Intensity, muscle activation, and PGC‐1α expression are dissociated following supramaximal interval exercise
Bibliographic record
Abstract
Muscle activation and the change in peroxisome proliferator‐activated receptor y co‐activator 1α (PGC‐1α) mRNA expression following high‐intensity interval exercise (HIIE) were examined in healthy men (n=8; age, 21.9 ± 2.2 yrs; VO 2peak , 53.1 ± 6.4 ml/min/kg; peak WR, 317 ± 23.5 watts). Participants reported to the lab for 3 experimental visits. On each visit HIIE was performed on a cycle ergometer with a target intensity of either 73 (LO), 100 (MI), or 133% (HI) of peak WR. Muscle biopsies were taken at rest and 3 hours after each exercise condition. There were no differences in total work between conditions (~730 kJ) while average power output (LO, 237 ± 21; MI, 323 ± 26; HI, 384 ± 35 watts) and EMG derived muscle activation (LO, 1262 ± 605; MI, 2089 ± 737; HI, 3029 ± 1206 total integrated EMG per interval) increased in an intensity dependent fashion. PGC‐1α mRNA was elevated (p<0.05) after all 3 conditions, with the increase observed following MI (~9 fold) being greater (p<0.05) than that observed following both LO and HI (~4 fold). When expressed relative to muscle activation, the change in PGC‐1α for HI was less than (p<0.05) that for LO and MI. These findings suggest that intensity dependent increases in PGC‐1α mRNA following submaximal exercise are largely due to increases in muscle recruitment. Interestingly, intensity, muscle activation and PGC‐1α mRNA were dissociated following HI. This research was supported by NSERC.
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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".