Topically-administered acetyl-L-carnitine increases sciatic nerve regeneration and improves functional recovery after tubulization of transected short nerve gaps
Bibliographic record
Abstract
BACKGROUND: Peripheral nerve injuries repair is still among the most challenging and concern-raising tasks in neurosurgery. The effect of an acetyl-L-carnitin-loaded silicone tube as an in-situ delivery system in defects bridging was studied using a rat sciatic nerve regeneration model. METHODS: A 10-mm sciatic nerve defect was bridged using a silicone tube (SIL/ALC) filled with 10 µL acetyl-L-carnitine (100 ng/mL). In the control group (SIL), the tube was filled with the same volume of the phosphate-buffered solution. The regenerated fibers were studied 4, 8, 12 and 16 weeks after surgery. RESULTS: The functional study confirmed faster recovery of the regenerated axons in acetyl-L-carnitine treated than control group (P<0.05). The mean ratios of gastrocnemius muscles weight were measured. There was a statistically significant difference between the muscle weight ratios of SIL/ALC and SIL groups (P<0.05). Morphometric indices of regenerated fibers showed that the number and diameter of the myelinated fibers in SIL/ALC were significantly higher than in the control group. In immuohistochemistry, the location of reactions to S-100 in the SIL/ALC group was clearly more positive than in the SIL group. CONCLUSIONS: Acetyl-L-carnitine, when loaded in a silicone tube, can bring to an improvement in functional recovery and quantitative morphometric indices of sciatic nerve.
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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".