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Record W28910549 · doi:10.1139/cjpp-2017-0282

Communication Decency Act, 110 Stat. 56 (1996

2000· article· en· W28910549 on OpenAlexvenueno aff
Joel M. Gora

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsstatBusinessTelecommunicationsComputer scienceCell biologyBiologySignal transduction

Abstract

fetched live from OpenAlex

Cardiac hypertrophy often refers to the abnormal growth of heart muscle through a variety of factors. The mechanisms of cardiomyocyte hypertrophy have been extensively investigated using neonatal rat cardiomyocytes treated with phenylephrine. α-Enolase is a glycolytic enzyme with "multifunctional jobs" beyond its catalytic activity. Its possible contribution to cardiac dysfunction remains to be determined. The present study aimed to investigate the change of α-enolase during cardiac hypertrophy and explore its role in this pathological process. We revealed that mRNA and protein levels of α-enolase were significantly upregulated in hypertrophic rat heart induced by abdominal aortic constriction and in phenylephrine-treated neonatal rat cardiomyocytes. Furthermore, knockdown of α-enolase by RNA interference in cardiomyocytes mimicked the hypertrophic responses and aggravated phenylephrine-induced hypertrophy without reducing the total glycolytic activity of enolase. In addition, knockdown of α-enolase led to an increase of GATA4 expression in the normal and phenylephrine-treated cardiomyocytes. Our results suggest that the elevation of α-enolase during cardiac hypertrophy is compensatory. It exerts a catalytic independent role in protecting cardiomyocytes against pathological hypertrophy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.991

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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.

Study designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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