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Record W2860296849 · doi:10.1093/infdis/jiy330

Serological Evidence for the Circulation of Ebolaviruses in Pigs From Sierra Leone

2018· article· en· W2860296849 on OpenAlexaff
Kerstin Fischer, Juliet Jabaty, Roland Suluku, Allison Groseth, Sarah K. Fehling, Anne Balkema‐Buschmann, Bashiru M. Koroma, Kristina M. Schmidt, Christine Atherstone, Hana M. Weingartl, Thomas C. Mettenleiter, Martin H. Groschup, Thomas Hoenen, Sandra Diederich

Bibliographic record

VenueThe Journal of Infectious Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsCanadian Food Inspection Agency
FundersBundesamt für LandwirtschaftBundesministerium für Ernährung und LandwirtschaftDeutsche Forschungsgemeinschaft
KeywordsSierra leoneEbolavirusEbola virusVirologyBiologyTransmission (telecommunications)OutbreakSerologyAntibodyVirusNatural reservoirNucleoproteinImmunology

Abstract

fetched live from OpenAlex

Many human ebolavirus outbreaks have been linked to contact with wildlife including nonhuman primates and bats, which are assumed to serve as host species. However, it is largely unknown to what extent other animal species, particularly livestock, are involved in the transmission cycle or act as additional hosts for filoviruses. Pigs were identified as a susceptible host for Reston virus with subsequent transmission to humans reported in the Philippines. To date, there is no evidence of natural Ebola virus (EBOV) infection in pigs, although pigs were shown to be susceptible to EBOV infection under experimental settings. To investigate the potential role of pigs in the ecology of EBOV, we analyzed 400 porcine serum samples from Sierra Leone for the presence of ebolavirus-specific antibodies. Three samples reacted with ebolavirus nucleoproteins but had no neutralizing antibodies. Our results (1) suggest the circulation of ebolaviruses in swine in Sierra Leone that are antigenically related but not identical to EBOV and (2) could represent undiscovered ebolaviruses with unknown pathogenic and/or zoonotic potential.

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)0.001
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 score0.029
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.099
GPT teacher head0.396
Teacher spread0.298 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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