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Zhao Y, Zhang X, Zhang Z, Huang W, Tang M, Du G, Qin Y. Hepatic toxicity prediction of bisphenol analogs by machine learning strategy. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173420. [PMID: 38777049 DOI: 10.1016/j.scitotenv.2024.173420] [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: 01/31/2024] [Revised: 04/14/2024] [Accepted: 05/19/2024] [Indexed: 05/25/2024]
Abstract
Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.
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Affiliation(s)
- Ying Zhao
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xueer Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhendong Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wenbo Huang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Min Tang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Guizhen Du
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Yufeng Qin
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Microbiology and Infection, School of Public Health, Nanjing Medical University, Nanjing, China.
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Chen S, Jiao S, Liang Q, Li P, Yin J, Li Q, Yu X, Li Q. Gaining More Insights from Synchrotron-Based X-ray Spectroscopy for Alkali Ion Rechargeable Batteries. Anal Chem 2024; 96:8021-8035. [PMID: 38659100 DOI: 10.1021/acs.analchem.4c01399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Alkali ion rechargeable batteries play a significant part in portable electronic devices and electronic vehicles. The rapid development of renewable energy technology nowadays demands batteries with even higher energy density for grid storage. To fulfill such demand, extensive research efforts have been devoted to optimizing electrochemical properties as well as developing novel energy storage schemes and designing new systems. In the investigation process, synchrotron-based X-ray spectroscopy plays a vital role in investigating the detailed degradation mechanism and developing novel energy storage schemes. Herein, we critically review the applications of synchrotron-based X-ray spectroscopy in battery research in recent years. This review begins with a discussion of the different scientific issues in alkali ion rechargeable batteries within various time and space scales. Subsequently, the principle of synchrotron-based X-ray spectroscopy is introduced, and the characteristics of various characterization techniques are summarized and compared. Typical application cases of synchrotron-based X-ray spectroscopy are then introduced into battery investigations. The final part presents perspectives in the development direction of both alkali ion rechargeable battery systems and synchrotron-based X-ray spectroscopy in the future.
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Affiliation(s)
- Supeng Chen
- College of Physics, Qingdao University, Qingdao 266071, China
| | - Sichen Jiao
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Liang
- College of Physics, Qingdao University, Qingdao 266071, China
| | - Peirong Li
- College of Physics, Qingdao University, Qingdao 266071, China
| | - Jixiang Yin
- College of Physics, Qingdao University, Qingdao 266071, China
| | - Qinghao Li
- College of Physics, Qingdao University, Qingdao 266071, China
| | - Xiqian Yu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiang Li
- College of Physics, Qingdao University, Qingdao 266071, China
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3
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Akepati SV, Gupta N, Jayaraman A. Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D). JACS AU 2024; 4:1570-1582. [PMID: 38665659 PMCID: PMC11040659 DOI: 10.1021/jacsau.4c00068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 04/28/2024]
Abstract
Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nanometers to microns). The output of an SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave; the latter is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While isotropic structural arrangement can be interpreted by analysis of the azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the "computational reverse engineering analysis for scattering experiments" (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. The CREASE-2D workflow identifies the structural features whose computed I(q, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental I(q, θ). We expect that this CREASE-2D method will be a valuable tool for materials' researchers who need direct interpretation of the 2D scattering profiles in contrast to analyzing azimuthally averaged 1D I(q) vs q profiles that can lose important information related to structural anisotropy.
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Affiliation(s)
| | - Nitant Gupta
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University
of Delaware, Newark, Delaware 19716, United States
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Ceballos-Sanchez O, Navarro-López DE, Mejía-Méndez JL, Sanchez-Ante G, Rodríguez-González V, Sánchez-López AL, Sanchez-Martinez A, Duron-Torres SM, Juarez-Moreno K, Tiwari N, López-Mena ER. Enhancing antioxidant properties of CeO 2 nanoparticles with Nd 3+ doping: structural, biological, and machine learning insights. Biomater Sci 2024; 12:2108-2120. [PMID: 38450552 DOI: 10.1039/d3bm02107f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO2 nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO2 nanoparticles. Incorporating Nd3+ induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO2 was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce3+/Ce4+ ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO2. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO2 nanoparticles. This study presents the first exploration of the influence of Nd3+ on the structural, optical, and biological attributes of CeO2, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials.
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Affiliation(s)
- Oscar Ceballos-Sanchez
- Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierias (CUCEI), Departamento de Ingenieria de Proyectos, Av. José Guadalupe Zuno # 48, Industrial Los Belenes, Zapopan, Jalisco, 45157, Mexico.
| | - Diego E Navarro-López
- Tecnologico de Monterrey, Escuela de ingeniería y Ciencias, Av. Gral. Ramón Corona No 2514, Colonia Nuevo México, Zapopan, Jalisco, 45121, Mexico
| | - Jorge L Mejía-Méndez
- Departamento de Ciencias Químico-Biológicas, Universidad de las Américas Puebla, Santa Catarina Mártir s/n, 72810 Cholula, Puebla, Mexico
| | - Gildardo Sanchez-Ante
- Tecnologico de Monterrey, Escuela de ingeniería y Ciencias, Av. Gral. Ramón Corona No 2514, Colonia Nuevo México, Zapopan, Jalisco, 45121, Mexico
| | - Vicente Rodríguez-González
- División de Materiales Avanzados, IPICYT, Instituto Potosino de Investigación Científica y Tecnológica, San Luis Potosí, S.L.P., Mexico
| | - Angélica Lizeth Sánchez-López
- Tecnologico de Monterrey, Escuela de ingeniería y Ciencias, Av. Gral. Ramón Corona No 2514, Colonia Nuevo México, Zapopan, Jalisco, 45121, Mexico
| | - Araceli Sanchez-Martinez
- Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierias (CUCEI), Departamento de Ingenieria de Proyectos, Av. José Guadalupe Zuno # 48, Industrial Los Belenes, Zapopan, Jalisco, 45157, Mexico.
| | - Sergio M Duron-Torres
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Campus Siglo XXI, Carretera Zacatecas, Guadalajara Km 6, Ejido La Escondida, 98160, Zacatecas, Mexico
| | - Karla Juarez-Moreno
- Centro de Física Aplicada y Tecnología Avanzada (CFATA), Universidad Nacional Autónoma de México (UNAM), Querétaro, QRO 76230, Mexico
| | - Naveen Tiwari
- Centro Singular de Investigación en Química Biológica y Materiales Moleculares (CIQUS), C/Jenaro de la Fuente s/n, Campus Vida, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Edgar R López-Mena
- Tecnologico de Monterrey, Escuela de ingeniería y Ciencias, Av. Gral. Ramón Corona No 2514, Colonia Nuevo México, Zapopan, Jalisco, 45121, Mexico
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Anker AS, Kjær ETS, Juelsholt M, Jensen KMØ. POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning. J Appl Crystallogr 2024; 57:34-43. [PMID: 38322723 PMCID: PMC10840315 DOI: 10.1107/s1600576723010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 02/08/2024] Open
Abstract
Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.
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Affiliation(s)
- Andy S. Anker
- Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark
| | - Emil T. S. Kjær
- Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark
| | - Mikkel Juelsholt
- Department of Materials, University of Oxford, Parks Road, Oxford, Oxfordshire OX1 3PH, United Kingdom
| | - Kirsten M. Ø. Jensen
- Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark
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