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López-Cortés XA, Manríquez-Troncoso JM, Kandalaft-Letelier J, Cuadros-Orellana S. Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development. J Chromatogr A 2024; 1734:465262. [PMID: 39197363 DOI: 10.1016/j.chroma.2024.465262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/06/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024]
Abstract
BACKGROUND The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current state of the art in using machine learning for the detection and classification of antimicrobial resistance from MALDI-TOF mass spectrometry data. METHODS A comprehensive review of the literature on machine learning applications for antimicrobial resistance detection was performed using databases such as Web Of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed. Only original articles in English were included. Studies applying machine learning without using MALDI-TOF mass spectra were excluded. RESULTS Forty studies met the inclusion criteria. Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli were the most frequently cited bacteria. The antibiotics resistance most studied corresponds to methicillin for S. aureus, cephalosporins for K. pneumoniae, and aminoglycosides for E. coli. Random forest, support vector machine and logistic regression were the most employed algorithms to predict antimicrobial resistance. Additionally, seven studies reported using artificial neural networks. Most studies reported metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) above 0.80. CONCLUSIONS Our study indicates that random forest, support vector machine, and logistic regression are effective for predicting antimicrobial resistance using MALDI-TOF MS data. Recent studies also highlight the potential of deep learning techniques in this area. We recommend further exploration of deep learning and multi-label supervised learning for comprehensive antibiotic resistance prediction in clinical practice.
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Affiliation(s)
- Xaviera A López-Cortés
- Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, 3480112, Chile; Centro de Innovación en Ingeniería Aplicada (CIIA), Universidad Católica del Maule, Talca, 3480112, Chile.
| | - José M Manríquez-Troncoso
- Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, 3480112, Chile
| | - John Kandalaft-Letelier
- Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, 3480112, Chile
| | - Sara Cuadros-Orellana
- Centro de Biotecnología de los Recursos Naturales, Universidad Católica del Maule, Talca, 3480112, Chile
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Zhu Z, Zhang Y, Li J, Han Y, Wang L, Zhang Y, Geng H, Zheng Y, Wang X, Sun C, Li B, Chen P. Mass spectrometry imaging-based metabolomics highlights spatial metabolic alterations in three types of liver injuries. J Pharm Biomed Anal 2024; 242:116030. [PMID: 38382318 DOI: 10.1016/j.jpba.2024.116030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
Liver's distinctive function renders it highly susceptible to diverse damage sources. Characterizing the metabolic profiles and spatial signatures in different liver injuries is imperative for early diagnosis and etiology-oriented treatment. In this comparative study, we conducted whole-body spatial metabolomics on zebrafish with liver injury induced by ethanol (EtOH), acetaminophen (APAP), and thioacetamide (TAA). The two specific levels, the whole-body and liver-specific metabolic profiles, as well as their regional distributions, were systematically mapped in situ by mass spectrometry imaging, which is distinct from conventional LC-MS and GC-MS methods. We found that liver injury regions exhibited more pronounced metabolic reprogramming than the entire organism, leading to significant alterations in eight fatty acids, three phospholipids, and four low-molecular-weight metabolites. More importantly, fatty acids as well as small molecule metabolites including glutamine, glutamate, taurine and malic acid displayed contrasting changes between alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD). In addition, phospholipids, including Lyso PC (16:0) and Lyso PE (18:0), demonstrated notable down-regulation in all damaged liver, whereas PC (34:1) underwent upregulation. This study not only deepens insights into distinct potential biomarkers for liver injuries, but also underscores spatial metabolomics as a powerful tool to elucidate possible pathogenic mechanisms in other metabolic diseases.
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Affiliation(s)
- Zihan Zhu
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Yun Zhang
- Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250103, China
| | - Jun Li
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Yuhao Han
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Lei Wang
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Yaqi Zhang
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Haoyuan Geng
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Yurong Zheng
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Xiao Wang
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Chenglong Sun
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Baoguo Li
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
| | - Panpan Chen
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China; Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
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Santos AA, Delgado TC, Marques V, Ramirez-Moncayo C, Alonso C, Vidal-Puig A, Hall Z, Martínez-Chantar ML, Rodrigues CM. Spatial metabolomics and its application in the liver. Hepatology 2024; 79:1158-1179. [PMID: 36811413 PMCID: PMC11020039 DOI: 10.1097/hep.0000000000000341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023]
Abstract
Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.
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Affiliation(s)
- André A. Santos
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - Teresa C. Delgado
- Liver Disease Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance, Derio, Bizkaia, Spain
- Congenital Metabolic Disorders, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Vanda Marques
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - Carmen Ramirez-Moncayo
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | | | - Antonio Vidal-Puig
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Centro Investigation Principe Felipe, Valencia, Spain
| | - Zoe Hall
- Division of Systems Medicine, Imperial College London, London, UK
| | - María Luz Martínez-Chantar
- Liver Disease Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance, Derio, Bizkaia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Carlos III National Health Institute, Madrid, Spain
| | - Cecilia M.P. Rodrigues
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
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Bi S, Wang M, Pu Q, Yang J, Jiang N, Zhao X, Qiu S, Liu R, Xu R, Li X, Hu C, Yang L, Gu J, Du D. Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research. Anal Chem 2024; 96:339-346. [PMID: 38102989 DOI: 10.1021/acs.analchem.3c04192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.
