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Record W2784142300 · doi:10.1139/cjm-2017-0504

Development of a recombinase polymerase amplification assay for <i>Vibrio parahaemolyticus</i> detection with an internal amplification control

2018· article· en· W2784142300 on OpenAlexvenueno aff
Huan-Lan Yang, Shuang Wei, Ravi Gooneratne, Anthony N. Mutukumira, Xuejun Ma, Shuze Tang, Xiyang Wu

Bibliographic record

VenueCanadian Journal of Microbiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsnot available
Fundersnot available
KeywordsVibrio parahaemolyticusRecombinase Polymerase AmplificationRecombinaseBiologyPolymerase chain reactionMolecular biologyMicrobiologyGeneBacteriaGenetics

Abstract

fetched live from OpenAlex

A novel RPA–IAC assay using recombinase polymerase and an internal amplification control (IAC) for Vibrio parahaemolyticus detection was developed. Specific primers were designed based on the coding sequence for the toxR gene in V. parahaemolyticus. The recombinase polymerase amplification (RPA) reaction was conducted at a constant low temperature of 37 °C for 20 min. Assay specificity was validated by using 63 Vibrio strains and 10 non-Vibrio bacterial species. In addition, a competitive IAC was employed to avoid false-negative results, which co-amplified simultaneously with the target sequence. The sensitivity of the assay was determined as 3 × 10 3 CFU/mL, which is decidedly more sensitive than the established PCR method. This method was then used to test seafood samples that were collected from local markets. Seven out of 53 different raw seafoods were detected as V. parahaemolyticus-positive, which were consistent with those obtained using traditional culturing method and biochemical assay. This novel RPA–IAC assay provides a rapid, specific, sensitive, and more convenient detection method for V. parahaemolyticus.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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