Local Polyethylene Glycol in Combination with Chitosan Based Hybrid Nanofiber Conduit Accelerates Transected Peripheral Nerve Regeneration
Bibliographic record
Abstract
OBJECTIVE: The incapability to promptly improve behavioral function after discontinuation of peripheral nerves is a current problem in clinical practice. Effect of local polyethylene glycol in combination with chitosan-based hybrid nanofiber conduit was assessed. STUDY DESIGN: A 10-mm sciatic nerve defect was bridged using a chitosan-based hybrid nanofiber conduit (Chitosan) filled with phosphate-buffered saline. In authograft group (AUTO), a segment of sciatic nerve was transected and reimplanted reversely. In polyethylene glycol-treated group (CHIT/PEG), the conduit was filled with polyethylene glycol solution. The regenerated fibers were studied within 12 weeks after surgery. RESULTS: The behavioral and functional tests confirmed faster recovery of the regenerated axons in PEG-treated group compared to Chitosan group (p < .05). The mean ratios of gastrocnemius muscles weight were measured. There was statistically significant difference between the muscle weight ratios of CHIT/PEG and Chitosan groups (p < .05). Morphometric indices of regenerated fibers showed number and diameter of the myelinated fibers were significantly higher in CHIT/PEG than in Chitosan. In immuohistochemistry, the location of reactions to S-100 in CHIT/PEG was clearly more positive than Chitosan group. CONCLUSION: polyethylene glycol solution when loaded in a chitosan-based hybrid nanofiber conduit resulted in acceleration of 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".