Separate and combined effects of K<sub>Ca</sub> and K<sub>ATP</sub> channel blockade with NOS inhibition on cutaneous vasodilation and sweating in older men during heat stress
Bibliographic record
Abstract
Our objective in this study was to examine the separate and combined effects of potassium (K+) channels and nitric oxide synthase (NOS) on cutaneous vasodilation and sweating in older men during rest and exercise in the heat. In 13 habitually active men (61 ± 4 yr), cutaneous vascular conductance and local sweat rate were assessed at six dorsal forearm skin sites continuously perfused with either 1) lactated Ringer (control), 2) 10 mM NG-nitro-l-arginine methyl ester (l-NAME, NOS inhibitor), 3) 50 mM tetraethylammonium (TEA; Ca2+-activated K+ channel blocker), 4) 5 mM glybenclamide (GLY; ATP-sensitive K+ channel blocker), 5) 50 mM TEA + 10 mM l-NAME, and 6) 5 mM GLY + 10 mM l-NAME via microdialysis. Participants rested in non-heat stress (25°C) and heat stress (35°C) conditions for ∼60 min each, followed by 50 min of moderate-intensity cycling (∼55% V̇o2peak) and 30 min of recovery in the heat. During rest and exercise in the heat, l-NAME, TEA + l-NAME, and GLY + l-NAME attenuated CVC relative to control (all P ≤ 0.05), although l-NAME was not different from TEA + l-NAME or GLY + l-NAME (all P > 0.05). TEA attenuated CVC during rest, whereas GLY attenuated CVC during exercise (both P ≤ 0.05). Additionally, whereas neither l-NAME nor TEA altered sweating throughout the protocol (all P > 0.05), combined TEA + l-NAME attenuated sweating during exercise in the heat ( P ≤ 0.05). We conclude that in habitually active older men blockade of KCa and KATP channels attenuates cutaneous vasodilation during rest and exercise in the heat, respectively, and these effects are NOS dependent. Furthermore, combined NOS inhibition and KCa channel blockade attenuates sweating during exercise in the heat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".