ArticleEnvironment & health (Washington, D.C.)2026
Exploring the Impact of E‑Waste Exposure on Childhood Blood Pressure: Metabolomics Analysis and Risk Prediction.
Article in Environment & health (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The alarming burden of hypertension in electronic waste (e-waste) recycling areas (EA) constitutes a pressing public health challenge. While growing evidence links changes in blood pressure to volatile organic compounds (VOCs) and metal-(loid) exposure, knowledge gaps persist in understanding the molecular mechanisms and developing operational screening tools. We aim to investigate the effects of VOC and metal-(loid) coexposure on the blood pressure of children in EA, uncover metabolic mechanisms via urinary metabolomics, and develop a machine learning algorithm-driven predictive model for identifying children with potential hypertension risks. We measured systolic (SBP) and diastolic (DBP) blood pressures, urinary VOC metabolites (mVOCs), metal-(loid)-s, and metabolomic profiles in EA children (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.