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Record W3044070898 · doi:10.1038/s41598-020-69054-3

Characterization of a novel STAT 2 knock-out hamster model of Crimean-Congo hemorrhagic fever virus pathogenesis

2020· article· en· W3044070898 on OpenAlexafffund
Charlene Ranadheera, Emelissa J. Valcourt, Bryce M. Warner, Guillaume Poliquin, Kyle Rosenke, Kathy L. Frost, Kevin Tierney, Greg Saturday, Jinxin Miao, Jonna B. Westover, Brian B. Gowen, Stephanie A. Booth, Heinz Feldmann, Zhongde Wang, David Safronetz

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesNHLBI Division of Intramural ResearchNational Institutes of HealthPublic Health AgencyPublic Health Agency of Canada
KeywordsPathogenesisImmunologyVirologyVirusBiologyInfectious disease (medical specialty)DiseaseViral pathogenesisMedicineViral replicationPathology

Abstract

fetched live from OpenAlex

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne pathogen causing a febrile illness in humans, which can progress to hemorrhagic manifestations, multi-organ failure, and death. Current mouse models of CCHFV infection reliably succumb to virus challenge but vary in their ability to reflect signs of disease similar to humans. In this study, we established a signal transducer and activator of transcription 2 (STAT2) knockout hamster model to expand the repertoire of animal models of CCHFV pathogenesis that can be used for therapeutic development. These hamsters demonstrated a systemic and lethal disease in response to infection. Hallmarks of human disease were observed including petechial rash, blood coagulation dysfunction, and various biochemistry and blood cell count abnormalities. Furthermore, we also demonstrated the utility of this model for anti-CCHFV therapeutic evaluation. The STAT2 knock-out hamster model of CCHFV infection may provide some further insights into clinical disease, viral pathogenesis, and pave the way for testing of potential drug and vaccine candidates.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.441

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 head0.062
GPT teacher head0.280
Teacher spread0.217 · 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

Citations83
Published2020
Admission routes2
Has abstractyes

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