Endothelial KCa channels: Novel targets to reduce atherosclerosis-driven vascular dysfunction
Bibliographic record
Abstract
Elevated levels of cholesterol in the blood can induce endothelial dysfunction, a condition characterized by impaired nitric oxide production and decreased vasodilatory capacity. Endothelial dysfunction can promote vascular disease, such as atherosclerosis, where macrophages accumulate in the vascular intima and fatty plaques form that impair normal blood flow in conduit arteries. Current pharmacological strategies to treat atherosclerosis mostly focus on lipid lowering to prevent high levels of plasma cholesterol that induce endothelial dysfunction and atherosclerosis. While this approach is effective for most patients with atherosclerosis, for some, lipid lowering is not enough to reduce their cardiovascular risk factors associated with atherosclerosis (e.g., hypertension, cardiac dysfunction, stroke, etc.). For such patients, additional strategies targeted at reducing endothelial dysfunction may be beneficial. One novel strategy to restore endothelial function and mitigate atherosclerosis risk is to enhance the activity of Ca2+-activated K+ (KCa) channels in the endothelium with positive gating modulator drugs. Here, we review the mechanism of action of these small molecules and discuss their ability to improve endothelial function. We then explore how this strategy could mitigate endothelial dysfunction in the context of atherosclerosis by examining how KCa modulators can improve cardiovascular function in other settings, such as aging and type 2 diabetes. Finally, we consider questions that will need to be addressed to determine whether KCa channel activation could be used as a long-term add-on to lipid lowering to augment atherosclerosis treatment, particularly in patients where lipid-lowering is not adequate to improve their cardiovascular health.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".