Resistance Exercise–induced Changes in Muscle Phenotype Are Load Dependent
Bibliographic record
Abstract
INTRODUCTION: Lower-load (LL), higher-repetition resistance exercise training (RET) can increase muscle mass in a similar degree as higher-load (HL), lower-repetition RET. However, little is known about how LL and HL RET modulate other aspects of the RET phenotype such as satellite cells, myonuclei, and mitochondrial proteins. We aimed to investigate changes in muscle mass, muscle strength, satellite cell activity, myonuclear addition, and mitochondrial protein content after prolonged RET with LL and HL RET. METHODS: We recruited 21 young men and randomly assigned them to perform 10 wk RET (leg press, leg extension, and leg curl) three times per week with the following conditions: 80FAIL (80% one-repetition maximum [1RM] performed to volitional fatigue), 30WM (30%1RM with volume matched to 80FAIL), and 30FAIL (30%1RM to volitional fatigue). Skeletal muscle biopsies were taken from the vastus lateralis pre- and post-RET intervention. RESULTS: After 10 wk of RET, only 30FAIL and 80FAIL showed an increase in peak torque and type I fiber cross-sectional area (P < 0.05). Moreover, only 30FAIL resulted in a significant decrease in the myonuclear domain of type II muscle fibers and an increase in mitochondrial proteins related to autophagy, fission, and fusion (all P < 0.05). CONCLUSION: We discovered that LL RET was effective at increasing the content of several mitochondrial proteins. Similar to previous research, we found that changes in muscle mass and strength were independent of load when repetitions were performed to volitional fatigue.
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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 | 0.000 | 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) | 0.003 | 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".