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Record W2163981048 · doi:10.1159/000098358

Role of Subcellular Remodeling in Cardiac Dysfunction due to Congestive Heart Failure

2007· review· en· W2163981048 on OpenAlexaff
Andrea P. Babick, Naranjan S. Dhalla

Bibliographic record

VenueMedical Principles and Practice · 2007
Typereview
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsHeart failureSarcolemmaMedicineSubcellular localizationCardiologyInternal medicineCardiac function curveCardiac dysfunctionVentricular remodelingCardiac muscleMyocyteCell biologyBiologyCytoplasm

Abstract

fetched live from OpenAlex

Although alterations in the size and shape of the heart (cardiac remodeling) are considered in explaining cardiac dysfunction during the development of congestive heart failure (CHF), there are several conditions including initial stages of cardiac hypertrophy, where cardiac remodeling has also been found to be associated with either an increased or no change in heart function. Extensive studies have indicated that cardiac dysfunction is related to defects in one or more subcellular organelles such as myofibrils, sarcoplasmic reticulum and sarcolemma, depending upon the stage of CHF. Such subcellular abnormalities in the failing hearts have been shown to occur at both genetic and protein levels. Blockade of the renin-angiotensin system has been reported to partially attenuate changes in subcellular protein, gene expression, functional activities and cardiac performance in CHF. These observations provide support for the role of subcellular remodeling (alterations in molecular and biochemical composition of subcellular organelles) in cardiac dysfunction in the failing heart. On the basis of existing knowledge, it appears that subcellular remodeling during the process of cardiac remodeling plays a major role in the development of cardiac dysfunction in CHF.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.060
GPT teacher head0.363
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2007
Admission routes1
Has abstractyes

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