Detection and Analysis of Drug and Disinfectant Resistance Genes in the Sewage of a Center for Disease Control and Prevention
Why this work is in the frame
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Bibliographic record
Abstract
Purpose: Sewage is a significant reservoir for drug and disinfectant resistance genes and a medium for dissemination. This study aimed to evaluate the presence of drug and disinfectant resistance genes in the sewage of a Center for Disease Control and Prevention (CDC) and to assess the risks of their dissemination. Methods: Sewage from a CDC in Hangzhou was collected, filtered, and enriched, and its microorganisms were cultured. The isolated bacteria were identified, and the minimum inhibitory concentration (MIC) was determined. The drug and disinfectant resistance genes in the sewage and bacteria were detected through polymerase chain reaction amplification. Results: Three kinds of bacteria were isolated from the sewage sample. The MIC for Sphingomonas and Staphylococcus xylosus against chlorine-containing disinfectants was 250 mg/L, whereas the MIC for Bacillus firmus was 500 mg/L. The β-lactam resistance gene TEM and the disinfectant resistance gene qacA were positive in the bacteria, whereas the β-lactam resistance genes TEM, SHV , and VIM-1 , the tetracycline resistance gene tetM , the aminoglycoside resistance genes aac(6’)/aph(2′) and aph3′-III , and the disinfectant resistance genes qacA, qacE , and qacEΔ 1 were positive in the sewage. Conclusion: Drug and disinfectant resistance genes were found in the sewage of a CDC and were associated with bacteria. Thus, optimizing the monitoring and treatment of sewage is crucial. Keywords: center for disease control and prevention, sewage, drug resistance gene, disinfectant resistance gene
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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 | 0.000 | 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 | 0.000 |
| 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 it