Use of Chitosan Conduit for Bridging Small-Gap Peripheral Nerve Defect in Sciatic Nerve Transection Model of Rat
Bibliographic record
Abstract
Objective-To evaluate effect of chitosan conduit for peripheral nerve regeneration using sciatic nerve transection model in rat Design- Experimental in vivo study. Animals- Sixty healthy male Wistar rats. Procedures-The rats were divided into four experimental groups (n=15) randomly. In sham group the left sciatic nerve was exposed through a gluteal muscle incision and after careful homeostasis the wound was sutured. In transected control group the left sciatic nerve was exposed the same way, transected proximal to the tibio-peroneal bifurcation leaving a 10 mm gap and the nerve ends were sutured to the adjacent muscles. In silicone or chitosan groups the left sciatic nerve was transected the same way and proximal and distal stumps were each inserted into a silicone or chitosan tube. Each group was further subdivided into three subgroups of five animals each and were studied 4, 8, 12 weeks post operatively. Results- Functional and electrophysiological analyses showed significant improvement of nerve function in chitosan than in silicone group (P < 0.05). Morphometric indices and immuohistochemistry indicated that there were significant differences (P < 0.05) between chitosan and silicone with transected control groups 12 weeks after surgery. Conclusion and Clinical Relevance- Chitosan conduit could be considered clinically as an effective biodegradable tube for peripheral nerve regeneration in the least harmful way that is available, easily performed and affordable. It also averts the need for foreign materials that are likely to provoke a foreign body reaction.
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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) | 0.000 | 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 | 0.001 | 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".