Effects of short- and long-term Schwann cell denervation on peripheral nerve regeneration, myelination, and size
Bibliographic record
Abstract
Poor functional recovery after peripheral nerve injury has been generally attributed to inability of denervated muscles to accept reinnervation and recover from denervation atrophy. However, deterioration of the Schwann cell environment may play a more vital role. This study was undertaken to evaluate the effects of chronic denervation on the capacity of Schwann cells in the distal nerve stump to support axonal regeneration and to remyelinate regenerated axons. We used a delayed cross-suture anastomosis technique in which the common peroneal (CP) nerve in the rat was denervated for 0-24 weeks before cross-suture of the freshly axotomized tibial (TIB) and chronically denervated CP nerve stumps. Motor neurons were backlabeled with either fluoro-ruby or fluorogold 12 months later, to identify and count TIB motor neurons that regenerated axons into chronically denervated CP nerve stumps. Number, size, and myelination of regenerated sensory and motor axons were determined using light and electron microscopy. We found that short-term denervation of < or =4 weeks did not affect axonal regeneration but more prolonged denervation profoundly reduced the numbers of backlabeled motor neurons and axons in the distal nerve stump. Yet, atrophic Schwann cells retained their capacity to remyelinate regenerated axons. In fact, the axons were larger and well myelinated by long-term chronically denervated Schwann cells. These findings demonstrate a progressive inability of chronically denervated Schwann cells to support axonal regeneration and yet a sustained capacity to remyelinate the axons which do regenerate. Thus, axonal interaction can effectively switch the nonmyelinating phenotype of atrophic Schwann cells back into the myelinating phenotype.
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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 | 0.001 |
| 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".