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Record W2603591942 · doi:10.1111/tbed.12606

Detection of Zaire ebolavirus in swine: Assay development and optimization

2017· article· en· W2603591942 on OpenAlexafffund
Bradley Pickering, Brad Collignon, Gregory A. Smith, Peter Marszal, Gary Kobinger, Hana M. Weingartl

Bibliographic record

VenueTransboundary and Emerging Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaUniversité LavalCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsEbolavirusVirologyFiloviridaeEbola virusBiologyMarburg virusNucleoproteinNeutralizationAntibodyMononegaviralesVirusViral diseaseImmunology

Abstract

fetched live from OpenAlex

Ebolaviruses (family Filoviridae, order Mononegavirales) cause often fatal, haemorrhagic fever in primates including humans. Pigs have been identified as a species susceptible to Reston ebolavirus (RESTV) infection, with indicated transmission to humans in the Philippines; however, their role during Ebola outbreaks in Africa needs to be clarified. To perform surveillance studies, detection of ebolavirus requires a prerequisite validation of viral RNA and antibody detection methods in swine samples. These diagnostic tests also need to be suitable for deployment to low-level containment laboratories. In this study, we developed a set of tests for detection of antibodies against Zaire ebolavirus (EBOV) in swine. Recombinant EBOV nucleoprotein was produced using a baculovirus expression system for indirect ELISA development. Evaluation of this assay was performed using laboratory and field samples, achieving a diagnostic specificity of 99%. Importantly, the indirect ELISA was able to detect antibodies to EBOV at 7 dpi, 3 days earlier than virus neutralization tests (VNT). The format of the VNT in this work was modified to a microtitre plaque reduction neutralization assay (miPRNT) complemented with immunostaining to provide a more rapid and highly specific assay. Finally, a confirmatory immunoblot assay was generated to supplement the indirect ELISA results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.296

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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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

Citations14
Published2017
Admission routes2
Has abstractyes

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