Long‐term changes in neurotrophic factor expression in distal nerve stump following denervation and reinnervation with motor or sensory nerve
Bibliographic record
Abstract
Several factors have been proposed to account for poor motor recovery after prolonged denervation, including motor neuron cell death and incomplete or poor regeneration of motor fibers into the muscle. Both may result from failure of the muscle and the distal motor nerve stump to continue expression of neurotrophic factors following delayed muscle reinnervation. This study investigated whether regenerating motor or sensory axons modulate distal nerve neurotrophic factor expression. We found that transected distal tibial nerve up-regulated brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF) mRNA, down-regulated neurotrophin-3 and ciliary neurotrophic factor mRNA, and that although these levels returned to normal with regeneration, the chronically denervated distal nerve stump continued to express these neurotrophic factors for at least 6 months following injury. A sensory nerve (the cutaneous saphenous nerve) sutured to distal tibial nerve lowered injury-induced BDNF and GDNF mRNA levels in distal stump, but repair with a mixed nerve (peroneal, containing muscle and cutaneous axons) was more effective. Repair with sensory or mixed nerves did not affect nerve growth factor or neurotrophin-3 expression. Thus, distal nerve contributed to a neurotrophic environment for nerve regeneration for at least 6 months, and sensory nerve repair helped normalize distal nerve neurotrophic factor mRNA expression following denervation. Furthermore, as BDNF and GDNF levels in distal stump increased following denervation and returned to control levels following reinnervation, their levels serve as markers for the status of regeneration by either motor or sensory nerve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".