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Record W3037266749 · doi:10.1016/j.omtn.2020.06.022

Rapid Development of Targeting circRNAs in Cardiovascular Diseases

2020· review· en· W3037266749 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.

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Natural Science Foundation of China
KeywordsComputational biologyCircular RNABioinformaticsBiologyDiseasemicroRNAMedicinePathologyGeneticsGene

Abstract

fetched live from OpenAlex

Circular RNAs (circRNAs) are circularized, single-stranded RNAs that are covalently linked. With their abundance in tissues and developmental stage-specific expression, circRNAs participate in a variety of physiological and pathological processes. In this review, we discuss the development of circRNAs used as biomarkers and therapeutic targets for cardiovascular diseases (CVDs), focusing on recent discoveries and applications of exosomal circRNAs that highlight opportunities and challenges. Some studies have identified a spectrum of circRNAs that are differentially expressed in CVDs, while other studies further manipulated specific circRNA expression and showed an ameliorated pathogenic state such as ischemic injury, hypertrophy, and cardiac fibrosis. Studies and applications of circRNAs are being rapidly developed. We expect to see clinical use of circRNAs as biomarkers and targets for disease treatment in the near future.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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