Determination of Arsenic Metabolic Complex Excreted in Human Urine after Administration of Sodium 2,3-Dimercapto-1-propane Sulfonate
Bibliographic record
Abstract
Sodium 2,3-dimercapto-1-propane sulfonate (DMPS) has been used to treat acute arsenic poisoning. Presumably DMPS functions by chelating some arsenic species to increase the excretion of arsenic from the body. However, the excreted complex of DMPS with arsenic has not been detected. Here we describe a DMPS complex with monomethylarsonous acid (MMA(III)), a key trivalent arsenic in the arsenic methylation process, and show the presence of the DMPS-MMA(III) complex in human urine after the administration of DMPS. The DMPS-MMA(III) complex was characterized using electrospray tandem mass spectrometry and determined by using HPLC separation with hydride generation atomic fluorescence detection (HGAFD). The DMPS-MMA(III) complex did not form a volatile hydride with borohydride treatment. On-line digestion with 0.1 M sodium hydroxide following HPLC separation decomposed the DMPS-MMA(III) complex and allowed for the subsequent quantification by hydride generation atomic fluorescence. Arsenite (As(III)), arsenate (As(V)), monomethylarsonic acid (MMA(V)), dimethylarsinic acid (DMA(V)), MMA(III), and DMPS-MMA(III) complex were analyzed in urine samples from human subjects collected after the ingestion of 300 mg of DMPS. The administration of DMPS resulted in a decrease of the DMA(V) concentration and an increase of the MMA(V) concentration excreted in the urine, confirming the previous results. The finding of the DMPS-MMA(III) complex in human urine after DMPS treatment provides an explanation for the inhibition of arsenic methylation by DMPS. Because MMA(III) is the substrate for the biomethylation of arsenic from MMA(V) to DMA(V), the formation of DMPS-MMA(III) complex would reduce the availability of MMA(III) for the subsequent biomethylation.
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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 | 0.001 | 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".