High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review.
Jifu LiuTongtong XiangXue-Chao SongShaoqing ZhangQi WuJie GaoMeilin LvChunzhen ShiXiaoxi YangYanna LiuJianjie FuWei ShiMingliang FangGuangbo QuHongxia YuGui-Bin JiangPublished in: Environmental science & technology (2024)
Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.
Keyphrases
- big data
- artificial intelligence
- human health
- high resolution
- healthcare
- machine learning
- risk assessment
- high resolution mass spectrometry
- cardiovascular disease
- gene expression
- mass spectrometry
- single cell
- climate change
- ms ms
- high efficiency
- dna methylation
- coronary artery disease
- life cycle
- liquid chromatography tandem mass spectrometry
- global health
- gas chromatography