NGF, BDNF, NT-3, and GDNF mRNA Expression in Rat Skeletal Muscle following Denervation and Sensory Protection
Bibliographic record
Abstract
Poor muscle and nerve functional recovery after nerve damage is a serious clinical problem, particularly if there is prolonged delay before nerve-muscle contact is reestablished. Our previous studies showed that sensory nerve cross-anastomosis (sensory protection) provides support to the denervated muscle. In the present study, we analyzed neurotrophic factor mRNA expression by RT-PCR in denervated rat gastrocnemius muscle receiving sensory protection with the saphenous nerve, compared to normal innervated muscle, to denervated muscle, and to denervated muscle repaired immediately with the peroneal (motor) nerve, after periods of 3 days to 3 months. No significant differences in mRNA levels of beta-actin, nerve growth factor, brain-derived neurotrophic factor or neurotrophin-3 were found between the sensory protection treatment and the denervated or the motor repair groups. However, sensory protection resulted in levels of muscle glial cell line-derived neurotrophic factor mRNA expression that were lower than in denervated muscle and higher than in muscle given immediate motor repair. These results demonstrate that glial cell line-derived neurotrophic factor mRNA is elevated following denervation but is partially down-regulated by sensory protection. Our study suggests that sensory protection provides a modified trophic environment by modulating neurotrophic factor synthesis in muscle.
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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 | 0.001 | 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".