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Record W2606489965 · doi:10.1177/1040638717695609

Modification of two capripoxvirus quantitative real-time PCR assays to improve diagnostic sensitivity and include beta-actin as an internal positive control

2017· article· en· W2606489965 on OpenAlexaff
Amaresh Das, Ming Deng, Shawn Babiuk, Michael T. McIntosh

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsMultiplexVirologyReal-time polymerase chain reactionBiologyMolecular biologyPolymerase chain reactionVirusBioinformaticsGene

Abstract

fetched live from OpenAlex

Capripoxviruses (CaPVs), consisting of Sheeppox virus (SPV), Goatpox virus (GPV), and Lumpy skin disease virus (LSDV) species, cause economically significant diseases in sheep, goats, and cattle, respectively. Quantitative real-time polymerase chain reaction (qPCR) assays are routinely used for rapid detection of CaPVs in surveillance and outbreak management programs. We further modified and optimized 2 previously published CaPV qPCR assays, referred to as the Balinsky and Bowden assays, by changing commercial PCR reagents used in the tests. The modified assays displayed 100% analytical specificity and showed no apparent changes in analytical sensitivities for detection of CaPVs compared with the original assays. Diagnostic sensitivities, assessed using 50 clinical reference samples from experimentally infected sheep, goats, and cattle, improved from 82% to 92% for the modified Balinsky assay and from 58% to 82% for the modified Bowden assay. The modified qPCR assays were multiplexed for detection of beta-actin as an indicator for potential false-negative results. The multiplex modified qPCR assays exhibited the same diagnostic sensitivities as the singleplex assays suggesting their utility in the detection of CaPVs.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.346
Teacher spread0.302 · 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.

Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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