Arsenic Speciation in Urine from Acute Promyelocytic Leukemia Patients undergoing Arsenic Trioxide Treatment
Bibliographic record
Abstract
Arsenic has been used successfully in clinical trials for treating acute promyelocytic leukemia (APL). Although sublethal doses of inorganic arsenic are used, little is known about the pharmacokinetics and metabolism of the high levels of arsenic in APL patients. To fill this important gap, this study describes the speciation of arsenic in urine from four APL patients treated with arsenic. Each patient was injected daily with an arsenite (As(III)) solution that contained 10 mg of As(2)O(3) precursor. Speciation analysis of the patient urine samples collected consecutively for 48 h, encompassing two intravenous injections of arsenic, revealed the presence of monomethylarsonous acid (MMA(III)), dimethylarsinous acid (DMA(III)), monomethylarsonic acid (MMA(V)), and dimethylarsinic acid (DMA(V)). The intermediate methyl arsenic metabolites, MMA(III) and DMA(III), were detected in most urine samples from all of the patients when a preservative, diethyldithiocarbomate, was added to the urine samples to stabilize these trivalent arsenic species. The major arsenic species detected in the urine samples from the patients were As(III), MMA(V), and DMA(V), accounting for >95% of the total arsenic excreted. The relative proportions of As(III), As(V), MMA(V), and DMA(V) in urine samples collected 24 h after the injections of As(III) were 27.6 +/- 6.1, 2.8 +/- 2.0, 22.8 +/- 8.1, and 43.7 +/- 13.3%, respectively. The relatively lower fraction of the methylated arsenic species in these APL patients under arsenic treatment as compared with that from the general population exposed to much lower levels of arsenic suggests that the high levels of As(III) inhibit the methylation of arsenic (inhibits the formation of methyl arsenic metabolites). The arsenic species excreted into the urine accounted for 32-65% of the total arsenic injected. These results suggest that other pathways of excretion, such as through the bile, may play an important role in eliminating (removing) arsenic from the human body when challenged by high levels of As(III).
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".