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Enhancement of Endothelial KCa Channel Activity as a Novel Strategy to Oppose Atherosclerosis

2022· article· en· W4225380597 on OpenAlexafffund
O. Daniel Vera, Ramesh C. Mishra, Darrell D. Belke, Liam Hamm, Heike Wulff, Andrew P. Braun

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsChannel (broadcasting)BusinessChemistryCardiologyMedicineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Atherosclerosis represents a major risk factor for cardiovascular disease and is associated with endothelial dysfunction (ED), which facilitates fatty plaque formation, impairs blood flow and increases arterial stiffness. A hallmark feature of ED is decreased bioavailability of nitric oxide (NO). We have previously reported that pharmacological activation of endothelial Ca 2+ ‐activated K + channels (KCa2.3 and KCa3.1) can oppose ED by enhancing endothelium‐dependent vasodilation. We hypothesized that improving endothelial function will mitigate the development and/or severity of atherosclerosis in Apoe knockout (Apoe ‐/‐ ) mice. Administration of the KCa channel activator SKA‐31 was utilized to improve endothelial function in vivo. Experimentally, male, 8‐week old Apoe ‐/‐ mice on a regular chow diet were administered one of three daily treatments for 16 weeks: SKA‐31 (10 mg/kg), the KCa3.1 channel blocker senicapoc (40 mg/kg), or drug vehicle alone. Pharmacological inhibition of KCa3.1 channels is reported to reduce atherosclerosis in Apoe ‐/‐ mice and was utilized as a benchmark. Drugs were formulated in miglyol and condensed milk, which was readily ingested by the mice. Cardiac function and arterial pulse wave velocity (PWV) were assessed by echocardiography under isoflurane anesthesia. Atherosclerotic lesions in the aorta were visualized by Oil‐Red‐O staining and select tissue histology was carried out using H&E staining. Abdominal aortic contractility and relaxation were measured by wire myography in drug‐treated Apoe ‐/‐ mice and age/sex‐matched wild‐type C57/BL6 mice. At the end of drug treatment, left ventricular (LV) ejection fraction, fractional shortening and LV posterior wall thickness were not different in Apoe ‐/‐ mice treated with either SKA‐31 or senicapoc compared with vehicle. Measurements of aortic PWV showed no significant differences among the three groups, which indicated that neither drug treatment decreased aortic stiffness. Oil‐Red‐O staining of the thoracic aorta and aortic arch revealed fatty plaque formation in Apoe ‐/‐ mice (i.e. 10‐12% of area) compared with WT controls (< 1%), however, neither SKA‐31 nor senicapoc treatments reduced plaque formation vs. vehicle. Phenylephrine (PE)‐evoked contraction of abdominal aortic rings appeared similar in WT and vehicle/drug treated Apoe ‐/‐ mice, whereas endothelium‐dependent, acetylcholine‐induced relaxation of PE‐constricted aortic rings was enhanced in aortic rings from mice treated with SKA‐31 and senicapoc vs. vehicle. Although relaxation to the smooth muscle vasodilator sodium nitroprusside was augmented in Apoe ‐/‐ mice compared with WT, this response was not altered by drug treatment. While both SKA‐31 and senicapoc improved aortic endothelial function, neither drug decreased plaque formation or aortic stiffness, perhaps due to the later onset of these events in the progression of atherosclerotic pathology. We will investigate this likelihood by feeding Apoe ‐/‐ mice a high fat diet to accelerate the development of plaque formation and arterial stiffness to determine the effects of both drug treatments on these later stages of atherosclerosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.275
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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