Impact of passive heat stress and passive heat acclimation on circulating extracellular vesicles: An exploratory analysis
Bibliographic record
Abstract
Abstract This retrospective exploratory analysis aimed to improve our understanding of the effect of passive heat stress and subsequent heat acclimation on the circulating concentration of extracellular vesicles (EVs). Healthy young adults (four females and six males, 25 ± 4 years of age, 1.72 ± 0.08 m in height and weighing 71.6 ± 9.0 kg) were heated with a water‐perfused suit before and after seven consecutive days of hot water immersion. Pre‐acclimation, participants were heated until oesophageal temperature increased to ∼1.4°C above baseline values. Post‐acclimation, participants were heated until oesophageal temperature reached the same absolute value as the pre‐acclimation visit (∼38.2°C). Venous blood samples were obtained before and at the end of passive heating to quantify plasma concentrations of EVs from all cell types (CSFE + ), all cell types except erythrocytes (CSFE + MHCI + ), platelets (CSFE + MHCI + CD41 + ), endothelial cells (CSFE + MHCI + CD62e + ), red blood cells (CSFE + CD235a + ) and leucocytes (CSFE + MHCI + CD45 + ) via flow cytometry. Passive heat stress increased the concentration of CFSE + EVs (46,150,000/ml [3,620,784, 88,679,216], P = 0.036), CFSE + MHCI + EVs (28,787,500/ml [9,851,127, 47,723,873], P = 0.021) and CSFE + MHCI + CD41 + EVs (28,343,500/ml [9,637,432, 47,049,568], P = 0.008). The concentration of CSFE + MHCI + CD62e + EVs (94,230/ml [−55,099, 243,559], P = 0.187), CSFE + CD235a + EVs (−1,414/ml [−15,709, 12,882], P = 0.403) or CSFE + MHCI + CD45 + EVs (−192,915/ml [−690,166, 304,336], P = 0.828) did not differ during heat stress. The change in circulating EVs during passive heat stress did not differ after heat acclimation (thermal state × acclimation interactions, all P ≥ 0.180). These results demonstrate that passive heat stress increases the circulating concentration of total and platelet EVs and that passive heat acclimation does not alter this increase.
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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) | 0.000 | 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) | 0.000 | 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".