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Record W4223658130 · doi:10.3390/v14040785

Combining a Universal Capture Ligand and Pan-Serotype Monoclonal Antibody to Develop a Pan-Serotype Lateral Flow Strip Test for Foot-and-Mouth Disease Virus Detection

2022· article· en· W4223658130 on OpenAlexafffund
Ming Yang, Dmytro Zhmendak, Valérie Mioulet, Donald P. King, Alison Burman, Charles Nfon

Bibliographic record

VenueViruses · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsFoot-and-mouth disease virusVirologySerotypeMonoclonal antibodyFoot-and-mouth diseaseVirusBiologyRecombinant DNAAntibodyAntigenImmunology

Abstract

fetched live from OpenAlex

Foot-and-mouth disease virus (FMDV) causes FMD, a highly contagious disease of cloven-hoofed animals including cattle, goats, pigs and sheep. Rapid detection of FMDV is critical to limit the devastating economic losses due to FMD. Current laboratory methods for FMDV detection such as virus isolation, real-time reverse transcription PCR and antigen detection enzyme-linked immunosorbent assay (AgELISA) are labor-intensive, requiring trained personnel and specialized equipment. We present the development and validation of a pan-serotype lateral flow strip test (LFST) that uses recombinant bovine integrin αvβ6 as a universal capture ligand and a pan-serotype monoclonal antibody (mAb) to detect FMDV. The LFST detected all seven FMDV serotypes, where the diagnostic sensitivity was comparable to the AgELISA, and the diagnostic specificity was 100% without cross-reactivity to other viruses causing vesicular disease in livestock. This rapid test will be useful for on-site FMDV detection, as well as in laboratories in endemic countries where laboratory resources are limited.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.455

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 studies0.0010.000
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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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