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Record W3042928905 · doi:10.3390/v12070755

Innate Immune Sensing of Influenza A Virus

2020· review· en· W3042928905 on OpenAlexafffund
Gaurav Malik, Yan Zhou

Bibliographic record

VenueViruses · 2020
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPublic Health Agency of Canada
KeywordsInnate immune systemPattern recognition receptorRIG-IInterferonBiologyInfluenza A virusVirusVirologyImmune systemProinflammatory cytokinePyrin domainImmunologyMDA5Viral replicationInflammasomeInflammationGeneRNA interferenceGenetics

Abstract

fetched live from OpenAlex

Influenza virus infection triggers host innate immune response by stimulating various pattern recognition receptors (PRRs). Activation of these PRRs leads to the activation of a plethora of signaling pathways, resulting in the production of interferon (IFN) and proinflammatory cytokines, followed by the expression of interferon-stimulated genes (ISGs), the recruitment of innate immune cells, or the activation of programmed cell death. All these antiviral approaches collectively restrict viral replication inside the host. However, influenza virus also engages in multiple mechanisms to subvert the innate immune responses. In this review, we discuss the role of PRRs such as Toll-like receptors (TLRs), Retinoic acid-inducible gene I (RIG-I), NOD-, LRR-, pyrin domain-containing protein 3 (NLRP3), and Z-DNA binding protein 1 (ZBP1) in sensing and restricting influenza viral infection. Further, we also discuss the mechanisms influenza virus utilizes, especially the role of viral non-structure proteins NS1, PB1-F2, and PA-X, to evade the host innate immune responses.

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 science0.0010.001
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 head0.078
GPT teacher head0.351
Teacher spread0.273 · 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

Citations101
Published2020
Admission routes2
Has abstractyes

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