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Record W3174073279 · doi:10.3389/fcvm.2021.702586

Circle the Cardiac Remodeling With circRNAs

2021· review· en· W3174073279 on OpenAlexfundno aff
Tiqun Yang, Tianxin Long, Tailai Du, Yili Chen, Yugang Dong, Zhan-Peng Huang

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsnot available
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceFundamental Research Funds for the Central UniversitiesTraditional Chinese Medicine Bureau of Guangdong ProvinceTerry Fox FoundationNational Natural Science Foundation of China
KeywordsPathogenesisMicrovesiclesBiologyCell biologyHeart failurePathologicalImmune systemmicroRNAVentricular remodelingTissue remodelingInflammationCancer researchImmunologyMedicineGenePathologyGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Cardiac remodeling occurs after the heart is exposed to stress, which is manifested by pathological processes such as cardiomyocyte hypertrophy and apoptosis, dendritic cells activation and cytokine secretion, proliferation and activation of fibroblasts, and finally leads to heart failure. Circular RNAs (circRNAs) are recently recognized as a specific type of non-coding RNAs that are expressed in different species, in different stages of development, and in different pathological conditions. Growing evidences have implicated that circRNAs play important regulatory roles in the pathogenesis of a variety of cardiovascular diseases. In this review, we summarize the biological origin, characteristics, functional classification of circRNAs and their regulatory functions in cardiomyocytes, endothelial cells, fibroblasts, immune cells, and exosomes in the pathogenesis of cardiac remodeling.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2021
Admission routes1
Has abstractyes

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