Isolation and detection of Shiga toxin-producing Escherichia coli in clinical stool samples using conventional and molecular methods
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it