Arsenic exposure and pruritus: evidence from observational, interventional, and Mendelian randomization studies
Bibliographic record
Abstract
Background: Pruritus has been reported as an adverse drug reaction to arsenic trioxide, but the association of arsenic exposure with pruritus has not been systematically investigated. To investigate the association of arsenic exposure with pruritus, we performed observational, interventional, and Mendelian randomization studies. Methods: A cross-sectional study was conducted in Shimen, China. A Mendelian randomization study was conducted to confirm the causal relationship between susceptibility to arsenic toxicity, in terms of genetically predicted percentages of monomethylated arsenic (MMA%) and dimethylated arsenic (DMA%) in urine, and chronic pruritus in the UK Biobank participants. Then, a case-control study in Shimen participants was conducted to determine the biomarker for pruritus, and arsenite-treated mice were used to confirm the biomarker. Last, a randomized, double-blind, placebo-controlled trial was conducted to test the efficacy of naloxone, a μ-opioid receptor antagonist, in arsenic-exposed patients with pruritus in Shimen. Results: Hair arsenic showed a dose-response relationship with the intensity of itch in 1092 participants. The Mendelian randomization analysis confirmed the causal relationship in the UK Biobank participants, with odds ratios of 1.043 for MMA% and 0.904 for DMA% above versus under median. Serum β-endorphin was identified as a significant biomarker associated with the intensity of itch. Consistently, treatment with arsenite in mice upregulated the level of β-endorphin. The randomized controlled trial showed that treatment with sublingual naloxone significantly relieved the intensity of itch in arsenic-exposed participants. Conclusion: Arsenic exposure is associated with pruritus, and β-endorphin serves as a biomarker of pruritus. Naloxone relieves pruritus in patients with [arseniasis](javascript:;).
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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 | 0.084 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".