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Administration of the KCa channel activator SKA-31 improves endothelial function in the aorta of atherosclerosis-prone mice

2023· article· en· W4378648856 on OpenAlexaff
O. Daniel Vera, Ramesh C. Mishra, Darrell D. Belke, Liam Hamm, Heike Wulff, Andrew P. Braun

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineElectrical impedance myographyEjection fractionApolipoprotein EInternal medicineEndothelial dysfunctionAortaEndocrinologyThoracic aortaCardiologyVasodilationHeart failure

Abstract

fetched live from OpenAlex

Atherosclerosis is a major risk factor for cardiovascular disease and is induced by hyperlipidemia and endothelial dysfunction (ED), leading to fatty plaque formation and impaired vascular function. Pharmacological activation of endothelial Ca2+-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, 8-week-old male Apoe-/- mice on a high fat diet (HFD) were administered one of three daily oral treatments for 12 weeks: the KCa channel activator 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. Cardiac function was assessed by echocardiography under isoflurane anesthesia. Atherosclerotic lesions in the thoracic aorta were visualized by Oil-Red-O staining, and abdominal aortic contractility and relaxation were measured by wire myography in Apoe-/- mice and age/sex-matched wild-type C57BL/6 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. Oil-Red-O staining of the thoracic aorta and aortic arch revealed fatty plaque formation in Apoe-/- mice (~15% of area) compared with WT controls (<1% of area), but neither SKA-31 nor senicapoc treatments reduced plaque formation vs. vehicle. Phenylephrine (PE)-evoked contraction of abdominal aortic rings was similar in WT and vehicle/drug treated Apoe-/- mice. Conversely, endothelium-dependent, acetylcholine-induced relaxation was significantly enhanced in PE-constricted aortic rings from SKA-31-treated mice (mean ± SD, 75.6 ± 21.1 %) vs. vehicle (51.2 ± 11.9 %, p=0.0024). Acetylcholine-induced relaxation was not improved by senicapoc administration. Relaxation induced by the smooth muscle vasodilator sodium nitroprusside was similar in WT and vehicle/drug treated Apoe-/- aortic rings. Summary: SKA-31 administration improved aortic endothelial function without reducing lesion density. Future studies will examine how SKA-31 improves endothelium-dependent relaxation and whether enhanced KCa channel activity (with SKA-31) in combination with lipid-lowering drugs (e.g., statins) can mitigate atherosclerosis more effectively. Enhancement of endothelial KCa channel activity may thus represent a viable stand-alone or “add-on” strategy to reduce morbidity and mortality associated with ED and atherosclerosis. Funding provided by the CIHR and NSERC This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.249
Teacher spread0.230 · 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
Published2023
Admission routes1
Has abstractyes

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