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Record W2322653941 · doi:10.1152/ajpheart.00419.2002

β-Adrenergic receptor blockade impairs NO-dependent dilation of large coronary arteries during exercise

2003· article· en· W2322653941 on OpenAlexaff
Masaki Okajima, Masayuki Takamura, Philippe Véquaud, Robert Parent, Michel Lavallée

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2003
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsPropranololConstrictionInternal medicineShear stressBlockadeEndocrinologyCardiologyMedicineReceptorChemistryMaterials science

Abstract

fetched live from OpenAlex

Shear stress-dependent nitric oxide (NO) formation prevents immoderate vascular constriction. We examined whether shear stress-dependent NO formation limits exercise-induced coronary artery constriction after beta-adrenergic receptor blockade in dogs. Control exercise led to increases (P < 0.01) in coronary blood flow (CBF) by 38 +/- 5 ml/min from 41 +/- 5 ml/min and in the external diameter of epicardial coronary arteries (CD) by 0.24 +/- 0.03 mm from 3.33 +/- 0.20 mm. CD and shear stress were linearly related. After propranolol, CD fell (P < 0.01) during exercise (0.08 +/- 0.03 from 3.23 +/- 0.19 mm), and the slope of the relationship between CD and shear stress was reduced (P < 0.01). This slope was not further altered by the additional blockade of NO formation. In propranolol-treated resting dogs, flow-dependent effects of intracoronary adenosine to mimic exercise-induced increases in shear stress (after propranolol) led to increases (P < 0.01) in CD (0.09 +/- 0.02 from 3.68 +/- 0.27 mm). Thus both shear stress-dependent NO formation and beta-adrenergic receptor activation are required to cause CD dilation during exercise. Suppression of beta-adrenergic receptor activation leads to impaired shear stress-dependent NO formation and allows alpha-adrenergic constriction to become dominant.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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

Citations12
Published2003
Admission routes1
Has abstractyes

Explore more

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