Arsenic Speciation Analysis in Human Saliva
Bibliographic record
Abstract
BACKGROUND: Determination of arsenic species in saliva is potentially useful for biomonitoring of human exposure and studying arsenic metabolism. Arsenic speciation in saliva has not been reported previously. METHODS: We separated arsenic species in saliva using liquid chromatography (LC) and quantified them by inductively coupled plasma mass spectrometry. We further confirmed the identities of arsenic species by LC coupled with electrospray ionization tandem mass spectrometry. These methods were successfully applied to the determination of arsenite (As(III)), arsenate (As(V)), and their methylation metabolites, monomethylarsonic acid (MMA(V)), and dimethylarsinic acid (DMA(V)), in >300 saliva samples collected from people who were exposed to varying concentrations of arsenic. RESULTS: The mean (range) concentrations (microg/L) in the saliva samples from 32 volunteers exposed to background levels of arsenic were As(III) 0.3 [not detectable (ND) to 0.7], As(V) 0.3 (ND to 0.5), MMA(V) 0.1 (ND to 0.2), and DMA(V) 0.7 (ND to 2.6). Samples from 301 people exposed to increased concentrations of arsenic in drinking water showed detectable As(III) in 99%, As(V) in 98%, MMA(V) in 80%, and DMA(V) in 68% of samples. The mean (range) concentrations of arsenic species in these saliva samples were (in microg/L) As(III) 2.8 (0.1-38), As(V) 8.1 (0.3-120), MMA(V) 0.8 (0.1-6.0), and DMA(V) 0.4 (0.1-3.9). Saliva arsenic correlated with drinking water arsenic. Odds ratios for skin lesions increased with saliva arsenic concentrations. The association between saliva arsenic concentrations and the prevalence of skin lesions was statistically significant (P <0.001). CONCLUSIONS: Speciation of As(V), As(III), MMA(V), and DMA(V) in human saliva is a useful method for monitoring arsenic exposure.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".