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Record W2121895001 · doi:10.1093/infdis/jis626

A Syrian Golden Hamster Model Recapitulating Ebola Hemorrhagic Fever

2012· article· en· W2121895001 on OpenAlexaff
Hideki Ebihara, Marko Zivcec, Donald J. Gardner, Darryl Falzarano, Rachel LaCasse, Rebecca Rosenke, Dan Long, Elaine Haddock, Elizabeth Fischer, Yoshihiro Kawaoka, Heinz Feldmann

Bibliographic record

VenueThe Journal of Infectious Diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institutes of Health
KeywordsViral hemorrhagic feverVirologyEbolavirusRodentHamsterCoagulopathyNonhuman primateEbola virusRodent modelImmunologyEbola Hemorrhagic FeverMedicineBiologyVirusInternal medicine

Abstract

fetched live from OpenAlex

Ebola hemorrhagic fever (EHF) is a severe viral infection for which no effective treatment or vaccine is currently available. While the nonhuman primate (NHP) model is used for final evaluation of experimental vaccines and therapeutic efficacy, rodent models have been widely used in ebolavirus research because of their convenience. However, the validity of rodent models has been questioned given their low predictive value for efficacy testing of vaccines and therapeutics, a result of the inconsistent manifestation of coagulopathy seen in EHF. Here, we describe a lethal Syrian hamster model of EHF using mouse-adapted Ebola virus. Infected hamsters displayed most clinical hallmarks of EHF, including severe coagulopathy and uncontrolled host immune responses. Thus, the hamster seems to be superior to the existing rodent models, offering a better tool for understanding the critical processes in pathogenesis and providing a new model for evaluating prophylactic and postexposure interventions prior to testing in NHPs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.312
Teacher spread0.287 · 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

Citations119
Published2012
Admission routes1
Has abstractyes

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