Role of arsenic and its resistance in nature
Bibliographic record
Abstract
Contamination of the environment with heavy metals has increased drastically over the last few decades. The heavy metals that are toxic include mercury, cadmium, arsenic, and selenium. Of these heavy metals, arsenic is one of the most important global environmental pollutants and is a persistent bioaccumulative carcinogen. It is a toxic metalloid that exists in two major inorganic forms: arsenate and arsenite. Arsenite disrupts enzymatic functions in cells, while arsenate behaves as a phosphate analog and interferes with phosphate uptake and utilization. Despite its toxicity, arsenic may be actively sequestered in plant and animal tissues. Various microbes interact with this metal and have shown resistance to arsenic exposure, and they appear to possess the ars operon for arsenic resistance consisting of three to five genes, i.e., arsRBC or arsRDABC, organized into a single transcriptional unit; some microbes even use it for respiration. Microbial interactions with metals may have several implications for the environment. Microbes may play a role in cycling of toxic heavy metals and in remediation of metal-contaminated sites. There is a correlation between tolerance to heavy metals and antibiotic resistance, a global problem currently threatening the treatment of infections in plants, animals, and humans. The purpose of this review is to highlight the nature and role of toxic arsenic in bacterial systems and to discuss the various genes responsible for this heavy-metal resistance in nature and the mechanisms to detoxify this element.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".