MétaCan
Menu
Back to cohort
Record W2947317752 · doi:10.1186/s12917-019-1927-4

Development and evaluation of a real-time RT-PCR and a field-deployable RT-insulated isothermal PCR for the detection of Seneca Valley virus

2019· article· en· W2947317752 on OpenAlex
Jianqiang Zhang, Charles Nfon, Chuan‐Fu Tsai, Chien-Hsien Lee, Lindsay Fredericks, Qi Chen, Avanti Sinha, Sarah A. Bade, Karen M. Harmon, Pablo Piñeyro, Phillip C. Gauger, Yun‐Long Tsai, Hwa-Tang Thomas Wang, Pei‐Yu Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Veterinary Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Food Inspection Agency
FundersIowa State University
KeywordsVirologyVesicular StomatitisVesicular stomatitis virusVirusBiologyReal-time polymerase chain reactionNucleic acidRNAGene

Abstract

fetched live from OpenAlex

Seneca Valley virus (SVV) has emerged in multiple countries in recent years. SVV infection can cause vesicular lesions clinically indistinguishable from those caused by other vesicular disease viruses, such as foot-and-mouth disease virus (FMDV), swine vesicular disease virus (SVDV), vesicular stomatitis virus (VSV), and vesicular exanthema of swine virus (VESV). Sensitive and specific RT-PCR assays for the SVV detection is necessary for differential diagnosis. Real-time RT-PCR (rRT-PCR) has been used for the detection of many RNA viruses. The insulated isothermal PCR (iiPCR) on a portable POCKIT™ device is user friendly for on-site pathogen detection. In the present study, SVV rRT-PCR and RT-iiPCR were developed and validated. Neither the SVV rRT-PCR nor the RT-iiPCR cross-reacted with any of the vesicular disease viruses (20 FMDV, two SVDV, six VSV, and two VESV strains), classical swine fever virus (four strains), and 15 other common swine viruses. Analytical sensitivities of the SVV rRT-PCR and RT-iiPCR were determined using serial dilutions of in vitro transcribed RNA as well as viral RNA extracted from a historical SVV isolate and a contemporary SVV isolate. Diagnostic performances were further evaluated using 125 swine samples by two approaches. First, nucleic acids were extracted from the 125 samples using the MagMAX™ kit and then tested by both RT-PCR methods. One sample was negative by the rRT-PCR but positive by the RT-iiPCR, resulting in a 99.20% agreement (124/125; 95% CI: 96.59–100%, κ = 0.98). Second, the 125 samples were tested by the taco™ mini extraction/RT-iiPCR and by the MagMAX™ extraction/rRT-PCR system in parallel. Two samples were positive by the MagMAX™/rRT-PCR system but negative by the taco™ mini/RT-iiPCR system, resulting in a 98.40% agreement (123/125; 95% CI: 95.39–100%, κ = 0.97). The two samples with discrepant results had relatively high CT values. The SVV rRT-PCR and RT-iiPCR developed in this study are very sensitive and specific and have comparable diagnostic performances for SVV RNA detection. The SVV rRT-PCR can be adopted for SVV detection in laboratories. The SVV RT-iiPCR in a simple field-deployable system could serve as a tool to help diagnose vesicular diseases in swine at points of need.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.204

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)0.0000.000

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 head0.218
GPT teacher head0.372
Teacher spread0.154 · 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