Rapid Detection of Shiga Toxin-Producing Bacteria in Feces by Multiplex PCR with Molecular Beacons on the Smart Cycler
Bibliographic record
Abstract
We have developed a rapid (1-h) real-time fluorescence-based PCR assay with the Smart Cycler thermal cycler (Cepheid, Sunnyvale, Calif.) for the detection of Shiga toxin-producing Escherichia coli (STEC), as well as other Shiga toxin-producing bacteria. Based on multiple-sequence alignments, we have designed two pairs of PCR primers that efficiently amplify all variants of the Shiga toxin genes stx(1) and stx(2), respectively. These primer pairs were combined for use in a multiplex assay. Two molecular beacons bearing different fluorophores were used as internal probes specific for each amplicon. Assays performed with purified genomic DNA from a variety of STEC strains (n = 23) from diverse geographic locations showed analytical sensitivities of about 10 genome copies per PCR. Non-STEC strains (n = 20) were also tested, and no amplification was observed. The PCR results correlated perfectly with the phenotypic characterization of toxin production in both STEC and non-STEC strains, thereby confirming the specificity of the assay. The assay was validated by testing 38 fecal samples obtained from 27 patients. Of these samples, 26 were PCR positive for stx(1) and/or stx(2). Compared with the culture results, both the sensitivity and the negative predictive value were 100%. The specificity was 92%, and the positive predictive value was 96%. Moreover, this assay detected STEC from a sample in which the STEC concentration was at the limit of detection of the conventional culture methods and from a sample in which STEC was not detected by the conventional culture methods. This real-time PCR assay is simple, rapid, sensitive, and specific and allows detection of all Shiga toxin-producing bacteria directly from fecal samples, irrespective of their serotypes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".