Different responses of apoptotic, inflammatory and heat shock protein gene expression to a single bout of high-intensity interval exercise between physically active and inactive men
Bibliographic record
Abstract
High mechanical load of muscles may induce muscular apoptosis on the one hand and adaptation to exercise on the other. This study aimed to explore whether changes of circulatory levels of inflammation, apoptosis and heat shock proteins (HSPs) messenger RNA (mRNA) following single bout of high-intensity interval exercise (HIIE) differs between physically active (PA) and inactive (PI) men. Nine PA and 9 PI (peak oxygen consumption: 2.6 ± 0.4 vs. 2.0 ± 0.2 L·min −1 ) healthy men (age: 28.7 ± 6.3 vs. 30.2 ± 4.5 years and body mass index: 2.6 ± 2.1 vs. 23.3 ± 2.8 kg·m −2 ) performed HIIE, comprising 4 repeats of a Wingate test (load: 0.050 kg·kg −1 body weight). Blood samples were collected before exercise, 5 min after HIIE, and 24 h after HIIE for measuring mRNA of inflammation markers interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), apoptosis markers including Bcl-2, Bax, and HSP27, HSP60, HSP70, HSP90 using quantitative real-time PCR analysis. Post-HIIE IL-6, TNFα and HSP60 were higher in the PI than the PA group 5 min after exercise (p = 0.003, effect size (ES) = 1.59; p = 0.007, ES = 1.59 and p = 0.027, ES = 1.10 respectively). HSP70 acutely increased only in the PA group (p = 0.024, ES = 1.20). The increase in Bcl-2 (p = 0.047, ES = 1.08) and Bax (p = 0.024, ES = 1.20) levels were higher in the PI group 5 min after HIIE. The present study indicated that the response of inflammatory, apoptosis and HSP gene expressions to HIIE in blood of healthy male volunteers strongly depends on their level of regular physical activity. Novelty: Blood IL-6 and HSP60 mRNA levels following high intensity exercise may indicate metabolic stress. Increased blood HSP70 mRNA in physically active men may show an alternative apoptosis suppression pathway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".