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.
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) | 0.000 | 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) | 0.001 | 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".