Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches

doi:10.1016/j.scitotenv.2023.162103

	Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches
	Wang, Xiaoqing1,3 ; Li, Fei1,2 ; Teng, Yuefa 1,3; Ji, Chenglong1,2 ; Wu, Huifeng1,2
发表期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
	2023-05-01
卷号	871 页码:11
关键词	Toxic effects Machine learning Risk assessment Mechanism -driven analysis TiO2-based engineered nanoparticles(nTiO2)
DOI	10.1016/j.scitotenv.2023.162103
通讯作者	Li, Fei([email protected])
英文摘要	The wide application of TiO2-based engineered nanoparticles (nTiO2) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO2 to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO2 on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) - catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) - glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with high accuracy and balanced accuracy for both training sets and external validation sets. Furthermore, the best model revealed the predominant factors (exposure concentration, exposure periods, and exposure matrix) influencing the oxidative stress induced by nTiO2. These results showed that high exposure concentrations and short exposure-intervals tended to cause oxidative damage to bivalves. In addition, gills and digestive glands could be vulnerable to nTiO2-induced oxidative damage as tissues/organs differences were the important factors controlling MDA activity. This study provided insights into important nano-features responsible for the different indicators of oxidative stress and thereby extended the application of machine learning approaches in toxicological assessment for nanoparticles.
资助机构	Yantai Science and Technology Development Plan ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
收录类别	SCI
语种	英语
关键词[WOS]	TITANIUM-DIOXIDE NANOPARTICLES ; BIVALVE CORBICULA-FLUMINEA ; METAL-OXIDE NANOPARTICLES ; TIO2 NANOPARTICLES ; DAPHNIA-MAGNA ; ENGINEERED NANOPARTICLES ; BIOCHEMICAL RESPONSES ; ESCHERICHIA-COLI ; MYTILUS-EDULIS ; GLOBAL TRENDS
研究领域[WOS]	Environmental Sciences & Ecology
WOS记录号	WOS:001022306300001
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/36793
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室
通讯作者	Li, Fei
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res YIC, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Shandong Key Lab Coastal Environm Proc,YICCAS, Yantai 264003, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiaoqing,Li, Fei,Teng, Yuefa,et al. Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2023,871:11.
APA	Wang, Xiaoqing,Li, Fei,Teng, Yuefa,Ji, Chenglong,&Wu, Huifeng.(2023).Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches.SCIENCE OF THE TOTAL ENVIRONMENT,871,11.
MLA	Wang, Xiaoqing,et al."Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches".SCIENCE OF THE TOTAL ENVIRONMENT 871(2023):11.