Alpha-lipoic acid loaded in chitosan conduit enhances sciatic nerve regeneration in rat.
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of topical administration of alpha-lipoic acid into chitosan conduit on peripheral nerve regeneration using a rat sciatic nerve transection model. MATERIALS AND METHODS: Forty five Wistar rats were divided into three experimental groups randomly. A 10-mm gap of sciatic nerve was bridged with a chitosan conduit following surgical preparation and anesthesia. In treatment group, the conduit was filled with 30 µl alpha-lipoic acid (10 mg/kg/bw).It was filled with 30 µl phosphate buffered saline solution in control group. In Sham group sciatic nerve was just exposed. RESULTS: The recovery of nerve function was faster in treatment group than in control, at 4 and 8 weeks after surgery (P-value<0.05). Conduction velocity was better in treatment group than in control group at 4 and 12 weeks (P-value<0.05). Recovery index was higher in treatment group than the control group, 8 weeks after surgery (P-value <0.05). Greater nerve fiber diameter, axon diameter, and myelin sheath thickness were observed in treatment group compared to control group at 8 and 12 weeks after surgery (P-value<0.05). The immunoreactivity of regenerated axons and myelin sheath in treatment group were far more similar to sham group. CONCLUSION: Alpha-lipoic acid when loaded in a chitosan conduit could improve transected sciatic nerve regeneration in rat.
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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) | 0.001 | 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".