Effects of topically administered FK506 on sciatic nerve regeneration and reinnervation after vein graft repair of short nerve gaps
Bibliographic record
Abstract
OBJECT: Despite the development of various nerve coaptation materials and techniques, achievement of desired functional peripheral nerve regeneration is still inadequate, and repair of peripheral nerve injuries is still one of the most challenging tasks and concerns in neurosurgery. The effect of an FK506-loaded vein graft as an in situ delivery system for FK506 in bridging the defects was studied using a rat sciatic nerve regeneration model. METHODS: A 10-mm sciatic nerve defect was bridged using an inside-out vein graft (IOVG) filled with 10 μl of a carrier-drug dilution (10 ng/ml FK506) in the IOVG/FK506 group. In the IOVG control group, the vein was filled with the same volume of carrier dilution alone. The regenerated fibers were studied 4, 8, and 12 weeks after surgery. RESULTS: Functional study confirmed faster recovery of the regenerated axons in the IOVG/FK506 group than in the IOVG group (p < 0.05). There was a statistically significant difference between the mean gastrocnemius muscle weight ratios of the IOVG/FK506 and IOVG control groups (p < 0.05). Morphometric indices of regenerated fibers showed that the number and diameter of the myelinated fibers were significantly higher in the IOVG/FK506 group than in the IOVG control group. Immunohistochemical analysis showed more positive immunoreactivity to S100 protein in the IOVG/FK506 group than in the IOVG control group. CONCLUSIONS: When loaded in a vein graft, FK506 resulted in improvement of functional recovery and quantitative morphometric indices of sciatic nerve. Topical application of this readily available agent offers the benefit of cost savings as well as avoiding the complications associated with systemic administration.
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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".