Presence of virulence genes and pathogenicity islands in extraintestinal pathogenic Escherichia coli isolates from Brazil
Bibliographic record
Abstract
INTRODUCTION: Extraintestinal pathogenic Escherichia coli (ExPEC) is associated with various diseases such as urinary tract infections, neonatal meningitis and septicemia. There are many virulence factors (VF) encoded by genes in ExPEC, including papC, papG, ecpA, iroN, fyuA, iutA, ompTp, tsh, hlyF, hlyA and iss. These virulence genes may be present in pathogenicity islands (PAI) or plasmids. METHODOLOGY: In this study, we analyzed the presence of VF encoding genes, PAI sequences and phylogenetic groups of 96 ExPEC strains isolated from the urine and blood of patients at the University Hospital of Londrina, and we compared them with 50 faecal commensal strains from healthy individuals. RESULTS: The VF fyuA (65.60%) was detected in pathogenic strains and commensal strains (46%). A comparison of the distribution of ExPEC and commensal strains in the phylogenetic groups showed that more ExPEC strains belonged to group B2 whereas more of the commensal isolates belonged to group A. The distribution of the seven PAI sequences between commensal strains and ExPEC strains showed that PAI IV536 was common in both ExPEC and commensal isolates. CONCLUSIONS: These results showed that the ExPEC strains that belonged to group B2 had more PAI sequences compared to those of the other groups, especially group B1, which had virulence genes but the lowest percentage of PAI sequences, which leads us to conclude that the virulence of ExPEC strains characterized as B2 is likely attributed to PAI encoded genes, whereas the virulence of ExPEC strains belonging to phylogenetic group B1 is likely due to plasmid encoded virulence genes.
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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 | 0.001 | 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) | 0.001 | 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".