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Record W2790684895 · doi:10.1080/19336950.2018.1454814

Pharmacologic targeting of endothelial Ca<sup>2+</sup>-activated K<sup>+</sup> channels: A strategy to improve cardiovascular function

2018· review· en· W2790684895 on OpenAlexafffund
Cini Mathew John, Rayan Khaddaj‐Mallat, Grace George, Taeyeob Kim, Ramesh C. Mishra, Andrew P. Braun

Bibliographic record

VenueChannels · 2018
Typereview
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsVasodilationEndothelial dysfunctionBlood pressureVascular resistanceVascular toneInternal medicineMedicineEndotheliumAgonistChemistryCardiologyEndocrinologyPharmacologyReceptor

Abstract

fetched live from OpenAlex

Endothelial small and intermediate-conductance, Ca2+-activated K+ channels (KCa2.3 and KCa3.1, respectively) play an important role in the regulation of vascular function and systemic blood pressure. Growing evidence indicates that they are intimately involved in agonist-evoked vasodilation of small resistance arteries throughout the circulation. Small molecule activators of KCa2.x and 3.1 channels, such as SKA-31, can acutely inhibit myogenic tone in isolated resistance arteries, induce effective vasodilation in intact vascular beds, such as the coronary circulation, and acutely decrease systemic blood pressure in vivo. The blood pressure-lowering effect of SKA-31, and early indications of improvement in endothelial dysfunction suggest that endothelial KCa channel activators could eventually be developed into a new class of endothelial targeted agents to combat hypertension or atherosclerosis. This review summarises recent insights into the activation of endothelial Ca2+ activated K+ channels in various vascular beds, and how tools, such as SKA-31, may be beneficial in disease-related conditions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.317
Teacher spread0.266 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2018
Admission routes2
Has abstractyes

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