1
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Xuan Y, Wang Y, Li R, Zhong Y, Wang N, Zhang L, Chen Q, Yu S, Yuan J. Using machine learning to classify the immunosuppressive activity of per- and polyfluoroalkyl substances. Toxicol Mech Methods 2024:1-9. [PMID: 39104137 DOI: 10.1080/15376516.2024.2387733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/28/2024] [Accepted: 07/29/2024] [Indexed: 08/07/2024]
Abstract
Per- and polyfluoroalkyl substances (PFASs), one of the persistent organic pollutants, have immunosuppressive effects. The evaluation of this effect has been the focus of regulatory toxicology. In this investigation, 146 PFASs (immunosuppressive or nonimmunosuppressive) and corresponding concentration gradients were collected from literature, and their structures were characterized by using Dragon descriptors. Feature importance analysis and stepwise feature elimination are used for feature selection. Three machine learning (ML) methods, namely Random Forest (RF), Extreme Gradient Boosting Machine (XGB), and Categorical Boosting Machine (CB), were utilized for model development. The model interpretability was explored by feature importance analysis and correlation analysis. The findings indicated that the three models developed have exhibited excellent performance. Among them, the best-performing RF model has an average AUC score of 0.9720 for the testing set. The results of the feature importance analysis demonstrated that concentration, SpPosA_X, IVDE, R2s, and SIC2 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. In conclusion, this study is the first application of ML models to investigate the immunosuppressive activity of PFASs. The variables used in the models can help understand the mechanism of the immunosuppressive activity of PFASs, allow researchers to more effectively assess the immunosuppressive potential of a large number of PFASs, and thus better guide environmental and health risk assessment efforts.
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Affiliation(s)
- Yuxin Xuan
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Yulu Wang
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Rui Li
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Yuyan Zhong
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Na Wang
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Lingyin Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Qian Chen
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Shuling Yu
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University, Kaifeng, Henan, P. R. China
| | - Jintao Yuan
- College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
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2
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Byeon E, Jeong H, Kim MS, Yun SC, Lee JS, Lee MC, Kim JH, Sayed AEDH, Bo J, Kim HS, Yoon C, Hagiwara A, Sakakura Y, Lee JS. Toxicity and speciation of inorganic arsenics and their adverse effects on in vivo endpoints and oxidative stress in the marine medaka Oryzias melastigma. JOURNAL OF HAZARDOUS MATERIALS 2024; 473:134641. [PMID: 38788572 DOI: 10.1016/j.jhazmat.2024.134641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 04/24/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Here, we investigate the effects of acute and chronic exposure to arsenate (AsV) and arsenite (AsIII) in the marine medaka Oryzias melastigma. In vivo effects, biotransformation, and oxidative stress were studied in marine medaka exposed to the two inorganic arsenics for 4 or 28 days. An investigation of embryonic development revealed no effect on in vivo parameters, but the hatching rate increased in the group exposed to AsIII. Exposure to AsIII also caused the greatest accumulation of arsenic in medaka. For acute exposure, the ratio of AsV to AsIII was higher than that of chronic exposure, indicating that bioaccumulation of inorganic arsenic can induce oxidative stress. The largest increase in oxidative stress was observed following acute exposure to AsIII, but no significant degree of oxidative stress was induced by chronic exposure. During acute exposure to AsV, the increase in the enzymatic activity of glutathione-S-transferase (GST) was twice as high compared with exposure to AsIII, suggesting that GST plays an important role in the initial detoxification process. In addition, an RNA-seq-based ingenuity pathway analysis revealed that acute exposure to AsIII may be related to cell-cycle progression. A network analysis using differentially expressed genes also revealed a potential link between the generation of inflammatory cytokines and oxidative stress due to arsenic exposure.
