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Record W4206960053 · doi:10.3390/pathogens11020138

Evaluation of a Lateral Flow Assay for Rapid Detection of African Swine Fever Virus in Multiple Sample Types

2022· article· en· W4206960053 on OpenAlexafffund
Chukwunonso Onyilagha, Kelvin Nguyen, Pam Dachung Luka, Ularamu Gulak Hussaini, Adeyinka Adedeji, Theophilus Odoom, Aruna Ambagala

Bibliographic record

VenuePathogens · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of CalgaryCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsAfrican swine fever virusVirologyBiologyVirusTiterOutbreakHerdAfrican swine feverVirulenceReal-time polymerase chain reactionVeterinary medicineMedicineGene

Abstract

fetched live from OpenAlex

Antibody-based lateral flow assay (LFA) is a quick and inexpensive tool used to detect pathogens in field samples, especially in hard-to-reach remote areas that may have limited access to central laboratories during an outbreak or surveillance. In this study, we investigated the ability of a commercially available LFA, PenCheck®, to detect African swine fever virus (ASFV) in clinical samples derived from pigs infected with highly virulent ASFV strains. The assay was specific and positively identified the majority of pigs showing high fever during the early stages (between 3 and 5 days) of infection. PenCheck® LFA also detected ASFV in serum and tissue samples collected from pigs that succumbed to experimental ASFV infection and whole blood, plasma, and tissue samples from the field. The limit of detection of the assay was ASFV titer 107.80 TCID50/mL, corresponding to ASFV real-time PCR values below 23 Ct. Although the sensitivity of the assay is less than that of the laboratory-based real-time PCR assays, the results obtained with the PenCheck® LFA in this study suggest that it can be used as a herd-level, field-deployable, and easy-to-use diagnostic tool to identify ASF-affected farms when access to portable molecular assays or central laboratories is not possible.

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)0.002
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 score0.848
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.078
GPT teacher head0.268
Teacher spread0.190 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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