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Record W4412981240 · doi:10.1016/j.microb.2025.100486

Detection of equine infectious anemia viral genome in equids serum samples in Canada

2025· article· en· W4412981240 on OpenAlexafffundabout
Sarah-Jo Paquette, Tara Furukawa-Stoffer, Carolyn James, Nariman Shahhosseini

Bibliographic record

VenueThe Microbe · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of LethbridgeCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsEquine infectious anemiaVirologyGenomeBiologyAnemiaMedicineGeneticsVirusGeneInternal medicine

Abstract

fetched live from OpenAlex

Serum is a suboptimal matrix for detecting Equine Infectious Anemia virus (EIAV) compared to whole blood (WB), which contains mononuclear cells where the virus primarily resides. WB and spleen samples are generally more reliable for detecting the viral genome. However, at the Canadian Food Inspection Agency (CFIA), only archived serum samples are available for future genotyping efforts. Given this limitation, the present study evaluated the feasibility of detecting EIAV RNA in archived serum samples from equids previously confirmed as seropositive for EIAV using reverse transcription real-time PCR (RT-qPCR). EIAV RNA was detected in of the majority of samples (n= 56, 72.7%), establishing proof-of-concept that the virus can be identified in serum, the only sample type available at CFIA. These findings suggest that further methodological development may enhance detection sensitivity from this matrix. Furthermore, the study highlights the potential for archived sera to support future genotyping projects and improve understanding of circulating EIAV strains in Canada.

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 score0.804
Threshold uncertainty score0.179

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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