Genotypic Characterisation and Risk Assessment of Virulent ESBL-Producing E. coli in Chicken Meat in Tunisia: Insights from Multi-Omics Machine Learning Perspective
Bibliographic record
Abstract
Antibiotics are frequently used in the poultry industry, which has led to the emergence of bacterial strains that are resistant to antimicrobial treatments. The main objectives of this research were to conduct a multimodal risk assessment, to determine the extent of contamination of chicken meat with Escherichia coli, assess the prevalence of strains resistant to extended-spectrum cephalosporins (ESC), and characterise the genes associated with resistance and virulence. A standardised procedure involving enrichment in buffered peptone water and isolation of E. coli on MacConkey agar was carried out on 100 chicken carcasses. Subsequently, the sensitivity of the strains was tested against 21 antibiotic discs. Additionally, ESBL production was detected using a double synergy test. Specific PCRs were employed to identify resistance to critical antibiotics in human medicine (such as cephalosporins, carbapenems, fluoroquinolones, and colistin), as well as the presence of virulence genes. The contamination rate of chicken meat with E. coli was 82%. The prevalence of ESC-resistant isolates was 91.2%. Furthermore, 76.5% of the isolates exhibited ESBL production, with the different beta-lactamase genes (blaCTXM, blaTEM, and blaSHV). The mcr-1 gene, associated with colistin resistance, was detected in four strains (5.9%). Some isolates also carried resistance genes such as sul1, sul2, sul3, tetA, tetB, qnrB, and qnrS. In addition, several virulence genes were detected. In our study, we were able to link the expression of AMR to the iron metabolic regulatory elements using a multimodal machine learning approach; this mechanism could be targeted to mitigate the bacteria virulence and resistance. The high prevalence of ESBL-producing and multi-resistant E. coli strains in poultry presents significant human health risks, with the focus on antibiotic-resistant uropathogenic strains since poultry meat could be an important source of uropathogenic strains, underscoring the danger of hard-to-treat urinary tract infections, stressing the need for controlled antibiotic use and thorough monitoring.
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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 | 0.001 | 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 | 0.001 |
| 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".