Virulence, serotype and phylogenetic groups of diarrhoeagenic Escherichia coli isolated during digestive infections in Abidjan, Cte dIvoire
Bibliographic record
Abstract
The virulence, serotype and phylogenetic traits of diarrhoeagenic Escherichia coli were detected in 502 strains isolated during digestive infections. Molecular detection of the target virulence genes, rfb gene of operon O and phylogenetic grouping genes Chua, yjaA and TSPE4.C2 was performed. Prevalence of strains harbouring virulent genes was 7.8%. The virulent genes eaeA, bfp, stx2, st1, lt, aggA, east1, ipaH, ial, cnf1 and afa were detected. EAEC (36%) and both EPEC and ATEC (25.6%) are the most detected pathovars (p<0.05). STEC (5.1%), NFEC (7.7) and DAEC (7.7) are less represented. Serogroups are overall diversified (89%), however, serogroups O157, O103 and O86, previously known to be associated with virulence were revealed. Most of the E. coli pathovars (53%) belonged to phylogenetic group A and in decreasing importance order, to D (23.5%), B1 (11.7%) and B2 (11.7%) groups. The study shows a diversified population of intestinal strains (84.6%), with a low phenotypic and phylogenetic link lower (p<0.05). Due to the great diversity of pathotypes, continuous monitoring should be implemented to identify risk factors and major pathways of contamination that help defining strategies to reduce infections associated with E. coli. Key words: Escherichia coli, virulence gene, serogroup, phylogenetic group, diversity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".