Ageing attenuates muscarinic‐mediated sweating differently in men and women with no effect on nicotinic‐mediated sweating
Bibliographic record
Abstract
Ageing attenuates muscarinic-mediated sweating. However, whether ageing also impairs nicotinic-mediated sweating remains unclear. Further, despite the known sex-related differences in peripheral sweat gland function, it remains unclear whether age-related modifications of muscarinic and nicotinic-mediated sweating, if any, are similar between men and women. We assessed local sweating in young and older healthy men and women (n = 11, each group) at two dorsal forearm skin sites receiving either: (a) methacholine (muscarinic receptor agonist, 5 doses: 0.0125, 0.25, 5, 100, 2000 mmol/L) or (b) nicotine (nicotinic receptor agonist, 5 doses: 1.2, 3.6, 11, 33, 100 mmol/L) via intradermal microdialysis. Age-related reductions in methacholine-induced sweating were observed at low-to-moderate doses (0.0125-5 mmol/L; all P ≤ 0.05) in men, whereas a reduction was only evident at the highest methacholine dose (2000 mmol/L; P ≤ 0.05) in women. No effect of ageing was observed for nicotine-induced sweating (all P > 0.26 for main effects of age, dose and all interactions). We showed that while healthy ageing attenuates low-to-moderate levels of muscarinic-mediated sweating in men, reductions are only observed at high levels of muscarinic-mediated sweating in women. However, healthy ageing does not modulate nicotinic-mediated sweating in either men or women.
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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) | 0.003 | 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".