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Record W2023429580 · doi:10.1155/2015/508989

Vitamin D Potentiates the Inhibitory Effect of MicroRNA‐130a in Hepatitis C Virus Replication Independent of Type I Interferon Signaling Pathway

2015· article· en· W2023429580 on OpenAlexaff
Xiaoqiong Duan, Yujuan Guan, Yujia Li, Shan Chen, Shilin Li, Limin Chen

Bibliographic record

VenueMediators of Inflammation · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersScience and Technology Planning Project of Guangdong ProvinceGuangdong Science and Technology Department
KeywordsCalcitriolInterferonHepatitis C virusViral replicationInnate immune systemDownregulation and upregulationBiologyIn vitroPharmacologyCancer researchImmune systemChemistryCell biologyVirologyVitamin D and neurologyImmunologyVirusBiochemistryEndocrinologyGene

Abstract

fetched live from OpenAlex

Calcitriol, the bioactive metabolite of vitamin D, was reported to inhibit HCV production in a synergistic fashion with interferon, a treatment in vitro. Our previous study established that miR-130a inhibits HCV replication by restoring the host innate immune response. We aimed to determine whether there is additive inhibitory effect of calcitriol and miR-130a on HCV replication. Here we showed that calcitriol potentiates the anti-HCV effect of miR-130a in both Con1b replicon and J6/JFH1 culture systems. Intriguingly, this potentiating effect of calcitriol on miR-130a was not through upregulating the expression of cellular miR-130a or through increasing the miR-130a-mediated IFNα/β production. All these findings may contribute to the development of novel anti-HCV therapeutic strategies although the antiviral mechanism needs to be further investigated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations15
Published2015
Admission routes1
Has abstractyes

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