Genomic characterization of multi drug resistant ESBL-producing Escherichia coli isolates from patients and patient environments in a teaching hospital in Ghana
Bibliographic record
Abstract
Abstract Background ESBL-producing Escherichia coli pose a growing health risk in community and healthcare settings. We investigated the resistome, virulome, mobilome, and genetic relatedness of multidrug-resistant (MDR) E. coli isolates from patients and their environment in a Ghanaian teaching hospital. Materials and methods Twenty-three MDR ESBL-producing or carbapenem-resistant E. coli isolates from a collection of MDR Gram-negative bacteria (GNB) from patients and environments were selected for genomic analyses. Whole genome sequencing and bioinformatics tools were used to analyze genomic characteristics and phylogeny. Results The prevalence and incidence of rectal carriage of ESBL E. coli among patients were 13.65% and 11.32% respectively. The β-lactamase genes, bla TEM−1B (10 isolates) and bla CTX−M−15 (12 isolates) were commonly associated with IncFIB plasmid replicons and co-occurred with aminoglycoside, macrolide, and sulfamethoxazole/trimethoprim resistance. Insertion sequences, transposons, and class I integrons were found with bla CTX−M−15 . Carriage and environmental isolates carried multiple virulence genes, with ter C being the most prevalent in 21 isolates. Seventeen sequence types (STs) were identified, including a novel ST (ST13846). Phylogenetic analysis grouped the isolates into four main clusters, with one outlier. High genetic relatedness was observed between two carriage isolates of ST940 and between a carriage isolate and an environmental isolate of ST648. Isolates with different STs, collected at different times and locations, also showed genetic similarities. Conclusion We identified ESBL-producing E. coli with diverse genomic characteristics circulating in different hospital directorates. Clonal relatedness was observed among isolates from patients and the environment, as well as between different patients, suggesting transmission within and between sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".