoing nerve repair all showed functional recovery whereas the nonrepaired nerve group did not. Concentration of glucose and lactate increased after denervation and then returned toward normal. Choline concentration decreased and then returned toward normal. In animals not undergoing nerve repair, the metabolic abnormalities persisted and showed no sign of recovery over the 8-week observation period. CONCLUSIONS: 1H NMR spectroscopy is a potentially useful tool to study changes in skeletal muscle metabolism after motor nerve injury. SIGNIFICANCE: NMR spectroscopy is rapidly developing into a clinically useful tool. High-field magnets have improved resolution and data acquisition. Basic experiments, such as those described here, will help guide the use of NMR spectroscopy in clinical medicine and will also lead to a better understanding of basic mechanisms of nerve injury and repair.
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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 | 0.000 |
| 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".