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Record W1904530464 · doi:10.1002/ddr.21269

MicroRNAs and Noncoding RNAs in Hepatic Lipid and Lipoprotein Metabolism: Potential Therapeutic Targets of Metabolic Disorders

2015· review· en· W1904530464 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

VenueDrug Development Research · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsmicroRNABiologyLipid metabolismNon-coding RNAGeneLong non-coding RNAComputational biologyRNACell biologyBioinformaticsGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Noncoding RNAs and microRNAs (miRNAs) represent an important class of regulatory molecules that modulate gene expression. The role of miRNAs in diverse cellular processes such as cancer, apoptosis, cell differentiation, cardiac remodeling, and inflammation has been intensively explored. Recent studies further demonstrated the important roles of miRNAs and noncoding RNAs in modulating a broad spectrum of genes involved in lipid synthesis and metabolic pathways. This overview focuses on the role of miRNAs in hepatic lipid and lipoprotein metabolism and their potential as therapeutic targets for metabolic syndrome. This includes recent advances made in the understanding of their target pathways and the clinical development of miRNAs in lipid metabolic disorders.

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)0.002
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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.042
GPT teacher head0.353
Teacher spread0.311 · 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