Side-To-Side Nerve Bridges Support Donor Axon Regeneration Into Chronically Denervated Nerves and Are Associated With Characteristic Changes in Schwann Cell Phenotype
Bibliographic record
Abstract
BACKGROUND: Chronic denervation resulting from long nerve regeneration times and distances contributes greatly to suboptimal outcomes following nerve injuries. Recent studies showed that multiple nerve grafts inserted between an intact donor nerve and a denervated distal recipient nerve stump (termed "side-to-side nerve bridges") enhanced regeneration after delayed nerve repair. OBJECTIVE: To examine the cellular aspects of axon growth across these bridges to explore the "protective" mechanism of donor axons on chronically denervated Schwann cells. METHODS: In Sprague Dawley rats, 3 side-to-side nerve bridges were placed over a 10-mm distance between an intact donor tibial (TIB) nerve and a recipient denervated common peroneal (CP) distal nerve stump. Green fluorescent protein-expressing TIB axons grew across the bridges and were counted in cross section after 4 weeks. Immunofluorescent axons and Schwann cells were imaged over a 4-month period. RESULTS: Denervated Schwann cells dedifferentiated to a proliferative, nonmyelinating phenotype within the bridges and the recipient denervated CP nerve stump. As donor TIB axons grew across the 3 side-to-side nerve bridges and into the denervated CP nerve, the Schwann cells redifferentiated to the myelinating phenotype. Bridge placement led to an increased mass of hind limb anterior compartment muscles after 4 months of denervation compared with muscles whose CP nerve was not "protected" by bridges. CONCLUSION: This study describes patterns of donor axon regeneration and myelination in the denervated recipient nerve stump and supports a mechanism where these donor axons sustain a proregenerative state to prevent deterioration in the face of chronic denervation.
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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 | 0.000 |
| 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".