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Record W2056324404 · doi:10.1094/pd-90-0185

Optimization and Application of a Multiplex RT-PCR System for Simultaneous Detection of Five Potato Viruses Using 18S rRNA as an Internal Control

2006· article· en· W2056324404 on OpenAlexaff
Zhiyou Du, Jishuang Chen, C. Hiruki

Bibliographic record

VenuePlant Disease · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyRibosomal RNAMultiplexPotato virus X18S ribosomal RNAVirologyMolecular biologyReverse transcription polymerase chain reactionRNAPotato virus YVirusMessenger RNAPlant virusGeneGenetics

Abstract

fetched live from OpenAlex

Search for a host RNA molecule appropriate as an internal control for reverse transcription-polymerase chain reaction (RT-PCR) detection of viruses in potato (Solanum tuberosum) was conducted. The 18S ribosomal RNA (rRNA) was compared with the commonly used nad2 mRNA in terms of detection sensitivity and degradation kinetics. Detection of 18S rRNA was 5 magnitudes more sensitive than that of nad2 mRNA. The 18S rRNA also displayed degradation kinetics more similar to that of Potato virus X (PVX). Based on this result, reaction components and cycling parameters were optimized for a multiplex RT-PCR protocol for simultaneous detection of five potato viruses using 18S rRNA as an internal control. The protocol simultaneously amplified cDNAs from Potato virus A, PVX, Potato virus Y, Potato leaf roll virus, Potato virus S, and 18S rRNA. The multiplex RT-PCR protocol was able to detect all viruses in different combinations. The technique was 100-fold greater for detection of PVX than that of commercial double-antibody sandwich-enzyme-linked immunosorbent assay (DAS-ELISA), and also could detect viruses in some samples that DAS-ELISA failed to detect. This multiplex RT-PCR technique demonstrates a higher sensitivity of virus detection than DAS-ELISA.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.466

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 studies0.0000.000
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations54
Published2006
Admission routes1
Has abstractyes

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