Biotransformation of seafood arsenic by intestinal microbiota: A critical review on ecotoxicological and health risk implications
Bibliographic record
Abstract
Arsenic (As) is a toxic element widely distributed in the marine environment, making seafood the primary dietary route of human exposure. It is present in diverse chemical forms, including low-toxicity organic species, such as arsenobetaine (AsB) and arsenosugars (AsSugars), and high-toxicity inorganic arsenic (iAs). While the human gut microbiota is known to play a crucial role in arsenic metabolism, a systematic summary of these complex biotransformations, particularly for organic species, is currently lacking. This review addresses this knowledge gap by providing a comprehensive overview of the established research on microbe-mediated arsenic metabolism. Our work summarizes the principal microbial pathways for inorganic arsenic, including oxidation, reduction, and methylation, which modulate its toxicity. Furthermore, we synthesize the emerging body of research on the metabolism of major organic arsenicals, covering the degradation of AsSugars, the biotransformation of arsenolipids (AsLipids), and the initial findings on the metabolic fate of AsB. The key functional bacterial genera implicated in these processes are also identified. We conclude that a thorough understanding of these intricate microbe-arsenic interactions is essential for improving ecotoxicological models, accurately assessing health risks associated with seafood consumption, and establishing robust, evidence-based public health guidelines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".