Development of Recombinase Aided Amplification (RAA)-Exo-Probe Assay for the Rapid Detection of Shiga Toxin-Producing <i>Escherichia coli</i>
Bibliographic record
Abstract
BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) is a significant cause of foodborne illness causing various gastrointestinal diseases including hemolytic uremic syndrome (HUS), the most severe form, which can lead to kidney failure or even death. OBJECTIVE: Here, we report the development of recombinase aided amplification (RAA)-exo-probe assays targeting the stx1 and stx2 genes for the rapid detection of STEC in food samples. METHODS: Primers and exo-probes were designed and optimized for the detection of stx1 and stx2 using RAA technology. The optimal STEC RAA-exo-probe assays were then tested for specificity and sensitivity, and validated in both spiked and real food samples. RESULTS: These assays were found to be 100% specific to STEC strains and were also highly sensitive with a detection limit of 1.6 × 103 CFU/mL or 32 copies/reaction. Importantly, the assays were able to successfully detect STEC in spiked and real food samples (beef, mutton, and pork), with a detection limit as low as 0.35 CFU/25g in beef samples after an overnight enrichment step. CONCLUSIONS: Overall, the RAA assay reactions completed within ∼20 min and were less dependent on expensive equipment, suggesting they can be easily adopted for in-field testing requiring only a fluorescent reader. HIGHLIGHTS: As such, we have developed two rapid, sensitive, and specific assays that can be used for the routine monitoring of STEC contamination in food samples, particularly in the field or in poorly equipped labs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".