Hydrolysis probe assays for the detection of pathogenic Enterohemorrhagic Escherichia coli: Multi-Country validation study
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) strains are foodborne pathogens frequently associated with outbreaks linked to red meat, posing significant global food safety concerns. While several commercial assays exist for detecting EHEC strains belonging to serogroups O26, O103, O111, and O121, they often fail to distinguish pathogenic strains from non-pathogenic strains lacking virulence genes. These non-pathogenic E. coli interfere with the accuracy of existing assays, resulting in false-positives rates between 81 - 100 % for beef products. This study aimed to standardize and validate four hydrolysis probe assays for the specific detection of pathogenic strains of E. coli O26, O103, O111, and O121. Primers and probes were designed to target single nucleotide polymorphisms conserved among pathogenic strains of each target serogroup. The standardized assays were validated using a comprehensive collection of pure culture and DNA samples (n = 248) from Canada, France, Switzerland, and the United States; laboratory-inoculated beef and spinach (n = 132), fractionally inoculated ground beef (n = 390), and samples from the federal red meat surveillance program (n = 166). The results demonstrated an overall accuracy of 94.8 % in predicting the virulence profile of pure culture strains. These four assays are a single-step PCR screening tool that can determine the presence of virulent strains. These assays provide food testing laboratories worldwide with a set of reliable methods for identifying pathogenic O26, O103, O111, and O121 strains. Thus, this assay will help to reduce product waste and financial losses caused by misleading positive results caused by presence of non-pathogenic strains.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".