SKA‐31, a positive modulator of SK <sub>Ca</sub> and IK <sub>Ca</sub> channels, increases systemic conductance and lowers arrterial pressure in an anesthetized pig model
Bibliographic record
Abstract
SKA‐31 regulates microvascular tone, tissue perfusion and blood pressure by acting on endothelial SK Ca and IK Ca channels. In this study, we have examined the hemodynamic effects of SKA‐31 in comparison to the established vasodilator sodium nitroprusside (SNP) in anesthetized domestic pigs. We measured left ventricular (LV), aortic and inferior vena cava (IVC) pressures and monitored levels of blood flow in the ascending aorta, right carotid, left anterior descending coronary and left renal arteries during acute intravenous infusions of SKA‐31 (0.1 to 5 mg/kg), followed by a single dose of SNP (5 μg/kg). SKA‐31 dose‐dependently and reversibly decreased mean aortic pressure (mP AO ) to a level comparable to that produced by SNP (−23±3 vs. −28±4 mmHg). Mean IVC pressure did not change. Both SKA‐31 (1 to 5 mg/kg doses) and SNP increased systemic conductance, along with coronary and carotid artery conductances; renal conductance did not change. There was no change in LV stroke volume (SV) for any infusion, and heart rate was not altered following SKA‐31 administration (132±14 vs. 128±13 bpm). With no change in SV, the significant decrease in LV stroke work (SW) observed with either SKA‐31 or SNP was largely attributed to decreased mP AO . In summary, SKA‐31 significantly decreased mP AO by increasing systemic conductance, and did not appear to reduce cardiac contractility. Supported by the CIHR (JVT and APB) and the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".