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Record W3044868940 · doi:10.1186/s10020-020-00196-w

Long non-coding RNA H19 in atherosclerosis: what role?

2020· review· en· W3044868940 on OpenAlexaff
Xian Shi, Yating Wei, Heng Li, Ting Jiang, Xi‐Long Zheng, Kai Yin, Guo-Jun Zhao

Bibliographic record

VenueMolecular Medicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsAngiogenesisBiologyLong non-coding RNAmicroRNARNANon-coding RNALipid metabolismGene expressionGeneCell biologyCancer researchComputational biologyGeneticsEndocrinology

Abstract

fetched live from OpenAlex

Atherosclerosis (AS) is widely accepted to be a multistep pathophysiological process associated with several other processes such as angiogenesis and inflammatory response. Long non-coding RNAs (lncRNAs) are non-protein coding RNAs (more than 200 nucleotides in length) and can regulate gene expression at the transcriptional and post-transcriptional levels. Recent studies suggest that lncRNA-H19 plays important roles in the regulation of angiogenesis, adipocyte differentiation, lipid metabolism, inflammatory response, cellular proliferation and apoptosis. In this review, we primarily discuss the roles of lncRNA-H19 in atherosclerosis-related pathophysiological processes and the potential mechanisms by which lncRNA-H19 regulates the development of atherosclerosis, to help provide a better understanding of the biological functions of lncRNA-H19 in atherosclerosis.

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)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.025
GPT teacher head0.327
Teacher spread0.301 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

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