Targeting endothelial KCa channels in vivo restores arterial and endothelial function in type 2 diabetic rats
Bibliographic record
Abstract
OBJECTIVE: This study tested the hypothesis that administration of the KCa channel activator SKA-31 restores endothelium-dependent vasodilation in vivo in Type 2 Diabetic (T2D) rats. BACKGROUND: Acute treatment of isolated resistance arteries from T2D rats and humans with SKA-31 significantly improved endothelium-dependent vasodilation. However, it is unknown whether these in situ actions translate to intact vascular beds in vivo. METHODS: Male Sprague Dawley (SD) and T2D Goto-Kakizaki (GK) rats (26-32 weeks of age) were injected intraperitoneally with either drug vehicle or 10 mg/kg SKA-31. Doppler ultrasound imaging was used to record reactive hyperemia/flow-mediated dilation (FMD) in the femoral artery following release of an occlusion cuff on the distal hind limb, along with diameter changes in the left main coronary artery in response to inhaled isoflurane (2 % → 5 %). RESULTS: Vehicle treated SD rats exhibited a robust and reversible FMD response, the magnitude and time course of which did not differ in SD rats treated with SKA-31. In contrast, only a weak FMD response was observed in vehicle-treated T2D GK rats, whereas prior SKA-31 administration restored FMD to the level observed in control SD rats. Exposure of SD rats to 5 % isoflurane caused robust coronary artery dilation, which was not altered by prior treatment with SKA-31. In T2D GK rats, 5 % isoflurane inhalation alone did not increase coronary artery diameter, however, a strong vasodilatory response was observed following SKA-31 treatment. SKA-31 administration did not modify intrinsic heart rate responses in either protocol. CONCLUSIONS: Enhancement of KCa channel activity in vivo restores endothelium-dependent vasodilation in T2D rats that exhibit peripheral endothelial dysfunction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".