The effect of plasma osmolality and baroreceptor loading status on postexercise heat loss responses
Bibliographic record
Abstract
We examined the separate and combined effects of plasma osmolality and baroreceptor loading status on postexercise heat loss responses. Nine young males completed a 45-min treadmill exercise protocol at 58 ± 2% V̇o2 peak, followed by a 60-min recovery. On separate days, participants received 0.9% NaCl (ISO), 3.0% NaCl (HYP), or no infusion (natural recovery) throughout exercise. In two additional sessions (no infusion), lower-body negative (LBNP) or positive (LBPP) pressure was applied throughout the final 45 min of recovery. Local sweat rate (LSR; ventilated capsule: chest, forearm, upper back, forehead) and skin blood flow (SkBF; laser-Doppler flowmetry: forearm, upper back) were continuously measured. During HYP, upper back LSR was attenuated from end-exercise to 10 min of recovery by ∼0.35 ± 0.10 mg·min(-1)·cm(-2) and during the last 20 min of recovery by ∼0.13 ± 0.03 mg·min(-1)·cm(-2), while chest LSR was lower by 0.18 ± 0.06 mg·min(-1)·cm(-2) at 50 min of recovery compared with natural recovery (all P < 0.05). Forearm and forehead LSRs were not affected by plasma hyperosmolality during HYP (all P > 0.28), which suggests regional differences in the osmotic modulation of postexercise LSR. Furthermore, LBPP application attenuated LSR by ∼0.07-0.28 mg·min(-1)·cm(-2) during the last 30 min of recovery at all sites except the forehead compared with natural recovery (all P < 0.05). Relative to natural recovery, forearm and upper back SkBF were elevated during LBPP, ISO, and HYP by ∼6-10% by the end of recovery (all P < 0.05). We conclude that 1) hyperosmolality attenuates postexercise sweating heterogeneously among skin regions, and 2) baroreceptor loading modulates postexercise SkBF independently of changes in plasma osmolality without regional differences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".