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Gao P, Wu F, Liu J, Li R, Jiang X, Pan W, Zhao F, Niu X, Xu W. Development of a dual-component biosensor for rapid and sensitive detection of influenza H7 and H5 subtypes. Talanta 2024; 280:126704. [PMID: 39151319 DOI: 10.1016/j.talanta.2024.126704] [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: 04/16/2024] [Revised: 07/17/2024] [Accepted: 08/12/2024] [Indexed: 08/19/2024]
Abstract
The outbreak of highly pathogenic influenza virus subtypes, such as H7 and H5, presents a significant global health challenge, necessitating the development of rapid and sensitive diagnostic methods. In this study, we have developed a novel dual-component biosensor assembly, each component of which incorporates an antibody fused with a nano-luciferase subunit. Our results demonstrate the effectiveness of this biosensor in enabling the rapid and sensitive detection of influenza H7 and other subtypes. Additionally, we successfully applied the biosensor in paper-based assay and lateral flow assay formats, expanding its versatility and potential for field-deployable applications. Notably, we achieved effective detection of the H7N9 virus using this biosensor. Furthermore, we designed and optimized a dedicated biosensor to the sensitive detection of the influenza H5 subtype. Collectively, our findings underscore the significant potential of this dual-component biosensor assembly as a valuable and versatile tool for accurate and timely diagnosis of influenza virus infections, promising to advance the field of influenza diagnostics and contribute to outbreak management and surveillance efforts.
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Affiliation(s)
- Peixuan Gao
- Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Fang Wu
- Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China; Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, 528000, China
| | - Jun Liu
- Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Rui Li
- Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Xiwen Jiang
- School of Life Sciences and Biopharmaceuticals, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Weiqi Pan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510120, China
| | - Fang Zhao
- National Clinical Research Centre for Infectious Diseases, the Third People's Hospital of Shenzhen and the Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518112, China
| | - Xuefeng Niu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510120, China
| | - Wei Xu
- Guangdong Provincial Key Laboratory of New Drug Screening, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism and Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, China; Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Southern Medical University, Guangzhou, 510515, China.
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2
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Elste J, Saini A, Mejia-Alvarez R, Mejía A, Millán-Pacheco C, Swanson-Mungerson M, Tiwari V. Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions. Biomolecules 2024; 14:911. [PMID: 39199298 PMCID: PMC11352483 DOI: 10.3390/biom14080911] [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: 06/13/2024] [Revised: 07/11/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
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Affiliation(s)
- James Elste
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Akash Saini
- Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA;
| | - Rafael Mejia-Alvarez
- Department of Physiology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA;
| | - Armando Mejía
- Departamento de Biotechnology, Universidad Autónoma Metropolitana-Iztapalapa, Ciudad de Mexico 09340, Mexico;
| | - Cesar Millán-Pacheco
- Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Av. Universidad No. 1001, Col Chamilpa, Cuernavaca 62209, Mexico;
| | - Michelle Swanson-Mungerson
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
| | - Vaibhav Tiwari
- Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; (J.E.); (M.S.-M.)
