Isolation and detection of Shiga toxin-producing Escherichia coli in clinical stool samples using conventional and molecular methods
Bibliographic record
Abstract
The isolation of Shiga toxin-producing Escherichia coli (STEC) other than serogroup O157 from clinical stool samples is problematic due to the lack of differential phenotypic characteristics from non-pathogenic E. coli. The development of molecular reagents capable of identifying both toxin and serogroup-specific genetic determinants holds promise for a more comprehensive characterization of stool samples and isolation of STEC strains. In this study, 876 stool samples from paediatric patients with gastroenteritis were screened for STEC using a cytotoxicity assay, commercial immunoassay and a conventional PCR targeting Shiga-toxin determinants. In addition, routine culture methods for isolating O157 STEC were also performed. The screening assays identified 45 stools presumptively containing STEC, and using non-differential culture techniques a total of 20 O157 and 22 non-O157 strains were isolated. These included STEC serotypes O157 : H7, O26 : H11, O121 : H19, O26 : NM, O103 : H2, O111 : NM, O115 : H18, O121 : NM, O145 : NM, O177 : NM and O5 : NM. Notably, multiple STEC serotypes were isolated from two clinical stool samples (yielding O157 : H7 and O26 : H11, or O157 : H7 and O103 : H2 isolates). These data were compared to molecular serogroup profiles determined directly from the stool enrichment cultures using a LUX real-time PCR assay targeting the O157 fimbrial gene lpfA, a microsphere suspension array targeting allelic variants of espZ and a gnd-based molecular O-antigen serogrouping method. The genetic profile of individual stool cultures indicated that the espZ microsphere array and lpfA real-time PCR assay could accurately predict the presence and provide preliminary typing for the STEC strains present in clinical samples. The gnd-based molecular serogrouping method provided additional corroborative evidence of serogroup identities. This toolbox of molecular methods provided robust detection capabilities for STEC in clinical stool samples, including co-infection of multiple serogroups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".