Exercise elevates plasma levels but not gene expression of IL-1β, IL-6, and TNF-α in blood mononuclear cells
Bibliographic record
Abstract
Physical activity induces a subclinical inflammatory response, mediated in part by leukocytes, and manifested by elevated concentrations of circulating proinflammatory cytokines, including interleukin (IL)-1beta, IL-6, and tumor necrosis factor-alpha (TNF-alpha). However, the source of the cytokines that appear during exercise remains unknown. In this study, we examined exercise-induced changes in plasma cytokine concentrations and their corresponding mRNA expression in peripheral blood mononuclear cells. Ten healthy [peak oxygen uptake = 48.8 +/- 6.5 (SD) ml. kg(-1). min(-1)] but untrained men [age = 25 +/- 5 (SD) yr] undertook 3 h of exercise (cycling and inclined walking) at 60-65% peak oxygen uptake. Circulating leukocyte subset counts were elevated during and 2 h postexercise but returned to normal within 24 h. Plasma concentrations of IL-1beta, IL-6, and TNF-alpha peaked at the end of exercise and remained elevated at 2 h (IL-6) and up to 24 h (IL-1beta and TNF-alpha) postexercise. Cytokine gene expression in circulating mononuclear cells was measured by using the reverse transcriptase-polymerase chain reaction; mRNA accumulation did not change with exercise. In conclusion, mRNA accumulation of IL-1beta, IL-6, and TNF-alpha in circulating mononuclear cells is not affected by 3 h of moderate endurance exercise and does not seem to account for the observed increases in plasma cytokines.
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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".