Adenosine receptor inhibition attenuates cutaneous vasoconstriction during whole‐body cooling
Bibliographic record
Abstract
It has been suggested that adenosine may modulate cutaneous vasoconstriction. Therefore, we examined if adenosine receptor inhibition would attenuate decreases in cutaneous vascular conductance (CVC) during cold stress. Two microdialysis probes were inserted into the dermis layer of the forearm in eight subjects (4 females, 4 males). The probes were infused continuously with Ringer's lactate (CON) or theophylline (THEO). Skin blood flow was measured continuously (laser‐Doppler) to calculate CVC (% of max). Following 45 min of infusion to ensure complete blockade of adenosine receptors, subjects were exposed to two 20 min periods of whole‐body cooling (via water‐perfused suit), each separated by 45 min of heat stress. Decreases in CVC did not differ between CON and THEO sites during the first cooling period (p=0.218). CVC increased to similar levels during the heat stress period (p=0.820). During the second cooling period, CVC was modeled using nonlinear regression, which revealed that the curves differed between sites (F (4,168) = 7.712, p<0.01). Minimum values of CVC were significantly greater at THEO (38 ± 10%) compared to CON (19 ± 13%, p<0.01). These results suggest that adenosine receptor inhibition significantly attenuates cutaneous vasoconstriction during whole‐body cold stress, but only following cutaneous vasodilation induced via passive heating. Supported by NSERC grant RGPIN‐298159–2009 (held by GP Kenny).
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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) | 0.001 | 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".