Cultivatable bacterial community in water from Lai Nullah contaminated with household sewage and industrial waste is more diverse and populated compared with nonpolluted water
Bibliographic record
Abstract
The effect of environmental pollutants on living organisms can be assessed by studying the changes in the indigenous microbial community. Therefore, in this study, cultivatable bacterial community in nonpolluted as well as household sewage and industrially polluted water of Lai Nullah flowing through Islamabad and Rawalpindi, Pakistan was analyzed. Bacterial community composition and population present in the polluted water were significantly different from the nonpolluted water (P < 0.05). Nonpolluted water had much fewer species and population of bacteria compared with polluted water. Sequence analysis of bacterial 16S rRNA gene revealed that Citrobacter freundii, Klebsiella pneumoniae, Escherichia coli, Lactobacillus plantarum, Geobacillus stearothermophilus, Enterococcus faecalis, Acinetobacter guillouiae, Ralstonia sp., Comamonas sp., and Stenotrophomonas maltophilia were specific to the polluted water. On the other hand, Aeromonas veronii, Exiguobacterium sp., and Lysinibacillus macroides were only found in the nonpolluted water. Among measured physicochemical parameters, higher colony count in the polluted water was best correlated with higher biological oxygen demand, phosphate, sodium, and chloride values (Spearman’s rho = 0.85). Concentration of heavy metals such as cadmium, chromium, copper, nickel, and lead were below 0.03 μg·mL−1 at all the study sites. During plate assay, bacterial strains found at polluted sites showed resistance to selected heavy metals with highest minimum inhibitory concentration for lead (8 mmol·L−1) followed by copper (5 mmol·L−1), nickel (3 mmol·L−1), and cadmium (1 mmol·L−1). All the bacterial isolates also showed various levels of resistance against antibiotics ampicillin, tetracycline, ciprofloxacin, and vancomycin using broth microdilution method. Current research provides new insight into the effect of household sewage and the industrially polluted water of Lai Nullah on the indigenous 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 | 0.000 |
| 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".