Isolation and characterization of mercury-resistant bacteria from wastewater sources in Egypt
Bibliographic record
Abstract
An important mechanism for microbial resistance to mercury is its reduction into elemental mercury (facilitated by the merA gene). Thirty-eight microbial isolates from a variety of wastewater sources in Egypt were collected. Approximately 14 of the 38 isolates exhibited not only a high degree of tolerance to mercury (up to 160 ppm) but also a high degree of resistance to other tested heavy metals (Cu, Co, Ni, and Zn). From these 14, the 10 most resistant isolates were selected for further study and were found to include 9 Gram-negative and 1 Gram-positive bacterial strains. Multi-antibiotic-resistance profiles were detected for 6 out of the 10 selected isolates. All the tested Gram-negative isolates (n = 9) harbored a plasmid-encoded merA gene. The mercury removal effectiveness for the 10 selected isolates ranged between 50% and 99.9%, among which Stenotrophomonas maltophilia ADW10 recorded the highest rate (99.9%; at an initial mercury concentration of 20 ppm). To the best of our knowledge, this is the first study to (i) demonstrate the presence of a multimetal-resistant S. maltophilia bacterium with a high mercury tolerance capacity that would make it a suitable candidate for future bioremediation efforts in heavy-metal-polluted areas in Egypt and (ii) report Pseudomonas otitidis as one of the mercury-resistant 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".