Effects of Dietary Intake of Arsenosugars and Other Organic Arsenic Species on Studies of Arsenic Methylation Efficiency in Humans
Bibliographic record
Abstract
Extensive research has used dimethylarsinic acid (DMA) in urine as a marker of arsenic methylation. The premise is that humans methylate inorganic arsenicals to monomethylarsonic acid (MMA) and DMA and excrete these arsenic species into the urine. However, DMA in urine not only comes from the methylation of inorganic arsenic but also could be a result of metabolism of other arsenic species, such as arsenosugars and arsenolipids. Most environmental health and epidemiological studies of arsenic methylation might have overlooked confounding factors that contribute to DMA in urine. Here we critically evaluate reported studies that used methylation indexes, concentration ratios of methylated arsenicals, or the percentage of DMA in urine as markers of arsenic methylation efficiency. Dietary intake of arsenosugars potentially confounds the calculation and interpretation of the arsenic methylation efficiencies. Many studies have not considered incidental dietary intake of arsenosugars, arsenolipids, and other organic arsenic species. Future studies should consider the dietary intake of diverse arsenic species and their potential effect on the urinary concentrations of DMA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".