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Record W4306914747 · doi:10.1002/jmv.28243

Modulation of cellular machineries by Zika virus‐encoded proteins

2022· review· en· W4306914747 on OpenAlexaff
Shupeng Dong, Maggie Z. X. Xiao, Qiming Liang

Bibliographic record

VenueJournal of Medical Virology · 2022
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsZika virusVirologyBiologyVirusMicrocephalyViral replicationFlavivirusGenomeViral pathogenesisPathogenesisGeneGeneticsImmunology

Abstract

fetched live from OpenAlex

The strain of Zika virus (ZIKV) that circulated during the 2015 epidemic in Brazil has been associated with more than 2000 cases of microcephaly from September 2015 through November 2016. The viral genome determines the biology and pathogenesis of a virus and the virus employs its own gene products to evade host immune surveillance, manipulate cellular machineries, and establish efficient replication. Therefore, understanding the functions of virus-encoded protein not only aids the knowledge of ZIKV biology but also guides the development of anti-ZIKV drugs. In this review, we focus on 10 proteins encoded by ZIKV and summarize their functions in ZIKV replication and pathogenesis according to studies published in the past 6 years.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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