Heat acclimation increases arterial elasticity in young men
Bibliographic record
Abstract
The major physiological adaptations that occur during heat acclimation (HA) are well documented. However, no studies have provided compelling evidence about the effect of HA on arterial elastic properties. The aim of this study was to examine the changes in large artery elasticity (LAE) and small artery elasticity (SAE) concomitant with HA and to determine the potential relationships among changes in arterial elasticity, baseline aerobic fitness level, and improvement in endurance capacity (EC). During 10-day HA, the subjects (n = 21) exercised daily on a treadmill for 110 min at an intensity of 55%-60% of peak oxygen uptake in a climatic chamber preset to 42 °C and 18% relative humidity. EC was tested in the heat before and after HA. Arterial elasticity was assessed by diastolic pulse wave analysis (HDI/Pulse Wave CR-2000) at baseline and after HA. Blood samples were drawn at baseline. After HA, there was a 17% increase in LAE (from 21.19 ± 4.72 mL·mm Hg(-1) × 10 to 24.77 ± 5.91 mL·mm Hg(-1) × 10, p < 0.05) and an 18% increase in SAE (from 9.32 ± 1.76 mL·mm Hg(-1) × 100 to 10.98 ± 1.75 mL·mm Hg(-1) × 100, p < 0.01). EC increased by 86% (from 88.62 ± 27.51 min to 161.95 ± 47.80 min, p < 0.001) as a result of HA. No significant associations were revealed between changes in arterial elasticity parameters and improvement in EC or baseline aerobic fitness level. We demonstrated, for the first time, that HA has a positive impact on the parameters of arterial elasticity. Further investigations are needed to determine the mechanisms underlying these changes and the potential relationships among arterial elasticity, aerobic fitness level, and EC.
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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) | 0.002 | 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".