Muscle metaboreceptors modulate postexercise sweating, but not cutaneous blood flow, independent of baroreceptor loading status
Bibliographic record
Abstract
We examined whether sustained changes in baroreceptor loading status during prolonged postexercise recovery can alter the metaboreceptors' influence on heat loss. Thirteen young males performed a 1-min isometric handgrip exercise (IHG) at 60% maximal voluntary contraction followed by 2 min of forearm ischemia (to activate metaboreceptors) before and 15, 30, 45, and 60 min after a 15-min intense treadmill running exercise (>90% maximal heart rate) in the heat (35°C). This was repeated on three separate days with continuous lower body positive (LBPP, +40 mmHg), negative (LBNP, -20 mmHg), or no pressure (Control) from 13- to 65-min postexercise. Sweat rate (ventilated capsule; forearm, chest, upper back) and cutaneous vascular conductance (CVC; forearm, upper back) were measured. Relative to pre-IHG levels, sweating at all sites increased during IHG and remained elevated during ischemia at baseline and similarly at 30, 45, and 60 min postexercise (site average sweat rate increase during ischemia: Control, 0.13 ± 0.02; LBPP, 0.12 ± 0.02; LBNP, 0.15 ± 0.02 mg·min(-1)·cm(-2); all P < 0.01), but not at 15 min (all P > 0.10). LBPP and LBNP did not modulate the pattern of sweating to IHG and ischemia (all P > 0.05). At 15-min postexercise, forearm CVC was reduced from pre-IHG levels during both IHG and ischemia under LBNP only (ischemia: 3.9 ± 0.8% CVCmax; P < 0.02). Therefore, we show metaboreceptors increase postexercise sweating in the middle to late stages of recovery (30-60 min), independent of baroreceptor loading status and similarly between skin sites. In contrast, metaboreflex modulation of forearm but not upper back CVC occurs only in the early stages of recovery (15 min) and is dependent upon baroreceptor unloading.
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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".