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Record W2794797190 · doi:10.3389/fcimb.2018.00117

Lethal Zika Virus Disease Models in Young and Older Interferon α/β Receptor Knock Out Mice

2018· article· en· W2794797190 on OpenAlexaff
Andrea Marzi, Jackson Emanuel, Julie Callison, Kristin L. McNally, Nicolette Arndt, Spencer Chadinha, Cynthia Martellaro, Rebecca Rosenke, Dana Scott, David Safronetz, Stephen S. Whitehead, Sonja M. Best, Heinz Feldmann

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2018
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesNHLBI Division of Intramural ResearchNational Institutes of HealthDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsLethalityZika virusVirologyInterferonDiseaseBiologyVirusMedicineImmunologyPathologyGenetics

Abstract

fetched live from OpenAlex

The common small animal disease models for Zika virus (ZIKV) are mice lacking the interferon responses, but infection of interferon receptor α/β knock out (IFNAR-/-) mice is not uniformly lethal particularly in older animals. Here we sought to advance this model in regard to lethality for future countermeasure efficacy testing against more recent ZIKV strains from the Asian lineage, preferably the American sublineage. We first infected IFNAR-/- mice subcutaneously with the contemporary ZIKV-Paraiba strain resulting in predominantly neurological disease with ~50% lethality. Infection with ZIKV-Paraiba by different routes established a uniformly lethal model only in young mice (4-weeks old) upon intraperitoneal infection. However, intraperitoneal inoculation of ZIKV-French Polynesia resulted in uniform lethality in older IFNAR-/- mice (10-12-weeks old). In conclusion, we have established uniformly lethal mouse disease models for efficacy testing of antivirals and vaccines against recent ZIKV strains representing the Asian lineage.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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 designObservational
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

Citations22
Published2018
Admission routes1
Has abstractyes

Explore more

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