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Wang N, Dong G, Qiao R, Yin X, Lin S. Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9487-9499. [PMID: 38691763 DOI: 10.1021/acs.est.4c00480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.
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Affiliation(s)
- Nan Wang
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Gongqing Dong
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Ruxia Qiao
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Xiang Yin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Sijie Lin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
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Fu X, Jiang J, Wu X, Huang L, Han R, Li K, Liu C, Roy K, Chen J, Mahmoud NTA, Wang Z. Deep learning in water protection of resources, environment, and ecology: achievement and challenges. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:14503-14536. [PMID: 38305966 DOI: 10.1007/s11356-024-31963-5] [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: 08/24/2023] [Accepted: 01/06/2024] [Indexed: 02/03/2024]
Abstract
The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.
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Affiliation(s)
- Xiaohua Fu
- Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha, 410004, People's Republic of China
| | - Jie Jiang
- Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha, 410004, People's Republic of China
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, Ministry of Ecology and Environment, South China Institute of Environmental Sciences, Guangzhou, 510655, People's Republic of China
| | - Xie Wu
- China Railway Water Information Technology Co, LTD, Nanchang, 330000, People's Republic of China
| | - Lei Huang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, People's Republic of China
| | - Rui Han
- China Environment Publishing Group, Beijing, 100062, People's Republic of China
| | - Kun Li
- Freeman Business School, Tulane University, New Orleans, LA, 70118, USA
- Guangzhou Huacai Environmental Protection Technology Co., Ltd, Guangzhou, 511480, People's Republic of China
| | - Chang Liu
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, Ministry of Ecology and Environment, South China Institute of Environmental Sciences, Guangzhou, 510655, People's Republic of China
| | - Kallol Roy
- Institute of Computer Science, University of Tartu, 51009, Tartu, Estonia
| | - Jianyu Chen
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, Ministry of Ecology and Environment, South China Institute of Environmental Sciences, Guangzhou, 510655, People's Republic of China
| | | | - Zhenxing Wang
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, Ministry of Ecology and Environment, South China Institute of Environmental Sciences, Guangzhou, 510655, People's Republic of China.
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Lu J, Zhang C, Xu W, Chen W, Tao L, Li Z, Cheng J, Zhang Y. Developmental toxicity and estrogenicity of glyphosate in zebrafish in vivo and in silico studies. CHEMOSPHERE 2023; 343:140275. [PMID: 37758082 DOI: 10.1016/j.chemosphere.2023.140275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/17/2023] [Accepted: 09/23/2023] [Indexed: 09/30/2023]
Abstract
As the most heavily used herbicide globally, glyphosate (GLY) has been detected in a variety of environments and has raised concerns about its ecological and health effects. There is debate as to whether GLY may disrupt the endocrine system. Here, we investigated the developmental toxicity of GLY in zebrafish based on deep learning-enabled morphometric analysis (DLMA). In addition, the estrogenic activity of GLY was assessed by endocrine disruption prediction, docking study and in vivo experiments. Results showed that exposure to environmental concentrations of GLY negatively impacted zebrafish development, causing yolk edema and pericardial edema. Endocrine disruption prediction suggested that GLY may target estrogen receptors (ER). Molecular docking analysis revealed binding of GLY to three zebrafish ER. In vivo zebrafish experiment, GLY enhanced the protein levels of ERα and the mRNA levels of cyp19a, HSD17b1, vtg1, vtg2, esr1, esr2a and esr2b. These results suggest that GLY may act as an endocrine disruptor by targeting ER, which warrants further attention for its potential toxicity to aquatic animals.
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Affiliation(s)
- Jian Lu
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Cheng Zhang
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, United States
| | - Wenping Xu
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Weidong Chen
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Liming Tao
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhong Li
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Jiagao Cheng
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Yang Zhang
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
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