Do metaboreceptors alter heat loss responses following dynamic exercise?
Bibliographic record
Abstract
Metaboreceptor activation during passive heating is known to influence cutaneous vascular conductance (CVC) and sweat rate (SR). However, whether metaboreceptors modulate the suppression of heat loss following dynamic exercise remains unclear. On separate days, before and after 15 min of high-intensity treadmill running in the heat (35°C), eight males underwent either 1) no isometric handgrip exercise (IHG) or ischemia (CON), 2) 1 min IHG (60% of maximum, IHG), 3) 1 min IHG followed by 2 min of ischemia (IHG+OCC), 4) 2 min of ischemia (OCC), or 5) 1 min IHG followed by 2 min of ischemia with application of lower body negative pressure (IHG+LBNP). SR (ventilated capsule), cutaneous blood flow (Laser-Doppler), and mean arterial pressure (Finometer) were measured continuously before and after dynamic exercise. Following dynamic exercise, CVC was reduced with IHG exercise (P < 0.05) and remained attenuated with post-IHG ischemia during IHG+OCC relative to CON (39 ± 2 vs. 47 ± 6%, P < 0.05). Furthermore, the reduction in CVC was exacerbated by application of LBNP during post-IHG ischemia (35 ± 3%, P < 0.05) relative to IHG+OCC. SR increased during IHG exercise (P < 0.05) and remained elevated during post-IHG ischemia relative to CON following dynamic exercise (0.94 ± 0.15 vs. 0.53 ± 0.09 mg·min(-1)·cm(-2), P < 0.05). In contrast, application of LBNP during post-IHG ischemia had no effect on SR (0.93 ± 0.09 mg·min(-1)·cm(-2), P > 0.05) relative to post-IHG ischemia during IHG+OCC. We show that CVC is reduced and that SR is increased by metaboreceptor activation following dynamic exercise. In addition, we show that the metaboreflex-induced loading of the baroreceptors can influence the CVC response, but not the sweating response.
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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".