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Record W4221074691 · doi:10.1128/jvi.00136-22

When Poly(A) Binding Proteins Meet Viral Infections, Including SARS-CoV-2

2022· review· en· W4221074691 on OpenAlexaff
Jie Gao, Yan‐Dong Tang, Wei Hu, Chunfu Zheng

Bibliographic record

VenueJournal of Virology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiologyViral replicationGeneVirologyGene expressionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DNA-binding proteinViral evolutionCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakGeneticsRegulation of gene expressionComputational biologyVirusGenomeTranscription factorInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Viruses have evolved diverse strategies to hijack the cellular gene expression system for their replication. The poly(A) binding proteins (PABPs), a family of critical gene expression factors, are viruses' common targets. PABPs act not only as a translation factor but also as a key factor of mRNA metabolism. During viral infections, the activities of PABPs are manipulated by various viruses, subverting the host translation machinery or evading the cellular antiviral defense mechanism. Viruses harness PABPs by modifying their stability, complex formation with other translation initiation factors, or subcellular localization to promote viral mRNAs translation while shutting off or competing with host protein synthesis. For the past decade, many studies have demonstrated the PABPs' roles during viral infection. This review summarizes a comprehensive perspective of PABPs' roles during viral infection and how viruses evade host antiviral defense through the manipulations of PABPs.

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 score0.010

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 science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.076
GPT teacher head0.383
Teacher spread0.308 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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