Growth factor and stem cell enhanced conduits in peripheral nerve regeneration and repair
Bibliographic record
Abstract
OBJECTIVE: Despite the capacity for spontaneous axonal regeneration, recovery after severe peripheral nerve injury remains variable and often very poor. In addition, autologous nerve grafts, considered to be the 'gold standard' in nerve repair technique, are plagued by restricted donor tissue availability and donor site morbidity. Our primary objective is to highlight new and emerging methods of nerve repair, which have the potential to significantly improve both the functional and behavioral outcome after clinical nerve injury. METHODS: A critical analysis of nerve injury and regeneration literature concentrating on outcome measures from both immediate and chronically denervated experimental works was conducted. RESULTS: Results of numerous works employing both growth factor and stem cell enhanced nerve guidance conduits have shown encouraging results. However, further research is needed to optimize guidance conduit dynamics, bioavailability and delivery of both growth factors and stem cells to enhance peripheral nerve regeneration and functional recovery. DISCUSSION: This review discusses current animal and clinical growth factor and stem cell studies, specifically focusing on future bio-engineering approaches in developing a nerve guidance conduit in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".