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Record W4413026209 · doi:10.3390/pathogens14080767

Evaluation of Spleen Swabs for Sensitive and High-Throughput Detection of Classical Swine Fever Virus

2025· article· en· W4413026209 on OpenAlexaff
Orie Hochman, Kalhari Goonewardene, Chungwon J. Chung, Aruna Ambagala

Bibliographic record

VenuePathogens · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of ManitobaUniversity of CalgaryUniversity of SaskatchewanCanadian Food Inspection Agency
FundersNational Pork Board
KeywordsSpleenClassical swine feverVirologyVirusBiologyPolymerase chain reactionReal-time polymerase chain reactionImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Despite intensive eradication efforts, classical swine fever (CSF) remains endemic across South America, Europe, Asia, and the Caribbean, highlighting the need for more effective surveillance and detection methods. Reverse-transcription real-time polymerase chain reaction (RRT-PCR) is the fastest, and most sensitive assay for detecting CSF virus (CSFV) genomic material. Previously, we demonstrated that spleen swabs outperformed spleen homogenates for the detection of ASFV genomic material by RRT-PCR. In this study, we compared CSFV genome detection in paired spleen homogenates and spleen swabs generated using 49 frozen and 33 fresh spleen samples collected from experimentally inoculated pigs with acute infection. The results show that the CSFV genome detection in spleen swabs is comparable to that in spleen homogenates. The study also demonstrated that the CSFV genomic material can be detected in spleen swabs during early CSFV infections, and the viruses can be successfully isolated from the swabs. The use of spleen swabs instead of spleen tissue homogenates for CSF detection will reduce labor, decrease costs associated with reporting, and increase the diagnostic throughput.

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)0.000
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 score0.950
Threshold uncertainty score0.103

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.287
Teacher spread0.242 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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