Effects of obesity and mild hypohydration on local sweating and cutaneous vascular responses during passive heat stress in females
Bibliographic record
Abstract
The purpose of this study was to evaluate the effect of obesity and mild hypohydration on local sweating (LSR) and cutaneous vascular conductance (CVC) responses during passive heat stress in females. Thirteen obese (age, 24 ± 4 years; 45.4% ± 5.2% body fat) and 12 nonobese (age, 22 ± 2 years; 25.1% ± 3.9% body fat) females were passively heated (1.0 °C rectal temperature increase) while either euhydrated (EUHY) or mildly hypohydrated (HYPO; via fluid restriction). Chest and forearm LSR (ventilated capsule) and CVC (Laser Doppler flowmetry) onset, sensitivity, and plateau/steady state were recorded as mean body temperature increased (ΔTb). Participants began trials EUHY (urine specific gravity, Usg = 1.009 ± 0.006) or HYPO (Usg = 1.025 ± 0.004; p < 0.05), and remained EUHY or HYPO. Independent of obesity, HYPO decreased sweat sensitivity at the chest (HYPO = 0.79 ± 0.35, EUHY = 0.95 ± 0.39 Δmg·min(-1)·cm(-2)/°C ΔTb) and forearm (HYPO = 0.82 ± 0.39, EUHY = 1.06 ± 0.34 Δmg·min(-1)·cm(-2)/°C ΔTb); forearm LSR plateau was also decreased (HYPO = 0.66 ± 0.19, EUHY = 0.78 ± 0.23 mg·min(-1)·cm(-2); all p < 0.05). Overall, obese females had lower chest-sweat sensitivity (0.72 ± 0.35 vs. 1.01 ± 0.33 Δmg·min(-1)·cm(-2)/°C ΔTb) and plateau (0.55 ± 0.27 vs. 0.80 ± 0.25 mg·min(-1)·cm(-2); p < 0.05). While hypohydrated, obese females had a lower chest LSR (p < 0.05) versus nonobese females midway (0.45 ± 0.26 vs. 0.73 ± 0.23 mg·min(-1)·cm(-2)) and at the end (0.53 ± 0.27 vs. 0.81 ± 0.24 mg·min(-1)·cm(-2)) of heating. Furthermore, HYPO (relative to the EUHY trials) led to a greater decrease in CVC sensitivity in obese (-28 ± 27 Δ% maximal CVC/°C ΔTb) versus nonobese females (+9.2 ± 33 Δ% maximal CVC/°C ΔTb; p < 0.05). In conclusion, mild hypohydration impairs females' sweating responses during passive heat stress, and this effect is exacerbated when obese.
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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".