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Affiliation(s)
- Eunjin Byeon
- Department of Biological Sciences, College of Science, Sungkyunkwan University, Suwon 16419, South Korea
| | - Haksoo Jeong
- Department of Biological Sciences, College of Science, Sungkyunkwan University, Suwon 16419, South Korea
| | - Min-Sub Kim
- Department of Biological Sciences, College of Science, Sungkyunkwan University, Suwon 16419, South Korea
| | - Seong Chan Yun
- Department of Biological Sciences, College of Science, Sungkyunkwan University, Suwon 16419, South Korea
| | - Jin-Sol Lee
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, South Korea
| | - Min-Chul Lee
- Department of Food & Nutrition, College of Bio-Nano Technology, Gachon University, Seongnam 13120, South Korea
| | - Jin-Hyoung Kim
- Division of Life Sciences, Korea Polar Research Institute, Incheon 21990, South Korea
| | | | - Jun Bo
- Laboratory of Marine Biology and Ecology, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Hyung Sik Kim
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, South Korea
| | - Chulho Yoon
- Ochang Center, Korea Basic Science Institute, Cheongju 28119, South Korea
| | - Atsushi Hagiwara
- Institute of Integrated Science and Technology, Graduate School of Fisheries Science and Environmental Sciences, Nagasaki University, Nagasaki 852-8521, Japan
| | - Yoshitaka Sakakura
- Institute of Integrated Science and Technology, Graduate School of Fisheries Science and Environmental Sciences, Nagasaki University, Nagasaki 852-8521, Japan
| | - Jae-Seong Lee
- Department of Biological Sciences, College of Science, Sungkyunkwan University, Suwon 16419, South Korea.
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3
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Zeng B, Wu Y, Huang Y, Colucci M, Bancaro N, Maddalena M, Valdata A, Xiong X, Su X, Zhou X, Zhang Z, Jin Y, Huang W, Bai J, Zeng Y, Zou X, Zhan Y, Deng L, Wei Q, Yang L, Alimonti A, Qi F, Qiu S. Carcinogenic health outcomes associated with endocrine disrupting chemicals exposure in humans: A wide-scope analysis. JOURNAL OF HAZARDOUS MATERIALS 2024; 476:135067. [PMID: 38964039 DOI: 10.1016/j.jhazmat.2024.135067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Abstract
Endocrine-disrupting chemicals (EDCs) are persistent and pervasive compounds that pose serious risks. Numerous studies have explored the effects of EDCs on human health, among which tumors have been the primary focus. However, because of study design flaws, lack of effective exposure levels of EDCs, and inconsistent population data and findings, it is challenging to draw clear conclusions on the effect of these compounds on tumor-related outcomes. Our study is the first to systematically integrate observational studies and randomized controlled trials from over 20 years and summarize over 300 subgroup associations. We found that most EDCs promote tumor development, and that exposure to residential environmental pollutants may be a major source of pesticide exposure. Furthermore, we found that phytoestrogens exhibit antitumor effects. The findings of this study can aid in the development of global EDCs regulatory health policies and alleviate the severe risks associated with EDCs exposure.
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Affiliation(s)
- Bin Zeng
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuwei Wu
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yin Huang
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Manuel Colucci
- Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), CH6500 Bellinzona, Switzerland; Università della Svizzera Italiana, CH6900 Lugano, Switzerland
| | - Nicolò Bancaro
- Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), CH6500 Bellinzona, Switzerland; Università della Svizzera Italiana, CH6900 Lugano, Switzerland
| | - Martino Maddalena
- Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), CH6500 Bellinzona, Switzerland; Università della Svizzera Italiana, CH6900 Lugano, Switzerland
| | - Aurora Valdata
- Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), CH6500 Bellinzona, Switzerland; Università della Svizzera Italiana, CH6900 Lugano, Switzerland
| | - Xingyu Xiong
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xingyang Su
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xianghong Zhou
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Zilong Zhang
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuming Jin
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Weichao Huang
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Jincheng Bai
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuxiao Zeng
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xiaoli Zou
- Department of Sanitary Technology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, China
| | - Linghui Deng
- National Clinical Research Center of Geriatrics, The Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China; Neurodegenerative Disorders Lab, Laboratories for Translational Research, Ente Ospedaliero Cantonale, Bellinzona, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Qiang Wei
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Lu Yang
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Andrea Alimonti
- Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), CH6500 Bellinzona, Switzerland; Università della Svizzera Italiana, CH6900 Lugano, Switzerland; Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Fang Qi
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563000, China.
