Repair of nerve defect with chitosan graft supplemented by uncultured characterized stromal vascular fraction in streptozotocin induced diabetic rats
Bibliographic record
Abstract
Regenerative properties of stem cells at the service of nerve repair have been initiated during recent decades. Effects of transplantation of characterized uncultured stromal vascular fraction (SVF) on peripheral nerve regeneration were studied using a rat sciatic nerve transection model. A 10-mm sciatic nerve defect was bridged using a chitosan conduit filled with SVF. In control group, chitosan conduit was filled with phosphate-buffered saline alone. The regenerated nerve fibers were studied 4 weeks, 8 weeks, and 12 weeks after surgery. In sham-operated group, the sciatic nerve was only exposed and manipulated. Behavioral and Functional studies confirmed faster recovery of regenerated axons in SVF transplanted animals than in control group (P < 0.05). Gastrocnemius muscle mass in SVF transplanted animals was found to be significantly more than that in control group. Morphometric indices of the regenerated fibers showed the number and diameter of the myelinated fibers were significantly higher in SVF transplanted animals than in control group. In immunohistochemistry, location of reactions to S-100 in SVF transplanted animals was clearly more positive than that in control group. SVF transplantation combined with chitosan conduit could be considered as a readily accessible source of stromal cells that improve functional recovery 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 | 0.001 | 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 | 0.001 |
| 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".