PCR Primers for Screening Food for Verotoxin-Producing Escherichia coli, Inclusive of Three vt1 and Seven vt2 Subtypes
Bibliographic record
Abstract
ABSTRACT: Verotoxin-producing Escherichia coli (VTEC; also known as Shiga toxin-producing E. coli) is a significant cause of foodborne illnesses around the world. Due to the serological and genomic diversity of VTEC, methods of detection for VTEC in food samples require detection of verotoxin or its gene vt (also known as stx). The current taxonomy of vt identifies three vt1 (a, c, d) and seven vt2 (a to g) subtypes. PCR detection of vt is convenient and rapid, but protocols may not detect all currently identified variants or subtypes of vt. The Health Canada Compendium of Analytical Methods protocol for the analysis of food for VTEC is MFLP-52. MFLP-52 includes a VT Screening PCR that is used to determine the presumptive presence of VTEC by the detection of vt in food enrichments and to differentiate VTEC from other isolates. The VT Screening PCR was developed prior to the establishment of the current vt taxonomy. An evaluation of VT Screening PCR for detection of the 10 established vt subtypes was performed, and it was discovered that the method could not detect subtypes vt1d and vt2f. Additional primers and a modified protocol were developed, and the modified VT Screening PCR was tested against an inclusivity panel of 50 VTEC strains, including representatives of 10 vt subtypes, and an exclusivity panel of 30 vt-negative E. coli from various sources, to ensure specificity. The reliability of MFLP-52 with the modified VT Screening PCR was assessed by analysis of four priority food matrices (ground beef, lettuce, cheese, and apple cider) inoculated with a VTEC strain at 2 to 5 CFU/25 g. The modified VT Screening PCR was determined to be able to detect all 10 vt subtypes and reliably detect the presence of VTEC in all tested food enrichments.
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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 | 0.001 |
| 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".