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miRNAs in Cardiac Hypertrophy and Heart Failure

2010· book-chapter· en· W1858885658 on OpenAlexaff
Zhiguo Wang

Bibliographic record

VenueBENTHAM SCIENCE PUBLISHERS eBooks · 2010
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsHeart failureMuscle hypertrophyCardiologyPathologicalmicroRNACardiac hypertrophyInternal medicineSudden cardiac deathCardiac function curveMedicineHypertrophic cardiomyopathyHeart diseaseBiologyGeneticsGene

Abstract

fetched live from OpenAlex

The aim of this chapter is to introduce the role of miRNAs in the pathological process of cardiac hypertrophy/heart failure. Cardiovascular disease is among the main causes of morbidity and mortality in developed countries. In response to stress, the adult heart undergoes remodelling process and hypertrophic growth to adapt to altered workloads and to compensate for the impaired cardiac function. Pathological hypertrophy results in loss of cardiac function and is the major predictor of heart failure and sudden death. Recent studies have established the role of miRNAs in cardiac hypertrophy/heart failure as causal factors or important regulators. miRNAs are aberrantly expressed in various animal models and in patients with heart failure. The miRNAs involved in cardiac hypertrophy/heart failure can in general be divided into two categories: anti-hypertrophic and pro-hypertrophic miRNAs. This chapter introduces the roles of miRNAs in experimental and clinical cardiac hypertrophy/heart failure. Detailed description is given of the well-studied pro-hypertrophic miRNAs miR-195, miR-208 and miR-23a, and anti-hypertrophic miRNAs miR-1, miR-133 and miR-9.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.214
Teacher spread0.207 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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