Class 1 Integrons, Selected Virulence Genes, and Antibiotic Resistance in <i>Escherichia coli</i> Isolates from the Minjiang River, Fujian Province, China
Bibliographic record
Abstract
Widespread fecal pollution of surface waters in developing countries is a threat to public health and may represent a significant pathway for the global dissemination of antibiotic resistance. The Minjiang River drainage basin in Fujian Province is one of China's most intensive livestock and poultry production areas and is home to several million people. In the study reported here, Escherichia coli isolates (n = 2,788) were sampled (2007 and 2008) from seven surface water locations in the basin and evaluated by PCR for carriage of selected genes encoding virulence factors, primarily for swine disease. A subset of isolates (n = 500) were evaluated by PCR for the distribution and characteristics of class 1 integrons, and a subset of these (n = 200) were evaluated phenotypically for resistance to a range of antibiotics. A total of 666 (24%) E. coli isolates carried at least one of the virulence genes elt, fedA, astA, fasA, estA, stx(2e), paa, and sepA. Forty-one percent of the isolates harbored class 1 integrons, and these isolates had a significantly higher probability of resistance to tobramycin, cefoperazone, cefazolin, ciprofloxacin, norfloxacin, azitromycin, and rifampin than isolates with no class 1 integron detected. Frequencies of resistance to selected antibiotics were as high as or higher than those in fecal, wastewater, and clinical isolates in published surveys undertaken in China, North America, and Europe. Overall, E. coli in the Minjiang River drainage basin carry attributes with public health significance at very high frequency, and these data provide a powerful rationale for investment in source water protection strategies in this important agricultural and urban setting in China.
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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 | 0.000 | 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 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".