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Record W2945706906 · doi:10.3390/v11050461

Equine Parvovirus-Hepatitis Frequently Detectable in Commercial Equine Serum Pools

2019· article· en· W2945706906 on OpenAlexaboutno aff
Toni Luise Meister, Birthe Tegtmeyer, Alexander Postel, Jessika‐M. V. Cavalleri, Daniel Tödt, Alexander Stang, Eike Steinmann

Bibliographic record

VenueViruses · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsVirologyParvovirusBiologyVirusTransmission (telecommunications)AntibodyHepatitisImmunology

Abstract

fetched live from OpenAlex

An equine parvovirus-hepatitis (EqPV-H) has been recently identified in association with equine serum hepatitis, also known as Theiler’s disease. This disease was first described by Arnold Theiler in 1918 and is often observed after applications with blood products in equines. So far, the virus has only been described in the USA and China. In this study, we evaluated the presence of EqPV-H in several commercial serum samples to assess the potential risk of virus transmission by equine serum-based products for medical and research applications. In 11 out of 18 commercial serum samples, EqPV-H DNA was detectable with a viral load up to 105 copies/mL. The same serum batches as well as three additional samples were also positive for antibodies against the EqPV-H VP1 protein. The countries of origin with detectable viral genomes included the USA, Canada, New Zealand, Italy, and Germany, suggesting a worldwide distribution of EqPV-H. Phylogenetic analysis of the EqPV-H NS1 sequence in commercial serum samples revealed high similarities in viral sequences from different geographical areas. As horse sera are commonly used for the production of anti-sera, which are included in human and veterinary medical products, these results implicate the requirement for diagnostic tests to prevent EqPV-H transmission.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.030
GPT teacher head0.245
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2019
Admission routes1
Has abstractyes

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