Human exposure to organic arsenic species from seafood
Bibliographic record
Abstract
Seafood, including finfish, shellfish, and seaweed, is the largest contributor to arsenic (As) exposure in many human populations. In contrast to the predominance of inorganic As in water and many terrestrial foods, As in marine-derived foods is present primarily in the form of organic compounds. To date, human exposure and toxicological assessments have focused on inorganic As, while organic As has generally been considered to be non-toxic. However, the high concentrations of organic As in seafood, as well as the often complex As speciation, can lead to complications in assessing As exposure from diet. In this report, we evaluate the presence and distribution of organic As species in seafood, and combined with consumption data, address the current capabilities and needs for determining human exposure to these compounds. The analytical approaches and shortcomings for assessing these compounds are reviewed, with a focus on the best practices for characterization and quantitation. Metabolic pathways and toxicology of two important classes of organic arsenicals, arsenolipids and arsenosugars, are examined, as well as individual variability in absorption of these compounds. Although determining health outcomes or assessing a need for regulatory policies for organic As exposure is premature, the extensive consumption of seafood globally, along with the preliminary toxicological profiles of these compounds and their confounding effect on assessing exposure to inorganic As, suggests further investigations and process-level studies on organic As are needed to fill the current gaps in knowledge.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".