Restoration of Endothelium‐dependent Vasodilatory Responses in T2D Resistance Arteries by a Pharmacologic Activator of Endothelial K <sub>Ca</sub> Channels
Bibliographic record
Abstract
Endothelial dysfunction is a common early pathogenic event in patients with type 2 diabetes (T2D). In isolated hearts from diabetic Goto-Kakizaki (GK) rats, we have reported that SKA-31, a positive modulator of endothelial Ca2+-activated K+ (KCa) channels, enhances total coronary flow. In the current study, we examined the effects of SKA-31 on cannulated, myogenically active cremaster arteries from 24 week old wild-type (WT) and T2D GK rats, along with small intra-thoracic arteries from patients. At 70 mmHg intraluminal pressure, inhibition of myogenic tone by 0.3 μM acetylcholine (ACh) or 0.1 μM bradykinin (BK) (39.5±3.5% and 30.3±5.4%, respectively) was weaker in GK arteries than that observed in WT arteries (ACh = 59.2±3.9%, BK = 50.5±4.1%). In contrast, inhibition of tone by 10 μM SNP, a smooth muscle relaxant, was comparable in both arteries (GK = 56.2±3.3%, WT = 51.5±5.9%). Following exposure to a threshold concentration of SKA-31 (0.3 μM), vasodilatory responses to ACh (55.2±4.3%) and BK (49.2±5.5%) were significantly augmented in GK arteries, whereas only modest effects were observed in WT arteries. SKA-31 exposure also enhanced vasodilatory responses to ACh (0.3 μM) and BK (0.1 μM) in human arteries, but did not alter responses to either SNP or pinacidil. Electrophysiologically, KCa current densities were similar in isolated endothelial cells from WT and GK vessels. In summary, these data demonstrate that SKA-31 is able to restore endothelium-dependent vasodilatory responses in a rat model of T2D exhibiting endothelial dysfunction and metabolic syndrome, and has similar effects in resistance arteries from T2D patients. Supported in part by research funding to APB from the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".