SKA‐31, an enhancer of SK <sub>Ca</sub> and IK <sub>Ca</sub> channels, increases coronary flow in normotensive rats
Bibliographic record
Abstract
Endothelial SK Ca and IK Ca channels play an important role in the regulation of microvascular tone. We have previously shown that SKA‐31 (naphtho[1,2‐d]thiazole‐2‐ylamine), can inhibit myogenic tone in isolated resistance arteries independent of vasodilatory agonists. We thus hypothesized that acute administration of SKA‐31 may dilate coronary arteries and increase coronary flow. In a Langendorff‐perfused beating rat heart preparation, SKA‐31 dose dependently (0.01–5 μg, single doses) increased total coronary flow (25–30%) in both male (EC 50 : 0.76 μg) and female (EC 50 : 0.54 μg) hearts. Increased coronary flow was associated with modest (<20%) increases in left ventricular (LV) pressure and heart rate. Observed changes in flow were more sensitive to SKA‐31 compared to LV pressure and heart rate. The SKA‐31 evoked increases in coronary flow, LV pressure and heart rate were inhibited by a combination of the SK Ca blocker apamin and the IK Ca blocker TRAM‐34. SKA‐31 evoked vasodilation and associated changes in LV pressure and heart rate were qualitatively comparable to responses evoked by bradykinin (1 μg) and adenosine (10 μg) in the same preparation. Our results demonstrate that SKA‐31 can induce coronary artery dilatation in intact functioning hearts, and further indicate that endothelial SK Ca /IK Ca channel‐mediated vasorelaxation can be evoked under basal conditions. Supported by CIHR (APB) and NIH (HW).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".