Speciation of Key Arsenic Metabolic Intermediates in Human Urine
Bibliographic record
Abstract
Biomethylation is the major human metabolic pathway for inorganic arsenic, and the speciation of arsenic metabolites is essential to a better understanding of arsenic metabolism and health effects. Here we describe a technique for the speciation of arsenic in human urine and demonstrate its application to the discovery of key arsenic metabolic intermediates, monomethylarsonous acid (MMAIII) and dimethylarsinous acid (DMAIII), in human urine. The study provides a direct evidence in support of the proposed arsenic methylation pathway in the human. The finding of MMAIII and DMAIII in human urine, along with recent studies showing the high toxicity of these arsenicals, suggests that the usual belief of arsenic detoxification by methylation needs to be reconsidered. The arsenic speciation technique is based on ion pair chromatographic separation of arsenic species on a 3-micron particle size column at 50 degrees C followed by hydride generation atomic fluorescence detection. Speciation of MMAIII, DMAIII, arsenite (AsIII), arsenate (AsV), monomethylarsonic acid (MMAV), and dimethylarsinic acid (DMAV) in urine samples is complete in 6 min with detection limits of 0.5-2 micrograms/L. There is no need for any sample pretreatment. The capability of rapid analysis of trace levels of arsenic species, which resulted in the findings of the key metabolic intermediates, makes the technique useful for routine arsenic speciation analysis required for toxicological and epidemiological studies.
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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".