Microbial Biofilm Diversity and Prevalence of Antibiotic Resistance Genes in Drinking Water Distribution System of Peshawar, Pakistan
Bibliographic record
Abstract
The occurrence of microbial communities harboring antibiotic resistance bacteria and antibiotic resistance genes in the drinking water distribution system pose a significant threat to the aquatic ecosystem and to public health, especially in developing countries. In this study, we have used next-generation sequencing technology to explore bacterial community diversity and the abundance of antibiotic resistance genes in biofilms collected from the drinking water distribution system of Peshawar, the capital city of the Khyber Pakhtunkhwa province of Pakistan. The results showed that Proteobacteria were the most abundant phyla (89.79%) in all biofilm samples, followed by Bacteroidetes (3.48%) and Actinobacteria (2.79%). At genus level, Pseudomonas was the most common (22.45%) in all biofilm samples. Overall, bacterial diversity and richness was higher in biofilm samples collected from the consumer end than the source site. Bacterial diversity was also dependent on the piping material (GI vs. PVC) and water supply (direct vs. indirect). Functional annotation reveals a differential abundance of common metabolic pathways at source and consumer end. Resistome analysis revealed a prevalence of resistance genes against 12 classes of antibiotics in all samples with macrolides resistance being the commonest at the consumer end (42.1%) and fluoroquinolone resistance at the source end (24%). To our knowledge, this is the first study that provides new insight and evidence into the microbial community diversity and antibiotic resistance in the drinking water supply system of Peshawar. These findings may ultimately help the authorities to design and implement effective strategies for controlling biofilms and ensuring a continuous supply of safe drinking water to the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".