Clonal relationships determined by multilocus sequence typing among enteropathogenic<i>Escherichia coli</i>isolated in Brazil
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) infections are a leading cause of infantile diarrhea in developing nations. Multilocus sequence typing (MLST) characterizes bacterial strains based on the sequences of internal fragments in housekeeping genes. Little is known about strains of EPEC analyzed by MLST from Brazil. In this study, a diverse collection of 29 EPEC strains isolated from patients with diarrhea, admitted to the University Hospital of Ribeirao Preto, was characterized by MLST. Strain analysis demonstrated 22 different sequence types (STs), of which almost half (48%) were new, indicating a high genotype diversity. The 22 STs were divided by eBURST into 12 clonal complexes. It was not possible to correlate typical and atypical EPEC with other strains in the MLST database. This is the first study that analyzed EPEC strains from South America that are included in the E. coli MLST database. Nine (31%) out of 29 strains are part of the CC10 clonal complex, the major clonal complex in the database, which comprises 174 strains and 86 different STs, suggesting that these strains might be the most important intestinal pathogenic E. coli worldwide. Genetic relationships between typical and atypical EPEC, enterohemorrhagic E. coli, and enteroaggregative E. coli strains were not established by MLST.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".