Effect of local heat application during exercise on gene expression related to mitochondrial homeostasis
Bibliographic record
Abstract
The aim of this study was to determine the impact of local muscle heating during endurance exercise on human skeletal muscle mitochondrial-related gene expression. Twelve subjects (25 ± 6 yr, 177 ± 8 cm, 78 ± 16 kg, and peak aerobic capacity 45 ± 8 mL·kg–1·min–1) cycled with one leg heated (HOT) and the other serving as a control (CON). Skin and intramuscular temperatures were taken before temperature intervention (Pre), after 30 minutes (Pre30), after exercise (Post) and four hours after exercise (4Post). Muscle biopsies were taken from each leg at Pre and 4Post. Intramuscular temperature increased within HOT (34.4 ± 0.7 °C to 36.1 ± 0.5 °C, p < 0.001) and was higher than CON at Pre30 (34.0 ± 0.7 °C, p < 0.001). However, temperatures at POST were similar (HOT 38.4 ± 0.7 °C, CON 38.3 ± 0.5 °C, p = 0.661). Skin temperature was higher than CON at Post30 (30.3 ± 1.0 °C, p < 0.001) and Post (HOT 34.6 ± 0.9 °C, CON 32.3 ± 1.6 °C, p < 0.001). PGC-1α, VEGF and NRF2 mRNA increased with exercise (p < 0.05) but was not altered with heating (p > 0.05). TFAM increased after exercise with heat application (HOT, p = 0.019) but not with exercise alone (CON, p = 0.422). There was no difference in NRF1, ESRRα, or any of the mitophagy related genes in response to exercise or temperature (p > 0.05). In conclusion, TFAM is enhanced by local heat application during endurance exercise, whereas other genes related to mitochondrial homeostasis are unaffected. Novelty: The main finding of this study is that localized heating increased TFAM mRNA expression. The normal exercise-induced increased PGC-1α gene expression was unaltered by local muscle heating.
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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".