Satellite cell activity, without expansion, after nonhypertrophic stimuli
Bibliographic record
Abstract
The purpose of the present studies was to determine the effect of various nonhypertrophic exercise stimuli on satellite cell (SC) pool activity in human skeletal muscle. Previously untrained men and women (men: 29 ± 9 yr and women: 29 ± 2 yr, n = 7 each) completed 6 wk of very low-volume high-intensity sprint interval training. In a separate study, recreationally active men (n = 16) and women (n = 3) completed 6 wk of either traditional moderate-intensity continuous exercise (n = 9, 21 ± 4 yr) or low-volume sprint interval training (n = 10, 21 ± 2 yr). Muscle biopsies were obtained from the vastus lateralis before and after training. The fiber type-specific SC response to training was determined, as was the activity of the SC pool using immunofluorescent microscopy of muscle cross sections. Training did not induce hypertrophy, as assessed by muscle cross-sectional area, nor did the SC pool expand in any group. However, there was an increase in the number of active SCs after each intervention. Specifically, the number of activated (Pax7(+)/MyoD(+), P ≤ 0.05) and differentiating (Pax7(-)/MyoD(+), P ≤ 0.05) SCs increased after each training intervention. Here, we report evidence of activated and cycling SCs that may or may not contribute to exercise-induced adaptations while the SC pool remains constant after three nonhypertrophic exercise training protocols.
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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".