Regional variation in nitric oxide‐dependent cutaneous vasodilatation during local heating in young adults
Bibliographic record
Abstract
New Findings What is the central question of this study? Are regional differences in nitric oxide (NO)‐dependent cutaneous vasodilatation during local skin heating present in young adults? What is the main finding and its importance? NO‐dependent cutaneous vasodilatation varied across the body. The abdomen demonstrated larger NO contributions, while the chest demonstrated smaller NO contributions, compared to other regions. This exploratory work is an important first step in characterizing regional heterogeneity of cutaneous microvascular control across the torso and limbs. Equally, it serves to generate hypotheses for future studies examining regional cutaneous microvascular control in ageing and disease. Abstract Regional variations in cutaneous vasodilatation during local skin heating exist across the body. While nitric oxide (NO) is a well‐known modulator of this response, the extent of regional differences in NO‐dependent cutaneous vasodilatation during local skin heating remains uncertain. In 16 habitually active young adults (8 females; 25 ± 5 years), cutaneous vascular conductance, normalized to maximum vasodilatation (% CVC max ), was assessed at the upper chest, abdomen, dorsal forearm, thigh and lateral calf during local skin heating. Across all regions, local skin temperatures were simultaneously increased from 33 to 42°C (1°C per 10 s), and held until a stable heating plateau was achieved (∼40 min). Next, with local skin temperature maintained at 42°C, 20 mM of N G ‐nitro‐ l ‐arginine methyl ester ( l ‐NAME) was continuously infused at each site until a stable l ‐NAME plateau was achieved (∼40 min). The difference between heating and l ‐NAME plateaus was identified as the NO contribution for each region. There was no evidence for region‐specific responses at baseline ( P = 0.561), the heating plateau ( P = 0.351) or l ‐NAME plateau ( P = 0.082), but there was for the NO contribution ( P = 0.048). Overall, point estimates for between‐region differences in the NO contribution varied across the body from 0 to 19% CVC max . The greatest effects were observed for the abdomen, wherein the NO contribution was consistently greater than for the other regions (range: 9–19% CVC max ). The chest was consistently lower than the other regions (range: 7–19% CVC max ). The smallest effects were observed between limb regions (range: 0–2% CVC max ). These findings advance our understanding of the mechanisms influencing regional variations in the cutaneous vasodilator response to local skin heating in young adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".