Phenotypic and Genotypic Characterization of Verotoxin-Producing<i>Escherichia coli</i>O103:H2 Isolates from Cattle and Humans
Bibliographic record
Abstract
Characterization of important non-O157 verotoxin-producing Escherichia coli (VTEC) has lagged considerably behind that of O157:H7 strains. This study characterized 91 VTEC O103:H2 strains from bovine and human sources and of North American and European origins by virulence or putative virulence genes, pulsed-field gel electrophoresis (PFGE) patterns, plasmid profiles, antimicrobial resistance, and colicin production. All strains were positive for vt1 and eae-epsilon; 97% were positive for ehxA; and all were negative for hlyA. Two strains carried vt2. There were 66 PFGE patterns grouped in six clusters, and there were 25 different plasmid profiles. Plasmid-encoded katP and etp genes were significantly more frequent in European than in North American human strains. The distribution of selected phenotypes was as follows: enterohemorrhagic E. coli (EHEC) hemolysin, 95%; colicin production, 38%; antimicrobial resistance, 58%. All the strains were negative for the alpha-hemolytic phenotype. In conclusion, the VTEC O103:H2 strains were diverse, as shown by PFGE, plasmid profiles, virulence markers, and antimicrobial resistance patterns, and all strains showed an EHEC hemolytic phenotype instead of the alpha-hemolytic phenotype that has been shown previously.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".