Resistance training variable manipulations are less relevant than intrinsic biology in affecting muscle fiber hypertrophy
Bibliographic record
Abstract
We aimed to investigate whether muscle fiber cross-sectional area (fCSA) and associated molecular processes could be differently affected at the group and individual level by manipulating resistance training (RT) variables. Twenty resistance-trained subjects had each leg randomly allocated to either a standard RT (RT-CON: without specific variables manipulations) or a variable RT (RT-VAR: manipulation of load, volume, muscle action, and rest interval at each RT session). Muscle fCSA, satellite cell (SC) pool, myonuclei content, and gene expression were assessed before and after training (chronic effect). Gene expression was assessed 24 h after the last training session (acute effect). RT-CON and RT-VAR increased fCSA and myonuclei domain in type I and II fibers after training (p < 0.05). SC and myonuclei content did not change for both conditions (p > 0.05). Pax-7, MyoD, MMP-2 and COL3A1 (chronic) and MGF, Pax-7, and MMP-9 (acute) increased similar for RT-CON and RT-VAR (p < 0.05). The increase in acute MyoG expression was significantly higher for the RT-VAR than RT-CON (p < 0.05). We found significant correlation between RT-CON and RT-VAR for the fCSA changes (r = 0.89). fCSA changes were also correlated to satellite cells (r = 0.42) and myonuclei (r = 0.50) changes. Heatmap analyses showed coupled changes in fCSA, SC, and myonuclei responses at the individual level, regardless of the RT protocol. The high between and low within-subject variability regardless of RT protocol suggests that the intrinsic biological factors seem to be more important to explain the magnitude of fCSA gains in resistance-trained subjects.
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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) | 0.002 | 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".