Speciation of Dimethylarsinous Acid and Trimethylarsine Oxide in Urine from Rats Fed with Dimethylarsinic Acid and Dimercaptopropane Sulfonate
Bibliographic record
Abstract
Speciation of arsenic in urine from rats treated with dimethylarsinic acid (DMA(V)) alone or in combination with dimercaptopropane sulfonate (DMPS) were studied. Methods were developed for the determination of the methylarsenic metabolites, especially trace levels of dimethylarsinous acid (DMA(III)) and trimethylarsine oxide (TMAO), in the presence of a large excess of DMA(V). Success was achieved by using improved ion-exchange chromatographic separation combined with hydride generation atomic fluorescence detection. Micromolar concentrations of DMA(III) were detected in urine of rats fed with a diet supplemented with either 100 microg/g of DMA(V) or a mixture of 100 microg/g of DMA(V) and 5600 microg/g of DMPS. No significant difference in the DMA(III) concentration was observed between the two groups; however, there was a significant difference in TMAO concentrations. Urine from rats fed with the diet supplemented with DMA(V) alone contained 73 +/- 30 microM TMAO, whereas urine from rats fed with the diet supplemented with both DMA(V) and DMPS contained only 2.8 +/- 1.4 microM TMAO. Solutions containing mixtures of 100 microg/L DMA(V) or TMAO and 5600 microg/L DMPS did not show reduction of DMA(V) and TMAO. The significant decrease (p < 0.001) of the TMAO concentration in rats administered with both DMA(V) and DMPS suggests that DMPS inhibits the biomethylation 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".