Cytokine induction during exertional hyperthermia is abolished by core temperature clamping: neuroendocrine regulatory mechanisms
Bibliographic record
Abstract
The immunomodulatory effects of physiological temperature change remain poorly understood and inter-relationships between changes in core temperature, stress hormones and cytokines during exertional hyperthermia are not well established. This experimental study was designed to examine how cytokine (tumour necrosis factor (TNF)-alpha, interleukin (IL)-6, IL-12 and IL-1ra (receptor antagonist)) and hormone (epinephrine (Epi), norepinephrine (NE), growth hormone (GH) and cortisol (CORT)) responses are modified when the exercise-induced rise in core temperature is attenuated or exacerbated by immersion in a water bath. Ten men ((mean +/- SD) age: 26.9 +/- 5.7 years; height 1.75 +/- 0.07 m; body mass 76.0 +/- 10.9 kg; O(2 peak): 48.0 +/- 12.4 mL kg(-1) min(-1)) completed two 40-min cycle ergometer exercise trials at 65% O(2 peak) while immersed to mid-chest. Rectal temperature (T(re)) peaked at 39.1 +/- 0.03 and 37.5 +/- 0.13 degrees C during the hot (39 degrees C) and cold (18 degrees C) conditions, respectively. Blood samples were collected before, during (20- and 40-min) and after (30- and 120-min) exercise. Increases in circulating NE (>350%), Epi (>500%), GH (>900%), IL-12 (>150%) and TNF-alpha (>90%) were greatest after 40-min exercise in the heat. Substantial elevations of CORT (80%), IL-1ra (150%) and IL-6 (>400%) did not occur until after exercise was complete. Core temperature clamping decreased the rise in circulating stress hormone concentrations and abolished increases in plasma cytokine concentrations. These findings suggest that exercise-associated elevations of T(re) mediate increases of circulating stress hormones, which subsequently contribute to induction of circulating cytokine release.
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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".