Sensory recovery after cell therapy in peripheral nerve repair: effects of naïve and skin precursor-derived Schwann cells
Bibliographic record
Abstract
OBJECT: Cell therapy is a promising candidate among biological or technological innovations sought to augment microsurgical techniques in peripheral nerve repair. This report describes long-term functional regenerative effects of cell therapy in the rat injury model with a focus on sensory recovery. METHODS: Schwann cells were derived from isogenic nerve or skin precursor cells and injected into the transected and immediately repaired sciatic nerve distal to the injury site. Sensory recovery was assessed at weeks 4, 7, and 10. Axonal regeneration was assessed at Week 11. RESULTS: By Week 10, thermal sensitivity in cell therapy groups returned to a level indistinguishable from the baseline (p > 0.05). Immunohistochemistry at 11 weeks after injury showed improved regeneration of NF+ and IB4+ axons. CONCLUSIONS: The results of this study show that cell therapy significantly improves thermal sensation and the number of regenerated sensory neurons at 11 weeks after injury. These findings contribute to the view of skin-derived stem cells as a reliable source of Schwann cells with therapeutic potential for functional recovery in damaged peripheral nerve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".