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Record W2990913926

Vesicular disease in pigs inoculated with a recent Canadian isolate of Senecavirus A.

2019· article· en· W2990913926 on OpenAlexaffabout
Kate Hole, Thanuja Ambagala, Charles Nfon

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Science Centre for Human and Animal HealthCanadian Food Inspection Agency
Fundersnot available
KeywordsInoculationOutbreakVirulenceVirologyVirusFecesDiseaseBiologyMedicineVeterinary medicineMicrobiologyImmunologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to investigate whether a virulent Canadian isolate of Senecavirus A (SVA) causes idiopathic vesicular disease (IVD) in pigs. Senecavirus A, which was first isolated in the United States in 2002 as Seneca Valley Virus, was linked to cases of porcine idiopathic vesicular disease in Canada in 2007 and in the United States in 2010. Since 2014, SVA outbreaks in Brazil, the US, Canada, China, Thailand, and Colombia point to an expanding global distribution and the need to study the pathogenicity of the virus. Unlike the prototype virus, recent US isolates of SVA have been shown to cause vesicular disease in pigs. We report vesicular disease in pigs following experimental inoculation with a 2016 Canadian isolate of SVA. All inoculated pigs developed vesicular lesions regardless of route of inoculation. Virus was detected in blood and oral fluids as well as on oral and fecal swabs. In addition, all pigs seroconverted to SVA by 6 days post-inoculation (DPI). This study confirms that recent Canadian isolates of SVA cause vesicular disease in pigs and highlights the importance of monitoring SVA for increased virulence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.864
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.176
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2019
Admission routes2
Has abstractyes

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