Phenotype–genotype discordance in antimicrobial resistance profiles of Gram-negative uropathogens recovered from catheter-associated urinary tract infections in Egypt
Bibliographic record
Abstract
OBJECTIVES: Catheter-associated urinary tract infections (CAUTIs) are among the most common healthcare-associated infections in low- and middle-income countries (LMICs), but there are few resistome data available for relevant uropathogens. The goal of this study was to characterize the antimicrobial resistance (AMR) phenotypes and genotypes of a large collection of Gram-negative bacteria recovered from CAUTIs in a hospital in Mansoura, Egypt. METHODS: Phenotypic AMR profiles and whole-genome sequence data were generated for 132 isolates. Resistomes were predicted using ResFinder, CARD and AMRFinder. Similarity of uropathogen genomic data was determined using sourmash (kmer signatures). Escherichia coli genomic data were subject to a pangenome analysis using Panaroo. RESULTS: Sixty-seven E. coli (Phylogroup B2; 53.7%, 36/67), 14 Pseudomonas aeruginosa, 11 Klebsiella pneumoniae, 9 Proteus mirabilis, 8 Providencia spp., 5 Enterobacter hormaechei and 18 rare CAUTI-associated isolates were identified. Several (22/132) isolates were multidrug-resistant, while almost half (62/132) were extensively drug-resistant. Phenotype-genotype discordance was found to be an important consideration in resistome studies in Egypt, with a total concordance of 91% (1115/1225), 85.7% (1273/1485) and 80.5% (1196/1485) for ResFinder, CARD and AMRFinder, respectively. Pseudomonas, at the species level, exhibited the greatest discordance. At the antimicrobial level, meropenem was subject to greatest discordance. New AMR variants were found for Egypt for Pseudomonas (blaOXA-486, blaOXA-488, blaOXA-905, blaIMP-43, blaPDC-35, blaPDC-45, blaPDC-201) and E. coli (blaTEM-176, blaTEM-190). CONCLUSIONS: This study shows that there is phenotype-genotype discordance in AMR profiling among CAUTI isolates, highlighting the need for comprehensive approaches in resistome studies. We also show the genomic diversity of Gram-negative uropathogens contributing to disease burden in a little-studied LMIC setting.
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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 | 0.001 | 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".