Prevalence of Antibiotic Resistance Genes (ARGs) in a small wastewater treatment plant in Egypt
Bibliographic record
Abstract
This study aimed to evaluate the occurrence and abundance of antibiotic resistance genes (ARGs) in one of the rural wastewater treatment plants (WWTP) in Egypt, namely Hawamdya.Conventional PCR and qPCR were used to detect and quantify the abundance of β-lactam (ampC and OXA-1), sulfonamide (sul1 and sul2), tetracycline (tetO and tetW), and macrolide (ermF and ermB) as well as class 1 integron (intl1).DNA was extracted from influent, activated sludge, and effluent samples.ARGs were detected and quantified in all the tested samples using specific primers for each gene, suggesting the prevalence of ARG in the wastewater entering and persisting the WWTP.The intl1 gene, a marker for integrons, was highly abundant in the influent and activated sludge, confirming that integrons play a significant role in the spread of antibiotic resistance in the Hawamdya WWTP.The highest occurrence of sul1 and ermF genes among all samples was detected in influent, suggesting the widespread use of sulfonamide and macrolide antibiotics in the located area.The activated sludge showed comparable copy numbers for most ARGs, suggesting that it serves as a reservoir of antibioticresistance genes.The effluent showed a high copy number for sul1 and sul2 genes but a lower level of intl1, indicating that the treatment process partially removes ARGs but may be less effective against integrons.The present study revealed a high abundance of ARG, especially sulfonamide resistance genes, in tested rural WWTP as a point source of ARGs in the environment and emphasized the need to control antibiotic use and develop more effective wastewater treatment strategies to minimize the spread of antibiotic resistance.
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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 | 0.001 | 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".