Resistome prevalence and diversity in Escherichia coli isolates of global wastewaters
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) is a global problem driven by unregulated antibiotic use leading to the emergence of resistant strains; the “antibiotic paradox” where the cure is the cause of deadlier infections. AMR is fueled by wastewater mismanagement and global mobility, leading to the dissemination of AMRs and multidrug-resistant (MDR) strains worldwide. We embarked on estimating the ‘invasion’ of antibiotic-resistant genes (ARGs) into the normal flora of humans. We screened 300 local wastewater E. coli and sequenced eight isolates to study the genome diversity and resistome, which were then compared with the 529 globally isolated wastewater E. coli (genomes from the PATRIC database). Local wastewaters had 26% resistant and 59% plasmid-bearing E. coli. Global wastewater resistome majorly comprised ARGs against beta-lactam, aminoglycosides, fluoroquinolone, sulfonamide, and trimethoprim. Resistance to colistin, a last-resort antibiotic, was prevalent in MDRs of European and South Asian isolates. Canada fared better in all the AMR parameters, likely due to effective AMR surveillance, antibiotic stewardship and wastewater disinfection, which could serve as a model for other regions. A systems approach is required to address the AMR crisis on a global scale, reduce antibiotic usage and increase the efficiency of wastewater management and disinfection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".