Increased Neuromuscular Activity Reduces Sprouting in Partially Denervated Muscles
Bibliographic record
Abstract
The effects of increasing neural activity on sprouting remain unclear and controversial. In a rat model of partial denervation of skeletal muscles, we investigated the effect of neuromuscular activity on sprouting. Rat hindlimb muscles were partially denervated by avulsion of either L4 or L5 spinal root. Immediately after partial denervation, the rats were divided into three groups: (1) normal caged activity, (2) running exercise on wheels, 8 hr daily, and (3) functional electrical stimulation (FES) of sciatic nerves, 20 Hz for 8 hr daily. At 1 month, muscle unit (MU) enlargement was quantitated electrophysiologically and histochemically. MU twitch force was increased by four- to fivefold by partial denervation in extensively denervated tibialis anterior (TA) and medial gastrocnemius (MG) and by approximately twofold in moderately denervated plantaris (PL) and soleus (SOL). For the extensively denervated TA and MG muscles, MU enlargement, measured electrophysiologically, declined significantly after an average of 1757 +/- 310 m/d running exercise and daily FES for 1 month. The detrimental effects on MU enlargement were much less but significant in the moderately denervated PL and did not reach statistical significance in the moderately denervated SOL muscle. Histochemical evaluation of sprouting showed a reduction in the number of sprouts in the extensively denervated TA muscle, but not the moderately denervated PL and SOL muscles, by increased neuromuscular activity. Thus, increased neuromuscular activity is detrimental primarily in muscles that are extensively denervated, and the MUs are smaller than under conditions in which the muscles experience normal physiological levels of activation.
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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) | 0.001 | 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".