Serum, interstitial and sweat ATP in humans exposed to heat stress: Insights into roles of ATP in the heat loss responses
Bibliographic record
Abstract
Abstract Hyperthermia increases intravascular adenosine triphosphate (ATP) and is associated with greater hyperthermia‐induced cutaneous vasodilation. Hyperthermia may also increase skin interstitial fluid ATP thereby activating cutaneous vascular smooth muscle cells and sweat glands. We evaluated the hypothesis that whole‐body heating would increase skin interstitial fluid ATP, and this response would be associated with an increase in cutaneous vasodilation and sweating. Nineteen (8 females) young adults underwent whole‐body heating using a water‐perfusion suit to increase core temperature by ~1°C during which time cutaneous vascular conductance (CVC, ratio of laser‐Doppler blood flow to mean arterial pressure) and sweat rate (ventilated capsule technique) were measured at four forearm skin sites to minimize between‐site variations. Dialysate from the skin sites were collected via intradermal microdialysis. Heating increased serum ATP, CVC, and sweat rate (all p ≤ 0.031). However, heating did not modulate dialysate ATP (median, baseline vs. end‐heating: 2.38 vs. 2.70 nmol/ml) (p = 0.068), though the effect size was moderate (Cohen's d = 0.566). While the heating‐induced increase in CVC was not correlated with changes in serum ATP (r = 0.439, p = 0.060), we observed a negative correlation (rs = −0.555, p = 0.017) between dialysate ATP and CVC. We did not observe a significant correlation between the heating‐induced sweating and serum, dialysate, or sweat ATP (rs = 0.091 to −0.322, all p ≥ 0.222). Altogether, we showed that passive heating increases ATP in blood and possibly skin interstitial fluid, with the latter potentially blunting cutaneous vasodilation. However, ATP does not appear to modulate sweating.
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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 | 0.001 |
| 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".