The effect of exogenous activation of protease-activated receptor 2 on cutaneous vasodilatation and sweating in young males during rest and exercise in the heat
Bibliographic record
Abstract
Protease-activated receptor 2 (PAR2) exists in the endothelial cells of skin vessels and eccrine sweat glands. We evaluated the hypothesis that exogeneous activation of PAR2 augments cutaneous vasodilatation and sweating during rest and exercise in the heat. In 10 young males (23 ± 5 y), cutaneous vascular conductance (CVC) and sweat rate were measured at four forearm skin sites treated with either 1) lactated Ringer (Control), 2) 0.05 mM, 3) 0.5 mM, or 4) 5 mM SLIGKV-NH2 (PAR2 agonist). Participants initially rested in a semi-recumbent posture under a normothermic ambient condition (25°C) for ~60 min. Thereafter, ambient temperature was increased to 35°C while the participants rested for an additional 60 min. Participants then performed a 50-min bout of cycling (~55% of their pre-determined peak oxygen uptake) followed by a 30-min recovery period. Administration of 5 mM SLIGKV-NH2 increased cutaneous vascular conductance relative to the Control site during normothermic resting (P ≤ 0.05). However, we showed that relative to the Control site, no effect on CVC was observed for any administered dose of SLIGKV-NH2 (0.05-5 mM) during rest (33–39%max CVC), end-exercise (68–70%max CVC), and postexercise recovery (49–53%max CVC) in the heat (all P > 0.05). There were no differences in sweat rate between the Control and all SLIGKV-NH2-treated sites throughout the protocol (0.21–0.23, 1.20–1.27, and 0.32–0.33 mg∙min−1∙cm−2 for rest, end-exercise, and postexercise in the heat, respectively, all P > 0.05). We show that while exogeneous PAR2 activation induces cutaneous vasodilatation during normothermic rest, it does not influence the cutaneous blood flow and sweating responses during rest, exercise or recovery 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) | 0.000 | 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) | 0.002 | 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".