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3
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Nguyen KT, Rima XY, Nguyen LTH, Wang X, Kwak KJ, Yoon MJ, Li H, Chiang CL, Doon-Ralls J, Scherler K, Fallen S, Godfrey SL, Wallick JA, Magaña SM, Palmer AF, Lee I, Nunn CC, Reeves KM, Kaplan HG, Goldman JD, Heath JR, Wang K, Pancholi P, Lee LJ, Reátegui E. Integrated Antigenic and Nucleic Acid Detection in Single Virions and Extracellular Vesicles with Viral Content. Adv Healthc Mater 2024:e2400622. [PMID: 38820600 DOI: 10.1002/adhm.202400622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 05/06/2024] [Indexed: 06/02/2024]
Abstract
Virion-mediated outbreaks are imminent and despite rapid responses, continue to cause adverse symptoms and death. Therefore, tunable, sensitive, high-throughput assays are needed to help diagnose future virion-mediated outbreaks. Herein, it is developed a tunable in situ assay to selectively enrich virions and extracellular vesicles (EVs) and simultaneously detect antigens and nucleic acids at a single-particle resolution. The Biochip Antigen and RNA Assay (BARA) enhanced sensitivities compared to quantitative reverse-transcription polymerase chain reaction (qRT-PCR), enabling the detection of virions in asymptomatic patients, genetic mutations in single virions, and enabling the continued long-term expression of viral RNA in the EV-enriched subpopulation in the plasma of patients with post-acute sequelae of the coronavirus disease of 2019 (COVID-19). BARA revealed highly accurate diagnoses of COVID-19 by simultaneously detecting the spike glycoprotein and nucleocapsid-encoding RNA in saliva and nasopharyngeal swab samples. Altogether, the single-particle detection of antigens and viral RNA provides a tunable framework for the diagnosis, monitoring, and mutation screening of current and future outbreaks.
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Affiliation(s)
- Kim Truc Nguyen
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Xilal Y Rima
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
- Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Luong T H Nguyen
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Xinyu Wang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | | | - Min Jin Yoon
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Hong Li
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Chi-Ling Chiang
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Jacob Doon-Ralls
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | | | | | | | | | - Setty M Magaña
- Translational Neuroimmunology, Center for Clinical and Translational Research, Nationwide Children's Hospital, Columbus, OH, 43205, USA
| | - Andre F Palmer
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Inyoul Lee
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | | | - Henry G Kaplan
- Providence Swedish Cancer Institute, Seattle, WA, 98104, USA
| | - Jason D Goldman
- Providence Swedish Medical Center, Seattle, WA, 98104, USA
- Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Kai Wang
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Preeti Pancholi
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, 43203, USA
| | - L James Lee
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Eduardo Reátegui
- William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH, 43210, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
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4
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [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: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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5
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Thiermann R, Sandler M, Ahir G, Sauls JT, Schroeder J, Brown S, Le Treut G, Si F, Li D, Wang JD, Jun S. Tools and methods for high-throughput single-cell imaging with the mother machine. eLife 2024; 12:RP88463. [PMID: 38634855 PMCID: PMC11026091 DOI: 10.7554/elife.88463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.
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Affiliation(s)
- Ryan Thiermann
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Michael Sandler
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Gursharan Ahir
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - John T Sauls
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Jeremy Schroeder
- Department of Biological Chemistry, University of Michigan Medical SchoolAnn ArborUnited States
| | - Steven Brown
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | | | - Fangwei Si
- Department of Physics, Carnegie Mellon UniversityPittsburghUnited States
| | - Dongyang Li
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Jue D Wang
- Department of Bacteriology, University of Wisconsin–MadisonMadisonUnited States
| | - Suckjoon Jun
- Department of Physics, University of California, San DiegoLa JollaUnited States
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6
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Drori P, Mouhadeb O, Moya Muñoz GG, Razvag Y, Alcalay R, Klocke P, Cordes T, Zahavy E, Lerner E. Rapid and specific detection of single nanoparticles and viruses in microfluidic laminar flow via confocal fluorescence microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.31.573251. [PMID: 38260394 PMCID: PMC10802330 DOI: 10.1101/2023.12.31.573251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Mainstream virus detection relies on the specific amplification of nucleic acids via polymerase chain reaction, a process that is slow and requires extensive laboratory expertise and equipment. Other modalities, such as antigen-based tests, allow much faster virus detection but have reduced sensitivity. In this study, we report the development of a flow virometer for the specific and rapid detection of single nanoparticles based on confocal microscopy. The combination of laminar flow and multiple dyes enable the detection of correlated fluorescence signals, providing information on nanoparticle volumes and specific chemical composition properties, such as viral envelope proteins. We evaluated and validated the assay using fluorescent beads and viruses, including SARS-CoV-2. Additionally, we demonstrate how hydrodynamic focusing enhances the assay sensitivity for detecting clinically-relevant virus loads. Based on our results, we envision the use of this technology for clinically relevant bio-nanoparticles, supported by the implementation of the assay in a portable and user-friendly setup.
