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腕带式个人被动采样器和可疑筛选方法突出了化学暴露中的性别差异。

Wristband Personal Passive Samplers and Suspect Screening Methods Highlight Gender Disparities in Chemical Exposures.

机构信息

Nicholas School of the Environment, Duke University, Durham, North Carolina 27710, United States.

School of Environmental Sustainability, Loyola University Chicago, Chicago, Illinois 60660, United States.

出版信息

Environ Sci Technol. 2024 Sep 3;58(35):15497-15510. doi: 10.1021/acs.est.4c06008. Epub 2024 Aug 22.

Abstract

Wristband personal samplers enable human exposure assessments for a diverse range of chemical contaminants and exposure settings with a previously unattainable scale and cost-effectiveness. Paired with nontargeted analyses, wristbands can provide important exposure monitoring data to expand our understanding of the environmental exposome. Here, a custom scripted suspect screening workflow was developed in the R programming language for feature selection and chemical annotations using gas chromatography-high-resolution mass spectrometry data acquired from the analysis of wristband samples collected from five different cohorts. The workflow includes blank subtraction, internal standard normalization, prediction of chemical uses in products, and feature annotation using multiple library search metrics and metadata from PubChem, among other functionalities. The workflow was developed and validated against 104 analytes identified by targeted analytical results in previously published reports of wristbands. A true positive rate of 62 and 48% in a quality control matrix and wristband samples, respectively, was observed for our optimum set of parameters. Feature analysis identified 458 features that were significantly higher on female-worn wristbands and only 21 features that were significantly higher on male-worn wristbands across all cohorts. Tentative identifications suggest that personal care products are a primary driver of the differences observed.

摘要

腕带个人采样器使人类暴露评估能够涵盖广泛的化学污染物和暴露环境,且具有以前无法达到的规模和成本效益。与非靶向分析相结合,腕带可以提供重要的暴露监测数据,从而扩展我们对环境暴露组学的理解。在这里,我们使用 R 编程语言开发了一个自定义脚本可疑筛选工作流程,用于使用从五个不同队列的腕带样本分析中获得的气相色谱-高分辨率质谱数据进行特征选择和化学注释。该工作流程包括空白扣除、内标归一化、预测产品中的化学用途,以及使用多种库搜索指标和来自 PubChem 的元数据进行特征注释等功能。该工作流程是针对之前发表的腕带报告中靶向分析结果确定的 104 种分析物进行开发和验证的。在质量控制矩阵和腕带样本中,我们最优参数的真阳性率分别为 62%和 48%。特征分析确定了 458 个在所有队列中女性佩戴的腕带上显著更高的特征,而只有 21 个在男性佩戴的腕带上显著更高的特征。暂定鉴定表明,个人护理产品是观察到的差异的主要驱动因素。

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