Impact of hot and cold exposure on human skeletal muscle gene expression
Bibliographic record
Abstract
Many human diseases lead to a loss of skeletal muscle metabolic function and mass. Local and environmental temperature can modulate the exercise-stimulated response of several genes involved in mitochondrial biogenesis and skeletal muscle function in a human model. However, the impact of environmental temperature, independent of exercise, has not been addressed in a human model. Thus, the purpose of this study was to compare the effects of exposure to hot, cold, and room temperature conditions on skeletal muscle gene expression related to mitochondrial biogenesis and muscle mass. Recreationally trained male subjects (n = 12) had muscle biopsies taken from the vastus lateralis before and after 3 h of exposure to hot (33 °C), cold (7 °C), or room temperature (20 °C) conditions. Temperature had no effect on most of the genes related to mitochondrial biogenesis, myogenesis, or proteolysis (p > 0.05). Core temperature was significantly higher in hot and cold environments compared with room temperature (37.2 ± 0.1 °C, p = 0.001; 37.1 ± 0.1 °C, p = 0.013; 36.9 ± 0.1 °C, respectively). Whole-body oxygen consumption was also significantly higher in hot and cold compared with room temperature (0.38 ± 0.01 L·min−1, p < 0.001; 0.52 ± 0.03 L·min−1, p < 0.001; 0.35 ± 0.01 L·min−1, respectively). In conclusion, these data show that acute temperature exposure alone does not elicit significant changes in skeletal muscle gene expression. When considered in conjunction with previous research, exercise appears to be a necessary component to observe gene expression alterations between different environmental temperatures in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".