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Record W4407665858 · doi:10.1080/17460794.2025.2467023

Innate immune evasion and general host shutoff strategies of human respiratory RNA viruses

2025· article· en· W4407665858 on OpenAlexafffund
Juliette Bougon, Denys A. Khaperskyy

Bibliographic record

VenueFuture Virology · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsEvasion (ethics)Innate immune systemVirologyImmune systemHost (biology)BiologyRNARespiratory systemImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

To replicate and spread efficiently, RNA viruses must inhibit the host immune signaling pathways and/or induce host shutoff and thereby evade host immunity. However, not all RNA viruses have the same consequences in terms of disease severity and ability to cause reinfections. What are the reasons for such differences? Are there any unique or common features of viral immune evasion? Answering these questions is imperative to elucidate the role of the balance between damaging pro-inflammatory and protective antiviral responses and to inform development of new therapeutic strategies. In this review, we provide a comparative overview of the knowledge regarding the innate immune evasion and general host shutoff strategies used by respiratory RNA viruses to enable their persistent spread in humans. We demonstrate that these viruses encode multiple and often redundant mechanisms of innate immune suppression, with potent general host shutoff being characteristic of viruses with wider host range and greater ability to cause reinfections. Accordingly, we argue that increased research effort in characterizing host shutoff mechanisms of different viruses and their contribution to virus fitness is needed to determine if targeting host shutoff represents a viable avenue for developing new antiviral therapies.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.382
Teacher spread0.347 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2025
Admission routes2
Has abstractyes

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