A pharmacologic activator of endothelial KCa channels improves coronary function in the hearts of type 2 diabetic rats (1078.10)
Bibliographic record
Abstract
Endothelial dysfunction is a common early pathogenic event in patients with type 2 diabetes (T2D) who exhibit cardiovascular disease. In the present study, we have examined the effect of SKA‐31, a positive modulator of endothelial Ca2+‐activated K+ (KCa) channels, on total coronary flow in isolated hearts from Goto‐Kakizaki (GK) rats, a non‐obese model of T2D and age‐matched controls. Total coronary flow and left ventricular developed pressure were monitored simultaneously in spontaneously beating, Langendorff‐perfused hearts. Acute, bolus administrations of bradykinin (BK, 1 μg) or adenosine (ADO, 10 μg) increased coronary flow, and these responses were blunted in diabetic hearts at 10‐12 and 18‐20 weeks of age. In contrast, SKA‐31 dose‐dependently (0.01‐5 μg bolus exposures) increased coronary flow to comparable levels in both control and diabetic rat hearts at both ages. Flow responses to sodium nitroprusside were not different between control and diabetic hearts, suggesting normal coronary smooth muscle function. Importantly, continuous exposure to a sub‐threshold concentration of SKA‐31 (i.e. 0.3 μM) did not alter basal coronary flow or heart rate, but did ameliorate the blunted BK and ADO‐evoked flow responses in diabetic hearts. In summary, these data demonstrate that SKA‐31 is an effective coronary vasodilator in a rat model of T2D exhibiting endothelial dysfunction and metabolic syndrome, and further rescues impaired vasodilatory responses to BK and ADO in the coronary circulation. Grant Funding Source : Supported by CIHR
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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 | 0.000 | 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) | 0.002 | 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".