Speciation of Key Arsenic Metabolic Intermediates in Human Urine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.029 | 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 it