| | - Shi Qiu
- Department of Urology, Institute of Urology and National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Università della Svizzera Italiana, CH6900 Lugano, Switzerland; Department of Sanitary Technology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
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4
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Wang F, Xiang L, Sze-Yin Leung K, Elsner M, Zhang Y, Guo Y, Pan B, Sun H, An T, Ying G, Brooks BW, Hou D, Helbling DE, Sun J, Qiu H, Vogel TM, Zhang W, Gao Y, Simpson MJ, Luo Y, Chang SX, Su G, Wong BM, Fu TM, Zhu D, Jobst KJ, Ge C, Coulon F, Harindintwali JD, Zeng X, Wang H, Fu Y, Wei Z, Lohmann R, Chen C, Song Y, Sanchez-Cid C, Wang Y, El-Naggar A, Yao Y, Huang Y, Cheuk-Fung Law J, Gu C, Shen H, Gao Y, Qin C, Li H, Zhang T, Corcoll N, Liu M, Alessi DS, Li H, Brandt KK, Pico Y, Gu C, Guo J, Su J, Corvini P, Ye M, Rocha-Santos T, He H, Yang Y, Tong M, Zhang W, Suanon F, Brahushi F, Wang Z, Hashsham SA, Virta M, Yuan Q, Jiang G, Tremblay LA, Bu Q, Wu J, Peijnenburg W, Topp E, Cao X, Jiang X, Zheng M, Zhang T, Luo Y, Zhu L, Li X, Barceló D, Chen J, Xing B, Amelung W, Cai Z, Naidu R, Shen Q, Pawliszyn J, Zhu YG, Schaeffer A, Rillig MC, Wu F, Yu G, Tiedje JM. Emerging contaminants: A One Health perspective. Innovation (N Y) 2024; 5:100612. [PMID: 38756954 PMCID: PMC11096751 DOI: 10.1016/j.xinn.2024.100612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.
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Affiliation(s)
- Fang Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leilei Xiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
- HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, China
| | - Martin Elsner
- Technical University of Munich, TUM School of Natural Sciences, Institute of Hydrochemistry, 85748 Garching, Germany
| | - Ying Zhang
- School of Resources & Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Bo Pan
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Hongwen Sun
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Taicheng An
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Guangguo Ying
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Bryan W. Brooks
- Department of Environmental Science, Baylor University, Waco, TX, USA
- Center for Reservoir and Aquatic Systems Research (CRASR), Baylor University, Waco, TX, USA
| | - Deyi Hou
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Damian E. Helbling
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Jianqiang Sun
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hao Qiu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Timothy M. Vogel
- Laboratoire d’Ecologie Microbienne, Universite Claude Bernard Lyon 1, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France
| | - Wei Zhang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yanzheng Gao
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Myrna J. Simpson
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Yi Luo
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Scott X. Chang
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Bryan M. Wong
- Materials Science & Engineering Program, Department of Chemistry, and Department of Physics & Astronomy, University of California-Riverside, Riverside, CA, USA
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Dong Zhu
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Karl J. Jobst
- Department of Chemistry, Memorial University of Newfoundland, 45 Arctic Avenue, St. John’s, NL A1C 5S7, Canada
| | - Chengjun Ge
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, School of Ecological and Environmental Sciences, Hainan University, Haikou 570228, China
| | - Frederic Coulon
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Jean Damascene Harindintwali
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiankui Zeng
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Haijun Wang
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China
| | - Yuhao Fu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Wei
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Rainer Lohmann
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA
| | - Changer Chen
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Yang Song
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Concepcion Sanchez-Cid
- Environmental Microbial Genomics, UMR 5005 Laboratoire Ampère, CNRS, École Centrale de Lyon, Université de Lyon, Écully, France
| | - Yu Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ali El-Naggar
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
- Department of Soil Sciences, Faculty of Agriculture, Ain Shams University, Cairo 11241, Egypt
| | - Yiming Yao
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Yanran Huang
- Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China
| | | | - Chenggang Gu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yanpeng Gao
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Chao Qin
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Hao Li
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Natàlia Corcoll
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Min Liu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Daniel S. Alessi
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
| | - Hui Li
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Kristian K. Brandt
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
- Sino-Danish Center (SDC), Beijing, China
| | - Yolanda Pico
- Food and Environmental Safety Research Group of the University of Valencia (SAMA-UV), Desertification Research Centre - CIDE (CSIC-UV-GV), Road CV-315 km 10.7, 46113 Moncada, Valencia, Spain
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Jianhua Guo
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jianqiang Su
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Philippe Corvini
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland
| | - Mao Ye
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Teresa Rocha-Santos
- Centre for Environmental and Marine Studies (CESAM) & Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Huan He
