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Record W3047743354 · doi:10.3390/ijms21165677

The Role of the Liver-Specific microRNA, miRNA-122 in the HCV Replication Cycle

2020· review· en· W3047743354 on OpenAlexafffund
Rasika D Kunden, Juveriya Qamar Khan, Sarah Ghezelbash, Joyce A. Wilson

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversities Space Research AssociationUniversity of Saskatchewan
KeywordsInternal ribosome entry sitemicroRNAHepatitis C virusUntranslated regionBiologyMiR-122Three prime untranslated regionViral replicationVirologyRNAVirusMessenger RNATranslation (biology)GeneCell biologyGenetics

Abstract

fetched live from OpenAlex

Hepatitis C virus (HCV) replication requires annealing of a liver specific microRNA, miR-122 to 2 sites on 5' untranslated region (UTR). While, microRNAs downregulate gene expression by binding to the 3' untranslated region of the target mRNA, in this case, the microRNA anneals to the 5'UTR of the viral genomes and upregulates the viral lifecycle. In this review, we explore the current understandings of the mechanisms by which miR-122 promotes the HCV lifecycle, and its contributions to pathogenesis. Annealing of miR-122 has been reported to (a) stimulate virus translation by promoting the formation of translationally active internal ribosome entry site (IRES) RNA structure, (b) stabilize the genome, and (c) induce viral genomic RNA replication. MiR-122 modulates lipid metabolism and suppresses tumor formation, and sequestration by HCV may influence virus pathogenesis. We also discuss the possible use of miR-122 as a biomarker for chronic hepatitis and as a therapeutic target. Finally, we discuss roles for miR-122 and other microRNAs in promoting other viruses.

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 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 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 score0.991
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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 teacher head, 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

Citations67
Published2020
Admission routes2
Has abstractyes

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