Vasodilators produced during active hyperaemia interact: potassium attenuates the vasodilatory effect of nitric oxide
Bibliographic record
Abstract
We previously demonstrated that multiple vasodilators (i.e. nitric oxide (NO), potassium (K+), adenosine (ADO)) are produced in response to skeletal muscle contraction and their ability to produce significant vasodilation differ depending on skeletal muscle stimulation parameters. Given the production of multiple vasodilators resulting from muscle contraction we sought to determine whether the dilators produced altered one another's vasodilatory ability. We tested whether NO's vasodilatory effect was altered in the presence of K + . Using the hamster cremaster muscle preparation in situ we used intravital microscopy to observe arteriolar vasodilation in response to a range of concentrations of an NO donor, S‐nitroso‐N‐acetyl pennicillamine (SNAP, 10 −8 M‐10 −4 M) in the absence and the presence of 10mM K + . The vasodilation with 10 −8 M‐10 −4 M SNAP (3.1±1.0μm, 7.6±1.4μm, 10.0±1.7μm, 15.7±2.3μm, 17.8±2.1μm respectively) was significantly attenuated in the presence of 10mM K + (−1.9±1.1μm, −0.2±1.5μm, 1.3±2.5μm, 6.5±3.1μm, 11.5±2.8μm respectively). Our data show that K + significantly attenuates the vasodilatory effect of NO. Thus, active hyperaemia may not be the sum effects of multiple individual vasodilators but a product of their interactions. This research was funded by NSERC, Canada.
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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) | 0.001 | 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 | 0.001 |
| 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".