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Record W2912081141 · doi:10.1128/jvi.02299-18

Cytoplasm and Beyond: Dynamic Innate Immune Sensing of Influenza A Virus by RIG-I

2019· review· en· W2912081141 on OpenAlexafffund
Guanqun Liu, Yan Zhou

Bibliographic record

VenueJournal of Virology · 2019
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaPublic Health Agency of Canada
KeywordsBiologyInnate immune systemRIG-ICytoplasmInfluenza A virusVirologyCell biologyVirusViral replicationMDA5Immune systemRNAGeneGeneticsRNA interference

Abstract

fetched live from OpenAlex

Innate immune sensing of influenza A virus (IAV) requires retinoic acid-inducible gene I (RIG-I), a fundamental cytoplasmic RNA sensor. How RIG-I's cytoplasmic localization reconciles with the nuclear replication nature of IAV is poorly understood. Recent findings provide advanced insights into the spatiotemporal RIG-I sensing of IAV and highlight the contribution of various RNA ligands to RIG-I activation. Understanding a compartment-specific RIG-I-sensing paradigm would facilitate the identification of the full spectrum of physiological RIG-I ligands produced during IAV infection.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.019
GPT teacher head0.314
Teacher spread0.295 · 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

Citations22
Published2019
Admission routes2
Has abstractyes

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