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Record W2897721185 · doi:10.1002/jcp.27384

Circular RNA in cardiovascular disease

2018· review· en· W2897721185 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Cellular Physiology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsLondon Health Sciences CentreLawson Health Research InstituteUniversity of WaterlooWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCircular RNACoronary artery diseasePathogenesisDiseaseMedicineMyocardial infarctionCardiologyCardiomyopathyDiabetic cardiomyopathyFibrosisHeart failureBioinformaticsRNAInternal medicineBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Circular RNA (circRNA) are endogenous transcripts that display differential expression across species, developmental stages, and pathologies. Their lack of free ends confers increased stability when compared with linear transcripts, making them ideal candidates for future diagnostic biomarkers and therapeutic interventions. Increasing evidence has implicated circRNA in the pathogenesis of multiple cardiovascular diseases. In this paper, we summarize current understanding of circRNA biogenesis, properties, expression profiles, detection methods, functions, and their implication in cardiac pathologies including/ischemia reperfusion injury, myocardial infarction, cardiac senescence, cardiac fibrosis, cardiomyopathy, cardiac hypertrophy and heart failure, atherosclerosis, coronary artery disease, and aneurysm.

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: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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