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Affiliation(s)
- Siwei Bi
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Manjiangcuo Wang
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Qianlun Pu
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Jinxi Yang
- West China Centre of Excellence for Pancreatitis, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital/West China Medical School, Sichuan University, Chengdu 610041, China
| | - Na Jiang
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Xueshan Zhao
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Siyuan Qiu
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu,Sichuan 610041, China
| | - Ruiqi Liu
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Renjie Xu
- Department of Respiratory Health West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xia Li
- West China School of Nursing, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chenggong Hu
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lie Yang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu,Sichuan 610041, China
| | - Jun Gu
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Dan Du
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
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Wang Y, Li S, Qian K. Nanoparticle-based applications by atmospheric pressure matrix assisted desorption/ionization mass spectrometry. NANOSCALE ADVANCES 2023; 5:6804-6818. [PMID: 38059044 PMCID: PMC10697002 DOI: 10.1039/d3na00734k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/20/2023] [Indexed: 12/08/2023]
Abstract
Recently, the development of atmospheric pressure matrix assisted desorption/ionization mass spectrometry (AP MALDI MS) has made contributions not only to biomolecule analysis but also to spatial distribution. This has positioned AP MALDI as a powerful tool in multiple domains, thanks to its comprehensive advantages compared to conventional MALDI MS. These developments have addressed challenges associated with previous AP MALDI analysis systems, such as optimization of apparatus settings, synthesis of novel matrices, preconcentration and isolation strategies before analysis. Herein, applications in different fields using AP MALDI MS were described, including peptide and protein analysis, metabolite analysis, pharmaceutical analysis, and mass spectrometry imaging.
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Affiliation(s)
- Yihan Wang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University Shanghai 200030 China
| | - Shunxiang Li
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University Shanghai 200030 China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University Shanghai 200030 China
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Guo X, Wang X, Tian C, Dai J, Zhao Z, Duan Y. Development of mass spectrometry imaging techniques and its latest applications. Talanta 2023; 264:124721. [PMID: 37271004 DOI: 10.1016/j.talanta.2023.124721] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 05/03/2023] [Accepted: 05/22/2023] [Indexed: 06/06/2023]
Abstract
Mass spectrometry imaging (MSI) is a novel molecular imaging technology that collects molecular information from the surface of samples in situ. The spatial distribution and relative content of various compounds can be visualized simultaneously with high spatial resolution. The prominent advantages of MSI promote the active development of ionization technology and its broader applications in diverse fields. This article first gives a brief introduction to the vital parts of the processes during MSI. On this basis, provides a comprehensive overview of the most relevant MS-based imaging techniques from their mechanisms, pros and cons, and applications. In addition, a critical issue in MSI, matrix effects is also discussed. Then, the representative applications of MSI in biological, forensic, and environmental fields in the past 5 years have been summarized, with a focus on various types of analytes (e.g., proteins, lipids, polymers, etc.) Finally, the challenges and further perspectives of MSI are proposed and concluded.
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Affiliation(s)
- Xing Guo
- College of Chemistry and Material Science, Northwest University, Xi'an, 710069, PR China
| | - Xin Wang
- College of Chemistry and Material Science, Northwest University, Xi'an, 710069, PR China
| | - Caiyan Tian
- College of Life Science, Sichuan University, Chengdu, 610064, PR China
| | - Jianxiong Dai
- Aliben Science and Technology Company Limited, Chengdu, 610064, PR China
| | | | - Yixiang Duan
- College of Chemistry and Material Science, Northwest University, Xi'an, 710069, PR China; Research Center of Analytical Instrumentation, Sichuan University, Chengdu, 610064, PR China.
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Zeng T, Zhang R, Chen Y, Guo W, Wang J, Cai Z. In situ localization of lipids on mouse kidney tissues with acute cadmium toxicity using atmospheric pressure-MALDI mass spectrometry imaging. Talanta 2022; 245:123466. [PMID: 35460980 DOI: 10.1016/j.talanta.2022.123466] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/01/2022] [Accepted: 04/05/2022] [Indexed: 10/18/2022]
Abstract
Cadmium-induced nephrotoxicity has been one of the major concerns for public health over the past century. Lipid peroxidation is a principal mechanism in its pathological process. Atmospheric pressure-MALDI mass spectrometry imaging (AP-MALDI MSI) enables direct mapping of lipids in the biological tissue sections. Considering the spatial visualization of lipids on mouse kidney tissues with acute cadmium toxicity is lacking, this study dedicates to filling the gap by using AP-MALDI MSI. Of the tested matrices, the optimized matrix for labeling lipids was 2,5-dihydroxyacetophenone (DHAP). A set of lipids including phosphatidylcholines (PC), phosphatidylglycerol (PG), lysophosphatidylcholine (LPC), sphingomyelin (SM), phosphatidic acid (PA), triglyceride (TG), phosphatidylethanolamine (PE) and phosphatidylinositol (PI), etc. were identified and visualized. Accordingly, PC, PG, LPC, SM, PA and TG were down-regulated while PE and PI were up-regulated in the renal cortex or medulla regions in kidney tissues of the mouse with acute cadmium toxicity. Such in situ locations of lipids on mouse kidney tissues with acute cadmium toxicity could help discover tissue-specific nephrotoxic biomarkers and provide new insights into its renal toxicological mechanism.
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Affiliation(s)
- Ting Zeng
- Food Science and Technology Program, Beijing Normal University-Hong Kong Baptist University United International College, Guangdong, Zhuhai, 519087, China; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Rong Zhang
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Yanyan Chen
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Wenjing Guo
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Jianing Wang
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China; Institute for Research and Continuing Education, Hong Kong Baptist University, Hong Kong, China.
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China.
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