Influence of forearm muscle metaboreceptor activation on sweating and cutaneous vascular responses during dynamic exercise
Bibliographic record
Abstract
We examined whether the sustained activation of metaboreceptor in forearm during cycling exercise can modulate sweating and cutaneous vasodilation. On separate days, 12 young participants performed a 1.5-min isometric handgrip exercise at 40% maximal voluntary contraction followed by 1) 9-min forearm ischemia (Occlusion, to activate metaboreceptor) or 2) no ischemia (Control) in thermoneutral conditions (27°C, 50%) with mean skin temperature clamped at 34°C. Thirty seconds after the handgrip exercise, participants cycled for 13.5 min at 40% V̇o2 max For Occlusion, forearm ischemia was maintained for 9 min followed by no ischemia thereafter. Local sweat rate (SR, ventilated capsule) and cutaneous vascular conductance (CVC, laser-Doppler perfusion units/mean arterial pressure) on the contralateral nonischemic arm as well as esophageal and skin temperatures were measured continuously. The period of ischemia in the early stages of exercise increased SR (+0.03 mg·cm(-2)·min(-1), P < 0.05) but not CVC (P > 0.05) above Control levels. No differences were measured in the esophageal temperature at which onset of sweating (Control 37.19 ± 0.09 vs. Occlusion 37.07 ± 0.09°C) or CVC (Control 37.21 ± 0.08 vs. Occlusion 37.08 ± 0.10°C) as well as slopes for these responses (all P > 0.05). However, a greater elevation in SR occurred thereafter such that SR was significantly elevated at the end of the ischemic period relative to Control (0.37 ± 0.05 vs. 0.23 ± 0.05 mg·cm(-2)·min(-1), respectively, P < 0.05) despite no differences in esophageal temperature. We conclude that the activation of forearm muscle metaboreceptor can modulate sweating, but not CVC, during cycling exercise without affecting the core temperature-SR relationship.
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) | 0.001 | 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".