Effect of Acute Low-load High-repetition Resistance Exercise on Protein Synthetic Signaling Pathway and Satellite Cell Activation in Skeletal Muscle of Rats
Bibliographic record
Abstract
PURPOSE: Previous studies have reported that low-load high-repetition (LLHR) resistance exercise can induce muscle hypertrophy to a similar extent as high-load low-repetition (HLLR). However, it is unclear which mechanical stress, such as exercise intensity, contraction number, and/or total work volume and which molecular mechanisms underpin the greater relative muscle hypertrophy after LLHR exercise.METHODS: Sprague-Dawley rats (12 weeks, N=35) were randomly allocated into control (Con, n=7), low-load low-repetition (LLLR, n=7), low-load medium-repetition (LLMR, n=7), LLHR (n=7), and HLLR (n=7) resistance exercise groups. They performed a ladder climbing exercise to evaluate the effect of exercise intensity, contraction number and work volume on the activation of satellite cells and protein synthesis signaling pathway.RESULTS: The number of active satellite cells and the phosphorylation of mTOR, p70S6K, ERK, and p38 were increased after LLHR and HLLR exercise (p<.005). However, phosphorylation of 4E-BP1 was only increased after the LLHR exercise (p<.05).CONCLUSIONS: Acute low load exercise accompanied by high total work volume may activate muscle protein synthesis mechanisms for hypertrophy similar to high load resistance exercise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".