Detection and Analysis of Drug and Disinfectant Resistance Genes in the Sewage of a Center for Disease Control and Prevention
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".