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Record W2732329906 · doi:10.5539/jfr.v6n4p134

A Simple Gold Nanoprobe Assay for the Identification of Staphylococcus Aureus, Listeria Monocytogenes and Salmonella Enteritidis in Food Specimens

2017· article· en· W2732329906 on OpenAlexvenueno aff
Dimitra Houhoula, Ekatherina Charvalos, Spyros J. Konteles, Stamatis Koussissis, Vladimiros Lougovois, Joseph Papaparaskevas

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
Fundersnot available
KeywordsListeria monocytogenesSalmonellaStaphylococcus aureusSalmonella enteritidisBiologyMicrobiologyPathogenListeriaPolymerase chain reactionNanoprobegenomic DNAVirologyDNAGeneBacteriaGeneticsFluorescence

Abstract

fetched live from OpenAlex

In the present study, we developed a gold nanoprobe assay, which relies on the colorimetric differentiation of specific DNA sequences, based on different aggregation profiles. We evaluated the assay on DNA extracted from pathogen cultures and from contaminated food specimens. The targets used were the femA gene for the identification of Staphylococcus aureus, the hly gene of the Listeria monocytogenes listeriolysin and the invA sequence for Salmonella spp. Comparison was performed with the reference assay, as described in the respective ISO guidelines for each pathogen, and a direct PCR amplification and detection. The minimum detection limit of the assay was defined at 123 fg/μL DNA, for all species, lower than PCR detection. Specificity was 100%. Sensitivity was 100%, as compared το the reference method, whereas the sensitivity of PCR was 93.3%. The evaluated assay could be used as a sensitive screening method, which does not require major infrastructure, for the detection and identification of pathogens in food specimens. In addition, it can accommodate detection of multiplespecies,thus allowing multiplexingand expedite turnaround time.

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)0.000
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 score0.077
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 head0.087
GPT teacher head0.347
Teacher spread0.260 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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