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Affiliation(s)
- Paz Drori
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Odelia Mouhadeb
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Gabriel G Moya Muñoz
- Physical and Synthetic Biology. Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | - Yair Razvag
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Ron Alcalay
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Philipp Klocke
- Physical and Synthetic Biology. Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | - Thorben Cordes
- Physical and Synthetic Biology. Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | - Eran Zahavy
- Department of Biochemistry and Molecular Genetics, Israel Institute for Biological Research, Ness Ziona, Israel
| | - Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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7
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Yang RS, Traver M, Barefoot N, Stephens T, Alabanza C, Manzella-Lapeira J, Zou G, Wolff J, Li Y, Resto M, Shadrick W, Yang Y, Ivleva VB, Tsybovsky Y, Carlton K, Brzostowski J, Gall JG, Lei QP. Mosaic quadrivalent influenza vaccine single nanoparticle characterization. Sci Rep 2024; 14:4534. [PMID: 38402303 PMCID: PMC10894272 DOI: 10.1038/s41598-024-54876-2] [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: 12/29/2023] [Accepted: 02/17/2024] [Indexed: 02/26/2024] Open
Abstract
Recent work by our laboratory and others indicates that co-display of multiple antigens on protein-based nanoparticles may be key to induce cross-reactive antibodies that provide broad protection against disease. To reach the ultimate goal of a universal vaccine for seasonal influenza, a mosaic influenza nanoparticle vaccine (FluMos-v1) was developed for clinical trial (NCT04896086). FluMos-v1 is unique in that it is designed to co-display four recently circulating haemagglutinin (HA) strains; however, current vaccine analysis techniques are limited to nanoparticle population analysis, thus, are unable to determine the valency of an individual nanoparticle. For the first time, we demonstrate by total internal reflection fluorescence microscopy and supportive physical-chemical methods that the co-display of four antigens is indeed achieved in single nanoparticles. Additionally, we have determined percentages of multivalent (mosaic) nanoparticles with four, three, or two HA proteins. The integrated imaging and physicochemical methods we have developed for single nanoparticle multivalency will serve to further understand immunogenicity data from our current FluMos-v1 clinical trial.
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Affiliation(s)
- Rong Sylvie Yang
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Maria Traver
- Twinbrook Imaging Facility, LIG, NIAID, NIH, Gaithersburg, MD, USA
| | - Nathan Barefoot
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Tyler Stephens
- Vaccine Research Center Electron Microscopy Unit, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Casper Alabanza
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | | | - Guozhang Zou
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Jeremy Wolff
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Yile Li
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Melissa Resto
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - William Shadrick
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Yanhong Yang
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Vera B Ivleva
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Yaroslav Tsybovsky
- Vaccine Research Center Electron Microscopy Unit, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Kevin Carlton
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | | | - Jason G Gall
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA
| | - Q Paula Lei
- Vaccine Production Program, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9 West Watkins Mill Rd., Gaithersburg, MD, 20878, USA.
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8
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Thiermann R, Sandler M, Ahir G, Sauls JT, Schroeder JW, Brown SD, Le Treut G, Si F, Li D, Wang JD, Jun S. Tools and methods for high-throughput single-cell imaging with the mother machine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.27.534286. [PMID: 37066401 PMCID: PMC10103947 DOI: 10.1101/2023.03.27.534286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.
