High prevalence of MDR and XDR Escherichia coli in hospital wastewater from Shiraz, Iran: ESBL and carbapenemase production
Bibliographic record
Abstract
BACKGROUND: Hospital wastewater (HWW) is a significant reservoir for multidrug-resistant (MDR) bacteria and antimicrobial resistance (AMR) genes, posing serious public health risks. This study investigated the phenotypic and genotypic resistance profiles of Escherichia coli (E. coli) isolates from HWW in Shiraz, Iran. Thirty-six HWW samples (18 influent, 18 effluent) were collected weekly over six weeks from three major hospitals. E. coli isolates were identified and assessed for antimicrobial susceptibility, extended-spectrum β-lactamase (ESBL) and carbapenemase production, resistance genes, phylogroups, and sequence types (STs). RESULTS: A total of 68 E. coli isolates were obtained (33 influent, 35 effluent). High resistance rates were observed for ampicillin (97.1%), cefazolin, and amoxicillin/clavulanic acid (86.8%). The lowest resistance rates were to imipenem (5.9%), meropenem (13.2%), and chloramphenicol (19.1%). MDR and extensively drug-resistant (XDR) profiles were identified in 80.9% and 22.1% of isolates, respectively. ESBL production was found in 57.4% of isolates. All nine carbapenem-resistant isolates were tested by both modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM); eight (88.9%) were positive by mCIM, while all were negative by eCIM. Resistance genes detected included blaTEM (39.7%), blaCTX-M (33.8%), and blaSHV (2.9%). Three isolates carried blaOXA-48, while one carried blaIMP and another carried blaVIM. Phylogroup B2 was most frequent (22.1%), with ST131 being the dominant type (33.8%). CONCLUSION: In conclusion, HWW may serve as a potential reservoir for resistant and pathogenic E. coli isolates, indicating the importance of monitoring wastewater as a step in addressing the issue of AMR.
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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 | 0.000 |
| 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".