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Record W2888657086 · doi:10.3390/v10080440

The Diverse Roles of microRNAs at the Host–Virus Interface

2018· review· en· W2888657086 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueViruses · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchCanada Research ChairsMcGill University
KeywordsBiologymicroRNAVirusGeneViral replicationViral pathogenesisRNAVirologyGene expressionRegulation of gene expressionGeneticsComputational biology

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression at the post-transcriptional level. Through this activity, they are implicated in almost every cellular process investigated to date. Hence, it is not surprising that miRNAs play diverse roles in regulation of viral infections and antiviral responses. Diverse families of DNA and RNA viruses have been shown to take advantage of cellular miRNAs or produce virally encoded miRNAs that alter host or viral gene expression. MiRNA-mediated changes in gene expression have been demonstrated to modulate viral replication, antiviral immune responses, viral latency, and pathogenesis. Interestingly, viruses mediate both canonical and non-canonical interactions with miRNAs to downregulate specific targets or to promote viral genome stability, translation, and/or RNA accumulation. In this review, we focus on recent findings elucidating several key mechanisms employed by diverse virus families, with a focus on miRNAs at the host⁻virus interface during herpesvirus, polyomavirus, retroviruses, pestivirus, and hepacivirus infections.

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)0.000
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 score0.764
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

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 science0.0010.001
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 head0.037
GPT teacher head0.328
Teacher spread0.291 · 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