The effect of glutamine on locomotor performance and skeletal muscle myosins following spinal cord injury in rats
Bibliographic record
Abstract
Following initial spinal cord injury (SCI), a cascade of pathological events, including oxidative stress, leads to secondary injury. Glutathione (GSH) plays a critical role in oxidant scavenging. Maintenance of GSH concentrations after SCI lessens secondary injury and improves recovery. Since glutamine promotes GSH synthesis, this nonessential amino acid was examined for therapeutic potential. Denervation alters the expression of myosin heavy chain (MHC) isoforms within skeletal muscles. The hypotheses of this study were that glutamine administration to SCI rats would lead to improved functional recovery and more preserved MHC phenotypes in representative locomotor muscles. Male Wistar rats were divided into four groups: healthy, sham with laminectomy, laminectomized SCI untreated, and laminectomized SCI treated with glutamine. Functional performance was measured weekly for 6 wk using Basso-Beattie-Bresnahan scale and angle board methods. MHC composition of rat soleus and extensor digitorum longus muscles was determined using SDS-PAGE. Glutamine-treated rats had significantly higher angle board scores (P < 0.001) and Basso-Beattie-Bresnahan scores (P < 0.01) than untreated SCI rats. Soleus of healthy rats contained 94% type 1 myosin isoform. Treated rats maintained 68%, which was significantly (P < 0.001) greater than 28% in untreated rats. The extensor digitorum longus of healthy rats contained 55% type 2b myosin. There was a significant (P < 0.001) decrease in this isoform following SCI, but no significant difference between treated and untreated groups. There were strong correlations between higher functional scores and more preserved MHC phenotypes. Our findings suggest glutamine improves functional recovery and helps preserve myosin profile by reducing secondary SCI, thereby maintaining more nerves.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".