Direct Quantitation and Detection of Salmonellae in Biological Samples without Enrichment, Using Two-Step Filtration and Real-Time PCR
Bibliographic record
Abstract
A new two-step filtration protocol followed by a real-time PCR assay based on SYBR green I detection was developed to directly quantitate salmonellae in two types of biological samples: i.e., chicken rinse and spent irrigation water. Four prefiltration filters, one type of final filter, and six protocols for recovery of salmonellae from the final filter were evaluated to identify an effective filtration protocol. This method was then combined with a real-time PCR assay based on detection of the invA gene. The best results were obtained by subsequent filtration of 100 ml of chicken rinse or 100 ml of spent irrigation water through filters with pore diameters of >40 mum to remove large particles and of 0.22 microm to recover the Salmonella cells. After this, the Salmonella cells were removed from the filter by vortexing in 1 ml of physiological saline, and this sample was then subjected to real-time quantitative PCR. The whole procedure could be completed within 3 h from sampling to quantitation, and cell numbers as low as 7.5 x 10(2) CFU per 100-ml sample could be quantified. Below this limit, qualitative detection of concentrations as low as 2.2 CFU/100 ml sample was possible on occasion. This study has contributed to the development of a simple, rapid, and reliable method for quantitation of salmonellae in food without the need for sample enrichment or DNA extraction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".