- Jiangsu Engineering Laboratory of Water and Soil Eco-remediation, School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Yi Yang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Meiping Tong
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Weina Zhang
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Fidèle Suanon
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- Laboratory of Physical Chemistry, Materials and Molecular Modeling (LCP3M), University of Abomey-Calavi, Republic of Benin, Cotonou 01 BP 526, Benin
| | - Ferdi Brahushi
- Department of Environment and Natural Resources, Agricultural University of Tirana, 1029 Tirana, Albania
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment & Ecology, Jiangnan University, Wuxi 214122, China
| | - Syed A. Hashsham
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marko Virta
- Department of Microbiology, University of Helsinki, 00010 Helsinki, Finland
| | - Qingbin Yuan
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Gaofei Jiang
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Louis A. Tremblay
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa 1142, New Zealand
| | - Qingwei Bu
- School of Chemical & Environmental Engineering, China University of Mining & Technology - Beijing, Beijing 100083, China
| | - Jichun Wu
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Willie Peijnenburg
- National Institute of Public Health and the Environment, Center for the Safety of Substances and Products, 3720 BA Bilthoven, The Netherlands
- Leiden University, Center for Environmental Studies, Leiden, the Netherlands
| | - Edward Topp
- Agroecology Mixed Research Unit, INRAE, 17 rue Sully, 21065 Dijon Cedex, France
| | - Xinde Cao
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin Jiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minghui Zheng
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Taolin Zhang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Yongming Luo
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lizhong Zhu
- Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Damià Barceló
- Chemistry and Physics Department, University of Almeria, 04120 Almeria, Spain
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA 01003, USA
| | - Wulf Amelung
- Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, 53115 Bonn, Germany
- Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Ravi Naidu
- Global Centre for Environmental Remediation (GCER), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
- Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
| | - Qirong Shen
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Yong-guan Zhu
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Andreas Schaeffer
- Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany
| | - Matthias C. Rillig
- Institute of Biology, Freie Universität Berlin, Berlin, Germany
- Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Gang Yu
- Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University, Zhuhai, China
| | - James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
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5
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Balraadjsing S, J G M Peijnenburg W, Vijver MG. Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size. ENVIRONMENT INTERNATIONAL 2024; 188:108764. [PMID: 38788418 DOI: 10.1016/j.envint.2024.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/17/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024]
Abstract
A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.
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Affiliation(s)
- Surendra Balraadjsing
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands.
| | - Willie J G M Peijnenburg
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands; Centre for Safety of Substances and Products, National Institute of Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, the Netherlands
| | - Martina G Vijver
- Institute of Environmental Sciences (CML), Leiden University, PO Box 9518, 2300 RA Leiden, the Netherlands
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6
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Li H, Li H, Zuo N, Lang D, Du W, Zhang P, Pan B. Can the concentration of environmentally persistent free radicals describe its toxicity to Caenorhabditis elegans? Evidence provided by neurotoxicity and oxidative stress. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133823. [PMID: 38442598 DOI: 10.1016/j.jhazmat.2024.133823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/16/2024] [Indexed: 03/07/2024]
Abstract
Environmentally persistent free radicals (EPFRs) are emerging pollutants stabilized on or inside particles. Although the toxicity of EPFR-containing particles has been confirmed, the conclusions are always ambiguous because of the presence of various compositions. A clear dose-response relationship was always challenged by the fact that the concentrations of these coexisted components simultaneously changed with EPFR concentrations. Without these solid dose-response pieces of evidence, we could not confidently conclude the toxicity of EPFRs and the description of potential EPFR risks. In this study, we established a particle system with a fixed catechol concentration but different reaction times to obtain particles with different EPFR concentrations. Caenorhabditis elegans (C. elegans) in response to different EPFR concentrations was systematically investigated at multiple biological levels, including behavior observations and biochemical and transcriptome analyses. Our results showed that exposure to EPFRs disrupted the development and locomotion of C. elegans. EPFRs cause concentration-dependent neurotoxicity and oxidative damage to C. elegans, which could be attributed to reactive oxygen species (ROS) promoted by EPFRs. Furthermore, the expression of key genes related to neurons was downregulated, whereas antioxidative genes were upregulated. Overall, our results confirmed the toxicity from EPFRs and EPFR concentration as a rational parameter to describe the extent of toxicity.
