Uptake and transformation of arsenic by <i>Acidomyces acidophilus</i> isolated from acidic mine tailings and its toxigenic implications
Bibliographic record
Abstract
Adequate treatment and safe disposal of high-acidity effluents generated during mining containing elevated concentrations of heavy metals and metalloids, such as arsenic, are a critical environmental challenge. In this work, we isolated and characterized an acidophilic fungus from acid mine drainage-affected tailings pond sludge containing high levels of heavy metals. This fungus was identified as Acidomyces acidophilus strain MSS1 and was characterized by its capacity to tolerate and metabolize As(V) and As(III). Our results show that As tolerance and removal capacity by this fungus is highly dependent on pH, being more effective at pH 3.0 than pH 5.4. The biotransformation mechanism involves internalization of As species, As(V) reduction to As(III), and possible biomethylation. It is also capable of oxidizing As(III) in the medium to As(V) to a lesser extent. Arsenite methyltransferase expression was upregulated in the presence of As(III), increasing approximately 25-fold at pH 3 and approximately 14-fold at pH 5.4, compared with fungus not exposed to As. However, in the presence of As(V), it only increased approximately five-fold at pH 5.4; thus, methylation of As is highly dependent on pH and the type of As species present. Additionally, As was removed by biosorption to the fungal biomass. Overall, our results suggest that A. acidophilus can be considered as a potential As bioremediation agent for the removal of As, in particular As(III), in highly acidic effluents, due to its remarkable tolerance to low pH and high metal concentrations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".