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Affiliation(s)
- Ryan Thiermann
- Department of Physics, University of California San Diego, La Jolla CA
| | - Michael Sandler
- Department of Physics, University of California San Diego, La Jolla CA
| | - Gursharan Ahir
- Department of Physics, University of California San Diego, La Jolla CA
| | - John T. Sauls
- Department of Physics, University of California San Diego, La Jolla CA
| | - Jeremy W. Schroeder
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI
| | - Steven D. Brown
- Department of Physics, University of California San Diego, La Jolla CA
| | | | - Fangwei Si
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA
| | - Dongyang Li
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Jue D. Wang
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI
| | - Suckjoon Jun
- Department of Physics, University of California San Diego, La Jolla CA
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9
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Pannipulath Venugopal V, Babu Saheer L, Maktabdar Oghaz M. COVID-19 lateral flow test image classification using deep CNN and StyleGAN2. Front Artif Intell 2024; 6:1235204. [PMID: 38348096 PMCID: PMC10860423 DOI: 10.3389/frai.2023.1235204] [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: 06/05/2023] [Accepted: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Introduction Artificial intelligence (AI) in healthcare can enhance clinical workflows and diagnoses, particularly in large-scale operations like COVID-19 mass testing. This study presents a deep Convolutional Neural Network (CNN) model for automated COVID-19 RATD image classification. Methods To address the absence of a RATD image dataset, we crowdsourced 900 real-world images focusing on positive and negative cases. Rigorous data augmentation and StyleGAN2-ADA generated simulated images to overcome dataset limitations and class imbalances. Results The best CNN model achieved a 93% validation accuracy. Test accuracies were 88% for simulated datasets and 82% for real datasets. Augmenting simulated images during training did not significantly improve real-world test image performance but enhanced simulated test image performance. Discussion The findings of this study highlight the potential of the developed model in expediting COVID-19 testing processes and facilitating large-scale testing and tracking systems. The study also underscores the challenges in designing and developing such models, emphasizing the importance of addressing dataset limitations and class imbalances. Conclusion This research contributes to the deployment of large-scale testing and tracking systems, offering insights into the potential applications of AI in mitigating outbreaks similar to COVID-19. Future work could focus on refining the model and exploring its adaptability to other healthcare scenarios.
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Affiliation(s)
| | - Lakshmi Babu Saheer
- School of Computing and Information Science, Anglia Ruskin University, Cambridge, United Kingdom
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10
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. CLINICAL PATHOLOGY (THOUSAND OAKS, VENTURA COUNTY, CALIF.) 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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11
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Wu ZQ, Ma YP, Liu H, Huang CZ, Zhou J. High Confidence Single Particle Analysis with Machine Learning. Anal Chem 2023; 95:15375-15383. [PMID: 37796610 DOI: 10.1021/acs.analchem.3c03297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.
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Affiliation(s)
- Zhang Quan Wu
- College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Yun Peng Ma
- Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Hui Liu
- Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Jun Zhou
- College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
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12
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Xie E, Ahmad S, Smyth RP, Sieben C. Advanced fluorescence microscopy in respiratory virus cell biology. Adv Virus Res 2023; 116:123-172. [PMID: 37524480 DOI: 10.1016/bs.aivir.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Respiratory viruses are a major public health burden across all age groups around the globe, and are associated with high morbidity and mortality rates. They can be transmitted by multiple routes, including physical contact or droplets and aerosols, resulting in efficient spreading within the human population. Investigations of the cell biology of virus replication are thus of utmost importance to gain a better understanding of virus-induced pathogenicity and the development of antiviral countermeasures. Light and fluorescence microscopy techniques have revolutionized investigations of the cell biology of virus infection by allowing the study of the localization and dynamics of viral or cellular components directly in infected cells. Advanced microscopy including high- and super-resolution microscopy techniques available today can visualize biological processes at the single-virus and even single-molecule level, thus opening a unique view on virus infection. We will highlight how fluorescence microscopy has supported investigations on virus cell biology by focusing on three major respiratory viruses: respiratory syncytial virus (RSV), Influenza A virus (IAV) and SARS-CoV-2. We will review our current knowledge of virus replication and highlight how fluorescence microscopy has helped to improve our state of understanding. We will start by introducing major imaging and labeling modalities and conclude the chapter with a perspective discussion on remaining challenges and potential opportunities.
