Evaluation of Four Loop-Mediated Isothermal Amplification (LAMP)Assays for Identification of Shiga Toxin Producing E.Coli O157 (STEC)and Non-O157 Strains
Bibliographic record
Abstract
Shiga toxin-producing strains of E. coli are a significant cause of food-borne outbreaks of gastroenteric disease. The objective of this study was to evaluate loop-mediated isothermal amplification (LAMP) assays for the identification of Shiga toxin producing E. coli O157 (STEC) and non-O157 strains. Four LAMP assays were developed for the detection of the rfbE gene of E. coli O157, the Shiga toxin genes stx1/stx2 and eae intimin gene. The assays were run on a real time fluorometer (Genie II, Optigene, Horsham, UK) that displays real time amplification, the time to positivity and amplicon annealing temperature (Tm). The specificity of the LAMP assays was confirmed by testing a panel of 35 enteric bacteria, viruses and parasites, all of which tested negative in all four assays. The lower limit of detection for each of the gene targets was 10-100 genome equivalents and 1 cfu of E. coli O157. The LAMP assays were evaluated by testing a total of 135 stool specimens by LAMP, PCR or the xTAG® Gastrointestinal Pathogen Panel (GPP) assay (Luminex Molecular Diagnostics, Toronto, ON, Canada). Following resolution of the discordants and using positivity in two or more assays as the reference standard, the sensitivity of the LAMP assays was 100% (27/27), 98.4% (63/64) and 98.0% (47/48) for rfbE, stx1/stx2 and eae genes respectively, while the specificity for the assays was 100% (107/107), 100% (71/71) and 98.5% (66/67), respectively. The LAMP assays had excellent sensitivity and specificity for detecting Shiga toxin-producing E. coli O157 (STEC) and non-O157 in stool specimens and they were faster and more accurate than PCR. We suggest that these assays could be incorporated into E. coli O157 (STEC) testing algorithms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".