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Development of a rapid, internally controlled, two target, real-time RT-PCR for detection of measles virus

2021· article· en· W3211245612 on OpenAlexaff
Vanessa Zubach, Alberto Severini, Joanne Hiebert

Bibliographic record

VenueJournal of Virological Methods · 2021
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsMeasles virusVirologyBiologyMeaslesMultiplexNucleoproteinMononegaviralesVirusParamyxoviridaeVaccinationMorbillivirusHemagglutinin (influenza)GenotypeReal-time polymerase chain reactionGeneViral diseaseGenetics

Abstract

fetched live from OpenAlex

Vaccination has greatly reduced global measles incidence, however measles remains endemic in many regions worldwide. Measles surveillance relies on high performance molecular detection of the virus. We have developed and validated a multiplex rRT-PCR assay for the detection of measles virus. The assay includes three independent probes with unique reporter dyes for the simultaneous detection of the measles hemagglutinin gene, nucleoprotein gene and endogenous RNaseP control. Using dilution series of synthetic RNAs the limits of detection were determined to be approximately 20 copies of measles RNA. The assay is extremely reproducible with very low intra-assay and inter-assay coefficients of varation for both the N and the H targets. After testing 68 confirmed measles positive and 86 measles negative archival clinical samples our data shows the multiplex assay has a sensitivity and specificity of 100 %, and a 100 % concordance with the expected results. No cross reactivity was identified with clinical specimens positive for six other viruses. According to the WHO, currently only the B3, D4, D8, H1 measles genotypes of the 24 recognized genotypes continue to circulate and this new multiplex assay successfully detected all four of those genotypes as well as six other genotypes.

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)0.003
metaresearch head score (Gemma)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.335
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.061
GPT teacher head0.414
Teacher spread0.353 · 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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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