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Record W2031798786 · doi:10.2217/clp.09.73

Metabolic shifts during cardiac hypertrophy

2009· article· en· W2031798786 on OpenAlexaff
Meera Kaur, Paramjit S. Tappia

Bibliographic record

VenueClinical Lipidology · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsSt. Boniface Hospital
FundersHospital Research Foundation
KeywordsInternal medicineEndocrinologyHeart failureGlycolysisMuscle hypertrophyBiologyAnabolismBeta oxidationLipid metabolismCarbohydrate metabolismHypoxia (environmental)MetabolismChemistryMedicine

Abstract

fetched live from OpenAlex

Evaluation of: Krishnan J, Suter M, Windak R et al.: Activation of a HIF‑1a–PPAR‑a axis underlies the integration of glycolytic and lipid anabolic pathways in pathologic cardiac hypertrophy. Cell. Metab. 9, 512–524 (2009). During the development of cardiac hypertrophy and progression to heart failure, the myocardial energy source switches from fatty acid oxidation to glycolysis; a process that is a reversion to the fetal energy substrate preference pattern. Alterations in cardiac metabolism in response to substrate availability appears to involve changes in the transcriptional control of genes implicated in the transport and metabolism of fatty acids and glucose, which are mainly regulated by a class of transcription factors termed PPARs. The transcriptional activation of glucose transporters and glycolytic enzymes is also mediated by hypoxia‑inducible factor‑1 (HIF‑1). Chronic activation of the HIF‑1 pathway in the heart is considered to have an adverse outcome. Thus, it can be suggested that chronic activation of the HIF pathway in hypertrophied hearts is maladaptive and contributes to cardiac degeneration and progression to heart failure. In this paper, a model in which activation of the HIF‑1a–PPAR‑g axis by pathologic stress is proposed to underlie the key changes in cell metabolism that are characteristic of and contribute to the development of cardiac hypertrophy and its transition to heart failure.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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 designObservational
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

Citations6
Published2009
Admission routes1
Has abstractyes

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