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Subacute and Chronic Effects of Quinapril on Cardiac Cytokine Expression, Remodeling, and Function After Myocardial Infarction in the Rat

2002· article· en· W2327641013 on OpenAlexaff
Wei Ge, Martin G. Sirois, Rong Qu, Peter Liu, Jean L. Rouleau

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of TorontoMount Sinai HospitalUniversity Health NetworkUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineQuinaprilMyocardial infarctionCardiac function curveInternal medicineHemodynamicsCytokineVentricular remodelingCardiologyHeart failureAngiotensin-converting enzymeACE inhibitorBlood pressure

Abstract

fetched live from OpenAlex

Inflammatory cytokines have been shown to have many cardiotoxic effects and to be activated in patients who have had a myocardial infarction (MI). Angiotensin-converting enzyme (ACE) inhibitors have been shown to have multiple beneficial effects after MI, but until now, their effects on cardiac cytokine expression were unknown. It was hypothesized that ACE inhibitors reduce cardiac cytokine expression and that this is associated with improved cardiac remodeling and hemodynamics. Rats had an MI created by coronary artery ligation and ACE inhibitors were started either early (day 1) or late (day 25) after MI and followed for a total of 28 days after MI. In the early-post-MI group, quinapril improved cardiac hemodynamics, improved ventricular remodeling, and prevented the increase in the expression of several cardiac cytokines (interleukin-1beta and -6) and reduced the cardiac expression of other cytokines (tumor necrosis factor-alpha and interleukin-5). The late introduction of quinapril (for 3 days) resulted in similar beneficial hemodynamic effects, and reductions in cardiac cytokines but did not result in improved cardiac remodeling. Thus, following MI, ACE inhibitors reduce cardiac cytokine expression both chronically and subacutely, an effect that may contribute to their beneficial effects after MI.

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)0.001
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 score0.636
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations34
Published2002
Admission routes1
Has abstractyes

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