Prevalence and Characterization of Shiga Toxin-Producing <i>Escherichia coli</i> Isolated from Cattle, Food, and Children during a One-Year Prospective Study in France
Bibliographic record
Abstract
During a 1-year survey of Shiga toxin-producing Escherichia coli (STEC) prevalence in central France, 2,143 samples were investigated by PCR for Shiga toxin-encoding genes. A total of 330 (70%) of 471 fecal samples collected from healthy cattle at the Clermont-Ferrand slaughterhouse, 47 (11%) of 411 beef samples, 60 (10%) of 603 cheese samples, and 19 (3%) of 658 stool specimens from hospitalized children with and without diarrhea were positive for the stx gene(s). A STEC strain was isolated from 34% (162 of 471) of bovine feces, 4% (16 of 411) of beef samples, 1% (5 of 603) of cheese samples, and 1.5% (10 of 658) of stool specimens. Of the 220 STEC strains isolated, 34 (15%) harbored the stx(1) gene, 116 (53%) harbored the stx(2) gene, and 70 (32%) carried both the stx(1) and stx(2) genes. However, 32 (14.5%) were not cytotoxic for Vero cells. The eae gene, found in 12 (5%) of the 220 strains, was significantly associated with the stx(1) gene and with isolates from children. Sequences homologous to ehxA were found in 102 (46%) of the 220 strains. Thirteen serotypes, OX3:H2, O113:H21, O113:H4, OX3:H21, O6:H10, OX178:H19, O171:H2, O46:H38, O172:H21, O22:H16, O91:H10, O91:H21, and O22:H8, accounted for 102 (55%) of 186 typeable isolates, and only one strain (0.5% of the 186 STEC isolates from cattle), belonged to the O157:H7 serotype. We showed that the majority of the STEC isolates from cattle, beef, and cheese were not likely to be pathogenic for humans and that the STEC strains isolated from children in this study were probably not responsible for diarrheal disease. Finally, the strains associated with hemolytic-uremic syndrome in the same geographical area were shown to belong to particular subsets of the STEC population found in the bovine reservoir.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".