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Affiliation(s)
- Huijie Li
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Hao Li
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
| | - Ning Zuo
- Yunnan Research Academy of Eco-environmental Science, Kunming 650034, China
| | - Di Lang
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Wei Du
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Peng Zhang
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Bo Pan
- Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
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7
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Su A, Cheng Y, Zhang C, Yang YF, She YB, Rajan K. An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171229. [PMID: 38402985 DOI: 10.1016/j.scitotenv.2024.171229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/27/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.
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Affiliation(s)
- An Su
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, PR China.
| | - Yingying Cheng
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, PR China
| | - Chengwei Zhang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Yun-Fang Yang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Yuan-Bin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Krishna Rajan
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260-1660, United States.
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8
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Azhagiya Singam E, Durkin KA, La Merrill MA, Furlow JD, Wang JC, Smith MT. Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4487-4499. [PMID: 38422483 PMCID: PMC10938639 DOI: 10.1021/acs.est.3c05974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/02/2024]
Abstract
Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.
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Affiliation(s)
| | - Kathleen A. Durkin
- Molecular
Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, California 94720, United States
| | - Michele A. La Merrill
- Department
of Environmental Toxicology, University
of California, Davis, California 95616, United States
| | - J. David Furlow
- Department
of Neurobiology, Physiology and Behavior, University of California, Davis California 95616, United States
| | - Jen-Chywan Wang
- Department
of Nutritional Sciences and Toxicology, University of California, Berkeley, California 94720, United States
| | - Martyn T. Smith
- Division
of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
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9
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Mudlaff M, Sosnowska A, Gorb L, Bulawska N, Jagiello K, Puzyn T. Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling. ENVIRONMENT INTERNATIONAL 2024; 185:108568. [PMID: 38493737 DOI: 10.1016/j.envint.2024.108568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/08/2024] [Accepted: 03/05/2024] [Indexed: 03/19/2024]
Abstract
Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.
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Affiliation(s)
| | - Anita Sosnowska
- QSAR Lab, Trzy Lipy 3, 80-172 Gdańsk, Poland; University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Leonid Gorb
- QSAR Lab, Trzy Lipy 3, 80-172 Gdańsk, Poland; Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, 150 Zabolotnogo str., 03680 Kyiv, Ukraine
| | - Natalia Bulawska
- QSAR Lab, Trzy Lipy 3, 80-172 Gdańsk, Poland; University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Karolina Jagiello
- QSAR Lab, Trzy Lipy 3, 80-172 Gdańsk, Poland; University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tomasz Puzyn
- QSAR Lab, Trzy Lipy 3, 80-172 Gdańsk, Poland; University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland.
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10
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Nannaware M, Mayilswamy N, Kandasubramanian B. PFAS: exploration of neurotoxicity and environmental impact. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:12815-12831. [PMID: 38277101 DOI: 10.1007/s11356-024-32082-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are widespread contaminants stemming from various industrial and consumer products, posing a grave threat to both human health and ecosystems. PFAS contamination arises from multiple sources, including industrial effluents, packaging, and product manufacturing, accumulating in plants and impacting the food chain. Elevated PFAS levels in water bodies pose significant risks to human consumption. This review focuses on PFAS-induced neurological effects, highlighting disrupted dopamine signalling and structural neuron changes in humans. Animal studies reveal apoptosis and hippocampus dysfunction, resulting in memory loss and spatial learning issues. The review introduces the BKMR model, a machine learning technique, to decipher intricate PFAS-neurotoxicity relationships. Epidemiological data underscores the vulnerability of young brains to PFAS exposure, necessitating further research. Stricter regulations, industry monitoring, and responsible waste management are crucial steps to reduce PFAS exposure.
