Molecular Detection of Non-O157 Shiga Toxin-Producing Escherichia coli (STEC) Directly from Stool Using Multiplex qPCR Assays
Bibliographic record
Abstract
Non-O157 Shiga toxin-producing E. coli (STEC) can cause outbreaks that have great economic and health impact. Since the implementation of STEC screening in Alberta in 2018, it is also essential to have a molecular serotyping method with faster turnaround time for cluster identification and surveillance purposes. This study sought to perform molecular serotyping of the top six non-O157 (O26, O45, O103, O111, O121 and O145) STEC serotypes directly from stools and enrichment broths compared to conventional methods on isolates. Multiplex, serotyping qPCR assays were used to determine sensitivity and specificity of the top six non-O157 STEC serotypes. Sensitivity and specificity were assessed for both singleplex and multiplex qPCR assays for comparison of the top six serotypes. Blinded stool specimens (n = 116) or broth samples (n = 482) submitted from frontline microbiology laboratories for STEC investigation were analyzed by qPCR. Both singleplex and multiplex assays were comparable, and we observed 100% specificity with a limit of detection of 100 colony-forming units per mL. Direct molecular serotyping from stool specimens mostly correlated (88%) with conventional serotyping of the cultured isolate. In cases of discordant serotypes, the top six non-O157 STEC mixed infections were identified and confirmed by culture and conventional serotyping. Detection of non-O157 STEC can be done directly from stool specimens using multiplex PCR assays with the ability to identify mixed infections, which would otherwise remain undetected by conventional serotyping of a single colony. This method can be easily implemented into a frontline diagnostic laboratory to enhance surveillance of non-O157 STEC, as more frontline microbiology laboratories move to culture independent assays.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".