Effect of Heating Duration on Brachial Artery Endothelial Function in Humans
Bibliographic record
Abstract
In humans local heat stress elicits arterial vasodilation through two known mechanisms: axon reflexes responsible for the initial rise in skin blood flow (SkBF), and heat shock protein (HSP)-mediated release of nitric oxide (NO) responsible for the prolonged plateau in SkBF. The effect of selective targeting of these mechanisms, through local heating protocols of varying durations, on endothelial function is unknown. PURPOSE: To determine the effect of 10 minutes (axon reflexes) vs. 30 minutes (axon reflexes + HSP-mediated NO release) of local forearm heating on brachial artery (BA) endothelial function. METHODS: Five young, apparently healthy, recreationally active males (21±2 years old) were recruited. In separate visits, heating was applied to the left forearm using a commercially available heating pad set to high for either 10 minutes (HEAT10, 41.7±0.8 °C) or 30 minutes (HEAT30, 43.5±2.0 °C). Endothelial function was measured before and after each heating intervention through a BA flow-mediated dilation (FMD) test. RESULTS: Allometric scaling was performed on the entire data set to account for the increased arterial diameter observed after heating in the 30-minute condition. A generalized estimating equations analysis with an exchangeable correlation structure revealed a main effect of time on BA FMD (rest: 6.4±1.3 vs. HEAT: 7.7±2.2%, P = 0.027), suggesting that both 10-minute and 30-minute heating protocols improved endothelial function. CONCLUSION: These findings suggest that BA FMD is improved similarly with 10 minutes or 30 minutes of local heat stress applied to the forearm; and that the addition of local NO release to axon reflex-mediated vasodilation may not further enhance the acute endothelial function responses. Supported by NSERC DG #238819-13.
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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) | 0.002 | 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".