Arsenic speciation in saliva of acute promyelocytic leukemia patients undergoing arsenic trioxide treatment
Bibliographic record
Abstract
Arsenic trioxide has been successfully used as a therapeutic in the treatment of acute promyelocytic leukemia (APL). Detailed monitoring of the therapeutic arsenic and its metabolites in various accessible specimens of APL patients can contribute to improving treatment efficacy and minimizing arsenic-induced side effects. This article focuses on the determination of arsenic species in saliva samples from APL patients undergoing arsenic treatment. Saliva samples were collected from nine APL patients over three consecutive days. The patients received 10 mg arsenic trioxide each day via intravenous infusion. The saliva samples were analyzed using high-performance liquid chromatography coupled with inductively coupled plasma mass spectrometry. Monomethylarsonous acid and monomethylmonothioarsonic acid were identified along with arsenite, dimethylarsinic acid, monomethylarsonic acid, and arsenate. Arsenite was the predominant arsenic species, accounting for 71.8 % of total arsenic in the saliva. Following the arsenic infusion each day, the percentage of methylated arsenicals significantly decreased, possibly suggesting that the arsenic methylation process was saturated by the high doses immediately after the arsenic infusion. The temporal profiles of arsenic species in saliva following each arsenic infusion over 3 days have provided information on arsenic exposure, metabolism, and excretion. These results suggest that saliva can be used as an appropriate clinical biomarker for monitoring arsenic species in APL patients.
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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) | 0.001 | 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".