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Record W3164955396 · doi:10.1139/facets-2020-0081

α<sub>1</sub>-adrenergic stimulation increases ventricular action potential duration in the intact mouse heart

2021· article· en· W3164955396 on OpenAlexaffvenue
William Joyce, Koen T. Scholman, Bjarke Jensen, Tobias Wang, Bastiaan J. Boukens

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStimulationPhenylephrineInternal medicinePhentolamineAdrenergicInotropePropranololEndocrinologyAdrenergic receptorPerfusionMedicineReceptorChemistryBlood pressure

Abstract

fetched live from OpenAlex

The role of α 1 -adrenergic receptors (α-ARs) in the regulation of myocardial function is less well-understood than that of β-ARs. Previous reports in the mouse heart have described that α 1 -adrenergic stimulation shortens action potential duration in isolated cells or tissues, in contrast to prolongation of the action potential reported in most other mammalian hearts. It has since become appreciated, however, that the mouse heart exhibits marked variation in inotropic response to α 1 -adrenergic stimulation between ventricles and even individual cardiomyocytes. We investigated the effects of α 1 -adrenergic stimulation on action potential duration at 80% of repolarization in the right and left ventricles of Langendorff-perfused mouse hearts using optical mapping. In hearts under β-adrenergic blockade (propranolol), phenylephrine or noradrenaline perfusion both increased action potential duration in both ventricles. The increased action potential duration was partially reversed by subsequent perfusion with the α-adrenergic antagonist phentolamine (1 μmol L −1 ). These data show that α 1 -receptor stimulation may lead to a prolonging of action potential in the mouse heart and thereby refine our understanding of how action potential duration adjusts during sympathetic stimulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.367
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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 teacher head, 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

Citations2
Published2021
Admission routes2
Has abstractyes

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