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Affiliation(s)
- Enyu Xie
- Nanoscale Infection Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Shazeb Ahmad
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research, Würzburg, Germany
| | - Redmond P Smyth
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research, Würzburg, Germany; Faculty of Medicine, University of Würzburg, Würzburg, Germany
| | - Christian Sieben
- Nanoscale Infection Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Institute of Genetics, Technische Universität Braunschweig, Braunschweig, Germany.
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13
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Seymour E, Ekiz Kanik F, Diken Gür S, Bakhshpour-Yucel M, Araz A, Lortlar Ünlü N, Ünlü MS. Solid-Phase Optical Sensing Techniques for Sensitive Virus Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:5018. [PMID: 37299745 PMCID: PMC10255700 DOI: 10.3390/s23115018] [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: 04/18/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
Viral infections can pose a major threat to public health by causing serious illness, leading to pandemics, and burdening healthcare systems. The global spread of such infections causes disruptions to every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has significant implications for saving lives, preventing the spread of the diseases, and minimizing social and economic damages. Polymerase chain reaction (PCR)-based techniques are commonly used to detect viruses in the clinic. However, PCR has several drawbacks, as highlighted during the recent COVID-19 pandemic, such as long processing times and the requirement for sophisticated laboratory instruments. Therefore, there is an urgent need for fast and accurate techniques for virus detection. For this purpose, a variety of biosensor systems are being developed to provide rapid, sensitive, and high-throughput viral diagnostic platforms, enabling quick diagnosis and efficient control of the virus's spread. Optical devices, in particular, are of great interest due to their advantages such as high sensitivity and direct readout. The current review discusses solid-phase optical sensing techniques for virus detection, including fluorescence-based sensors, surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS), optical resonators, and interferometry-based platforms. Then, we focus on an interferometric biosensor developed by our group, the single-particle interferometric reflectance imaging sensor (SP-IRIS), which has the capability to visualize single nanoparticles, to demonstrate its application for digital virus detection.
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Affiliation(s)
- Elif Seymour
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M4P 1R2, Canada;
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA;
| | - Fulya Ekiz Kanik
- Department of Electrical Engineering, Boston University, Boston, MA 02215, USA; (F.E.K.); (M.B.-Y.)
| | - Sinem Diken Gür
- Department of Biology, Hacettepe University, Ankara 06800, Türkiye;
| | - Monireh Bakhshpour-Yucel
- Department of Electrical Engineering, Boston University, Boston, MA 02215, USA; (F.E.K.); (M.B.-Y.)
- Department of Chemistry, Bursa Uludag University, Bursa 16059, Türkiye
| | - Ali Araz
- Department of Chemistry, Dokuz Eylül University, Izmir 35390, Türkiye;
| | - Nese Lortlar Ünlü
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA;
| | - M. Selim Ünlü
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA;
- Department of Electrical Engineering, Boston University, Boston, MA 02215, USA; (F.E.K.); (M.B.-Y.)
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14
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Promja S, Puenpa J, Achakulvisut T, Poovorawan Y, Lee SY, Athamanolap P, Lertanantawong B. Machine Learning-Assisted Real-Time Polymerase Chain Reaction and High-Resolution Melt Analysis for SARS-CoV-2 Variant Identification. Anal Chem 2023; 95:2102-2109. [PMID: 36633573 PMCID: PMC9843624 DOI: 10.1021/acs.analchem.2c05112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.
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Affiliation(s)
- Sutossarat Promja
- Department
of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand
| | - Jiratchaya Puenpa
- Center
of Excellence in Clinical Virology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Titipat Achakulvisut
- Department
of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand
| | - Yong Poovorawan
- Center
of Excellence in Clinical Virology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Su Yin Lee
- Faculty
of Applied Sciences, AIMST University, Bedong, Kedah 08100, Malaysia
| | - Pornpat Athamanolap
- Department
of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand
- Integrative
Computational BioScience (ICBS) Center, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand
| | - Benchaporn Lertanantawong
- Department
of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya 73170, Nakhon Pathom, Thailand
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