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Circulating miRNAs as Biomarkers for Cardiac Disease

2010· book-chapter· en· W2611791402 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-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsDiseasemicroRNAMedicineMyocardial infarctionBiomarkerHeart failureProstate cancerDiagnostic biomarkerCoronary artery diseaseBioinformaticsInternal medicineCancerCardiologyBiologyGene

Abstract

fetched live from OpenAlex

This chapter aims to discuss recent advances of circulating miRNAs as new and promising biomarkers for cardiac disease. The elucidation of miRomes between diseased and normal cardiovascular tissues or between different cardiovascular disease types, stages and grades, gives the chance to identify the miRNAs most probably involved in cardiovascular disease and to establish new diagnostic and prognostic markers. Recent findings suggest that circulating miRNAs may be plasma biomarkers for the diagnosis of lung, colorectal, and prostate cancers. These findings have been also tested for cardiovascular disease. miRNAs are present in human plasma in a remarkably stable form that is protected from endogenous RNase activity. The levels of miRNAs in serum are reproducible and consistent among individuals of the same species. In particular, blood miR-1, miR-133, miR- 208a and miR-499 have been suggested as biomarkers of acute myocardial infarction; miR-208, miR-423-5p and some other miRNAs in the circulation are correlated with heart failure; and miR-122, miR-124 and miR-133 may be used to predict cerebral artery occlusion stroke.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.939
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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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