A Simple Gold Nanoprobe Assay for the Identification of Staphylococcus Aureus, Listeria Monocytogenes and Salmonella Enteritidis in Food Specimens
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".