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Record W1755891760 · doi:10.2174/97816080518471100101

MicroRNAs and Cardiovascular Disease

2010· book· en· W1755891760 on OpenAlex
Zhiguo Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBENTHAM SCIENCE PUBLISHERS eBooks · 2010
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsDiseasemicroRNAMedicineBiologyInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Micro RNAs (miRNAs) are known to play an important role in gene expression regulation. Owing to their ever-increasing implications in the control of various biological and pathological processes, miRNAs have now been considered novel biomarkers of various human diseases including, cancer, viral disease, cardiovascular disorders, metabolic disturbances, etc. Unique expression profiles have been associated with specific pathological states. Cardiovascular disease is among the main causes of mortality in developed countries. The pathological process of the heart is associated with altered expression profile of genes that are important for cardiac function. The implications of miRNAs in the pathological process of the cardiovascular system have recently been recognized, and the research on miRNAs in relation to cardiovascular disease is now in rapid progress. The aim of this ebook is to present updated research on the subject with analyses from published reports. Micro RNAs and Cardiovascular Disease caters to the growing interest and information needs of cardiac clinicians and gene expression researchers.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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