Isolation, identification, and characterization of resistant bacteria to antibiotics from pharmaceutical effluent and study of their antibiotic resistance
Bibliographic record
Abstract
Pharmaceutical effluents primarily enter aquatic environments through the discharge of treated and untreated wastewater from various sources, including hospitals, pharmaceutical manufacturing facilities, and households. Microbes sourced from pharmaceutical effluents such as Pseudomonas spp. pose a significant public health concern because of their high levels of resistance to multiple drugs and extreme multidrug resistance. Therefore, the present study was conducted for the isolation, identification, and molecular characterization of selected isolates from pharmaceutical effluents and also determined their antibiotic sensitivity patterns. From June 2016 to March 2017, a study was conducted on four well-known pharmaceutical companies specializing in antibiotic production in Dhaka and Gazipur. Four wastewater samples were collected from various origins and then brought to the Bacteriology laboratory for microbiological examination. Twelve pure isolates were obtained and characterized through cultural and biochemical tests while molecular identification of Pseudomonas spp. was performed using the 16S rRNA gene sequence. Twelve commercially available antibiotics were used for antibiotic sensitivity tests using Kirby-Bauer disk diffusion methods. We isolated the most predominant isolates, Pseudomonas aeruginosa (41.67%), followed by Bacillus spp. (33.33%) and Staphylococcus spp. (25%) respectively. Among 12 antibiotics, ciprofloxacin is 100% sensitive against P. aeruginosa, while the remaining 11 antibiotics are 100% resistant. Bacillus spp. showed 100% resistance to all antibiotics while 50% sensitive to vancomycin and 100% to chloramphenicol, respectively. Staphylococcus spp. was 100% resistant to all antibiotics. Our research suggested that P. aeruginosa is the reservoir of antibiotic resistance genes and spreads disease to humans from the environment. The findings of this study, i.e., the isolation, identification, and characterization of antibiotic-resistant bacteria from pharmaceutical effluent have highlighted, comprehended, and mitigated the dissemination of antibiotic resistance and opportunistic bacteria.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".