ROLE OF CHRONIC SCHWANN CELL DENERVATION IN POOR FUNCTIONAL RECOVERY AFTER NERVE INJURIES AND EXPERIMENTAL STRATEGIES TO COMBAT IT
Bibliographic record
Abstract
OBJECTIVE: To present our data about the role of chronic denervation (CD) of the distal nerve stumps as compared with muscle denervation atrophy and experimental strategies to promote better functional recovery. METHODS: A rat model of nerve injury and repair was used. The common peroneal branch of the sciatic nerve was subjected to 0 to 24 weeks of CD before cross-suture with the tibial motoneurons. Our outcome measures included the numbers of motoneurons that regenerated their axons and the numbers that reinnervated muscle targets (motor units). To overcome the effects of CD, we used subcutaneous injection of FK506 and in vitro reactivation of Schwann cells that had been subjected to 24 weeks of CD with transforming growth factor beta. RESULTS: Numbers of regenerated motoneurons and reinnervated motor units decreased as a function of duration of CD. However, axons that regenerated through the distal nerve stumps reinnervated the muscle targets and even formed enlarged motor unit size regardless of the duration of CD. FK506 doubled the numbers of tibial motoneurons that regenerated their axons into the common peroneal nerve even after delayed repair. Reactivation of chronically denervated Schwann cells with transforming growth factor beta significantly increased their capacity to support axonal regeneration. CONCLUSION: CD of the distal nerve stumps is the primary factor that results in poor axonal regeneration and subsequently poor functional recovery. Acceleration of the rate of axonal regeneration and/or reactivation of Schwann cells of the distal nerve stumps are effective experimental strategies to promote axonal regeneration and functional recovery.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".