Effect of local cold application during exercise on gene expression related to mitochondrial homeostasis
Bibliographic record
Abstract
Exercise training increases mitochondrial content in active skeletal muscle. Previous work suggests that mitochondrial-related genes respond favorably to exercise in cold environments. However, the impact of localized tissue cooling is unknown. The purpose of this study was to determine the impact of local muscle cooling during endurance exercise on human skeletal muscle mitochondrial-related gene expression. Twelve subjects (age, 28 ± 6 years) cycled at 65% peak power output. One leg was cooled (C) for 30 min before and during exercise with a thermal wrap while the other leg was wrapped but not cooled, room temperature (RT). Muscle biopsies were taken from each vastus lateralis before and 4 h after exercise for the analysis of gene expression. Muscle temperature was lower in the C (29.2 ± 0.7 °C) than the RT (34.1 ± 0.3 °C) condition after pre-cooling for 30 min before exercise (p < 0.001) and remained lower after exercise in the C (36.9 ± 0.5) than the RT (38.4 ± 0.2, p < 0.001) condition. PGC-1α and NRF1 mRNA expression were lower in the C (p = 0.012 and p = 0.045, respectively) than the RT condition at 4 h after exercise. There were no temperature-related differences in other genes (p > 0.05). These data suggest that local cooling has an inhibitory effect on exercise-induced PGC-1α and NRF1 expression in human skeletal muscle. Those considering using local cooling during exercise should consider other systemic cooling options. Novelty: Local cooling has an inhibitory effect on exercise-induced PGC-1α and NRF1 expression in human skeletal muscle. Local cooling may lead to a less robust exercise stimulus compared with standard conditions.
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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) | 0.001 | 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".