Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches
Wang, Xiaoqing1,3; Li, Fei1,2; Teng, Yuefa1,3; Ji, Chenglong1,2; Wu, Huifeng1,2
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
2023-05-01
Volume871Pages:11
KeywordToxic effects Machine learning Risk assessment Mechanism -driven analysis TiO2-based engineered nanoparticles(nTiO2)
DOI10.1016/j.scitotenv.2023.162103
Corresponding AuthorLi, Fei(fli@yic.ac.cn)
AbstractThe 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.
Funding OrganizationYantai Science and Technology Development Plan ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
Indexed BySCI
Language英语
WOS KeywordTITANIUM-DIOXIDE NANOPARTICLES ; BIVALVE CORBICULA-FLUMINEA ; METAL-OXIDE NANOPARTICLES ; TIO2 NANOPARTICLES ; DAPHNIA-MAGNA ; ENGINEERED NANOPARTICLES ; BIOCHEMICAL RESPONSES ; ESCHERICHIA-COLI ; MYTILUS-EDULIS ; GLOBAL TRENDS
WOS Research AreaEnvironmental Sciences & Ecology
WOS IDWOS:001022306300001
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.yic.ac.cn/handle/133337/33346
Collection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
Corresponding AuthorLi, Fei
Affiliation1.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
Recommended Citation
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.
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