The roles of the Na<sup>+</sup>/K<sup>+</sup>-ATPase, NKCC, and K<sup>+</sup>channels in regulating local sweating and cutaneous blood flow during exercise in humans in vivo
Bibliographic record
Abstract
Na+/K+-ATPase has been shown to regulate the sweating and cutaneous vascular responses during exercise; however, similar studies have not been conducted to assess the roles of the Na-K-2Cl co-transporter (NKCC) and K+ channels. Additionally, it remains to be determined if these mechanisms underpinning the heat loss responses differ with exercise intensity. Eleven young (24 ± 4 years) males performed three 30-min semirecumbent cycling bouts at low (30% VO2peak), moderate (50% VO2peak), and high (70% VO2peak) intensity, respectively, each separated by 20-min recovery periods. Using intradermal microdialysis, four forearm skin sites were continuously perfused with either: (1) lactated Ringer solution (Control); (2) 6 mmol·L−1 ouabain (Na+/K+-ATPase inhibitor); (3) 10 mmol·L−1 bumetanide (NKCC inhibitor); or (4) 50 mmol·L−1 BaCl2 (nonspecific K+ channel inhibitor); sites at which we assessed local sweat rate (LSR) and cutaneous vascular conductance (CVC). Inhibition of Na+/K+-ATPase attenuated LSR compared to Control during the moderate and high-intensity exercise bouts (both P ˂ 0.01), whereas attenuations with NKCC and K+ channel inhibition were only apparent during the high-intensity exercise bout (both P ≤ 0.05). Na+/K+-ATPase inhibition augmented CVC during all exercise intensities (all P ˂ 0.01), whereas CVC was greater with NKCC inhibition during the low-intensity exercise only (P ˂ 0.01) and attenuated with K+ channel inhibition during the moderate and high-intensity exercise conditions (both P ˂ 0.01). We show that Na+/K+-ATPase, NKCC and K+ channels all contribute to the regulation of sweating and cutaneous blood flow but their influence is dependent on the intensity of dynamic exercise.
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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".