Identification of Methylated Dithioarsenicals in the Urine of Rats Fed with Sodium Arsenite
Bibliographic record
Abstract
Biotransformation of inorganic arsenic results in the formation of methylarsenicals of both oxygen and sulfur analogues. Aiming to improve our understanding of metabolism of inorganic arsenic in animals, we conducted an animal feeding study with an emphasis on identifying new arsenic metabolites. Female F344 rats were given 0, 1, 10, 25, 50, and 100 μg/g of arsenite (iAs(III)) in the diet. Arsenic species in rat urine were determined using high performance liquid chromatography (HPLC) separation and inductive coupled plasma mass spectrometry (ICPMS) and electrospray ionization tandem mass spectrometry (ESI MS/MS) detection. Nine arsenic species were detected in the urine of the iAs(III)-dosed rats. Seven of these arsenic species were consistent with previous reports, including iAs(III), arsenate, monomethyarsonic acid, dimethylarsinic acid, trimethylarsine oxide, monomethylmonothioarsonic acid, and dimethylmonothioarsinic acid. Two new methyldithioarsencals, monomethyldithioarsonic acid (MMDTA(V)) and dimethyldithioarsinic acid (DMDTA(V)), were identified for the first time in the urine of rats treated with iAs(III). The concentrations of both MMDTA(V) and DMDTA(V) in rat urine were dependent on the dosage of iAs(III) in diet. The concentration of DMDTA(V) was approximately 5 times higher than that of MMDTA(V). MMDTA(V) has not been identified in any biological samples of animals, and DMDTA(V) has not been reported as a metabolite of inorganic arsenic in the rats. The identification of novel methylated dithioarsenicals as metabolites of inorganic arsenic in the rat urine provided further insights into the understanding of the metabolism of arsenic.
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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 | 0.001 | 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) | 0.001 | 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".