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Affiliation(s)
- Mrunal Nannaware
- Department of Chemical Engineering, Institute of Chemical Technology Mumbai, Marathwada Campus Jalna, Jalna, 431203, India
| | - Neelaambhigai Mayilswamy
- Department of Metallurgical and Material Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune, 411025, Maharashtra, India
| | - Balasubramanian Kandasubramanian
- Department of Metallurgical and Material Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune, 411025, Maharashtra, India.
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11
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Biswas S, Wong BM. Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances. ACS ES&T ENGINEERING 2024; 4:96-104. [PMID: 38229882 PMCID: PMC10788865 DOI: 10.1021/acsestengg.3c00216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 01/18/2024]
Abstract
Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.
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Affiliation(s)
- Sohag Biswas
- Materials Science & Engineering
Program, Department of Chemistry, and Department of Physics &
Astronomy, University of California-Riverside, Riverside, California 92521, United States
| | - Bryan M. Wong
- Materials Science & Engineering
Program, Department of Chemistry, and Department of Physics &
Astronomy, University of California-Riverside, Riverside, California 92521, United States
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12
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Yu X. Global classification models for predicting acute toxicity of chemicals towards Daphnia magna. ENVIRONMENTAL RESEARCH 2023; 238:117239. [PMID: 37778597 DOI: 10.1016/j.envres.2023.117239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/10/2023] [Accepted: 09/18/2023] [Indexed: 10/03/2023]
Abstract
Molecular descriptors reflecting structural information on hydrophobicity, reactivity, polarizability, hydrogen bond and charged groups, were used to predict the toxicity (pLC50) of chemicals towards Daphnia magna with global quantitative structure-activity/toxicity relationship (QSAR/QSTR) models. A sufficiently large dataset including 1517 chemical toxicity to Daphnia magna was divided into a training set (758 pLC50) and a test set (759 pLC50). By applying random forest algorithm, two classification models, Class Model A and Class Model B were developed, having prediction accuracy, sensitivity and specificity above 85% for Class 1 (with pLC50 ≤ 4.48) and Class 2 (with pLC50 > 4.48). The Class Model A was based on nine molecular descriptors and RF parameters of nodesize = 1, ntree = 80 and mtry = 2, and yielded accuracy of 92.3% (training set), 85.6% (test set) and 88.9% (total data set). Class Model B was based on ten descriptors and parameters, nodesize = 1, ntree = 90 and mtry = 2, produced accuracy of 88.3% (training set), 86.8% (test set) and 87.5% (total data set). The two classification models were satisfactory compared with other classification model reported in the literature, although classification models in this work dealt with more samples. Thus, the two classification models with a larger applicability domain provided efficient tools for assessing chemical aquatic toxicity towards Daphnia magna.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China.
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13
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Singh AV, Bansod G, Mahajan M, Dietrich P, Singh SP, Rav K, Thissen A, Bharde AM, Rothenstein D, Kulkarni S, Bill J. Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment. ACS OMEGA 2023; 8:21377-21390. [PMID: 37360489 PMCID: PMC10286258 DOI: 10.1021/acsomega.3c00596] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/09/2023] [Indexed: 06/28/2023]
Abstract
Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.
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Affiliation(s)
- Ajay Vikram Singh
- Department
of Chemical and Product Safety, German Federal
Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Girija Bansod
- Rajiv
Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (deemed to be) University, Pune 411045, India
| | - Mihir Mahajan
- Department
of Informatics, Technical University of
Munich, 85758 Garching, Germany
| | - Paul Dietrich
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Shivam Pratap Singh
- School
of Computer and Mathematical Sciences, University
of Greenwich, London SE10 9LS, U.K.
| | - Kranti Rav
- Delta
Biopharmaceutical, Andhra Pradesh 524126, India
| | - Andreas Thissen
- SPECS
Surface Nano Analysis GmbH, Voltastrasse 5, 13355 Berlin, Germany
| | - Aadya Mandar Bharde
- Guru
Nanak Khalsa College of Arts Science and Commerce, Mumbai 400 037, India
| | - Dirk Rothenstein
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
| | - Shilpa Kulkarni
- Seeta
Nursing Home, Shivaji
Nagar, Nashik, Maharashtra 422002, India
| | - Joachim Bill
- Institute
for Materials Science, Department of Bioinspired Materials, University of Stuttgart, 70569 Stuttgart, Germany
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