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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [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: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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2
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Xiao C, Wang J, Yang S, Heng M, Su J, Xiao H, Song J, Li W. VISN: virus instance segmentation network for TEM images using deep attention transformer. Brief Bioinform 2023; 24:bbad373. [PMID: 37903415 DOI: 10.1093/bib/bbad373] [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: 04/24/2023] [Revised: 08/02/2023] [Accepted: 09/29/2023] [Indexed: 11/01/2023] Open
Abstract
The identification of viruses from negative staining transmission electron microscopy (TEM) images has mainly depended on experienced experts. Recent advances in artificial intelligence have enabled virus recognition using deep learning techniques. However, most of the existing methods only perform virus classification or semantic segmentation, and few studies have addressed the challenge of virus instance segmentation in TEM images. In this paper, we focus on the instance segmentation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and other respiratory viruses and provide experts with more effective information about viruses. We propose an effective virus instance segmentation network based on the You Only Look At CoefficienTs backbone, which integrates the Swin Transformer, dense connections and the coordinate-spatial attention mechanism, to identify SARS-CoV-2, H1N1 influenza virus, respiratory syncytial virus, Herpes simplex virus-1, Human adenovirus type 5 and Vaccinia virus. We also provide a public TEM virus dataset and conduct extensive comparative experiments. Our method achieves a mean average precision score of 83.8 and F1 score of 0.920, outperforming other state-of-the-art instance segmentation algorithms. The proposed automated method provides virologists with an effective approach for recognizing and identifying SARS-CoV-2 and assisting in the diagnosis of viruses. Our dataset and code are accessible at https://github.com/xiaochiHNU/Virus-Instance-Segmentation-Transformer-Network.
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Affiliation(s)
- Chi Xiao
- State key laboratory of digital medical engineering, School of Biomedical Engineering, Hainan University, 570228, Haikou, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, 570228, Haikou, China
| | - Jun Wang
- Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
| | - Shenrong Yang
- State key laboratory of digital medical engineering, School of Biomedical Engineering, Hainan University, 570228, Haikou, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, 570228, Haikou, China
| | - Minxin Heng
- State key laboratory of digital medical engineering, School of Biomedical Engineering, Hainan University, 570228, Haikou, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, 570228, Haikou, China
| | - Junyi Su
- State key laboratory of digital medical engineering, School of Biomedical Engineering, Hainan University, 570228, Haikou, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, 570228, Haikou, China
| | - Hao Xiao
- Key Laboratory for Matter Microstructure and Function of Hunan Province, Key Laboratory of Low-dimensional Quantum Structures and Quantum Control, School of Physics and Electronics, Hunan Normal University, 410081, Changsha, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 102206, Beijing, China
| | - Jingdong Song
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 102206, Beijing, China
- NHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, 102206, Beijing, China
| | - Weifu Li
- College of Informatics, Huazhong Agricultural University, 430070, Wuhan, China
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3
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Kaphle A, Jayarathna S, Moktan H, Aliru M, Raghuram S, Krishnan S, Cho SH. Deep Learning-Based TEM Image Analysis for Fully Automated Detection of Gold Nanoparticles Internalized Within Tumor Cell. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1474-1487. [PMID: 37488822 PMCID: PMC10433944 DOI: 10.1093/micmic/ozad066] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/28/2023] [Accepted: 05/22/2023] [Indexed: 07/26/2023]
Abstract
Transmission electron microscopy (TEM) imaging can be used for detection/localization of gold nanoparticles (GNPs) within tumor cells. However, quantitative analysis of GNP-containing cellular TEM images typically relies on conventional/thresholding-based methods, which are manual, time-consuming, and prone to human errors. In this study, therefore, deep learning (DL)-based methods were developed for fully automated detection of GNPs from cellular TEM images. Several models of "you only look once (YOLO)" v5 were implemented, with a few adjustments to enhance the model's performance by applying the transfer learning approach, adjusting the size of the input image, and choosing the best optimization algorithm. Seventy-eight original (12,040 augmented) TEM images of GNP-laden tumor cells were used for model implementation and validation. A maximum F1 score (harmonic mean of the precision and recall) of 0.982 was achieved by the best-trained models, while mean average precision was 0.989 and 0.843 at 0.50 and 0.50-0.95 intersection over union threshold, respectively. These results suggested the developed DL-based approach was capable of precisely estimating the number/position of internalized GNPs from cellular TEM images. A novel DL-based TEM image analysis tool from this study will benefit research/development efforts on GNP-based cancer therapeutics, for example, by enabling the modeling of GNP-laden tumor cells using nanometer-resolution TEM images.
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Affiliation(s)
- Amrit Kaphle
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sandun Jayarathna
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hem Moktan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maureen Aliru
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Subhiksha Raghuram
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sunil Krishnan
- Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Sang Hyun Cho
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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4
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Shiaelis N, Tometzki A, Peto L, McMahon A, Hepp C, Bickerton E, Favard C, Muriaux D, Andersson M, Oakley S, Vaughan A, Matthews PC, Stoesser N, Crook DW, Kapanidis AN, Robb NC. Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning. ACS NANO 2023; 17:697-710. [PMID: 36541630 PMCID: PMC9836350 DOI: 10.1021/acsnano.2c10159] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods and has the potential for significant impact.
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Affiliation(s)
- Nicolas Shiaelis
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
| | - Alexander Tometzki
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
| | - Leon Peto
- Nuffield
Department of Medicine, University of Oxford, OxfordOX3 9DU, United Kingdom
- Department
of Microbiology, Oxford University Hospitals
NHS Foundation Trust, OxfordOX3 9DU, United
Kingdom
| | - Andrew McMahon
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
| | - Christof Hepp
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
| | - Erica Bickerton
- The
Pirbright Institute, Ash Road, Pirbright, Woking, SurreyGU24 0NF, United
Kingdom
| | - Cyril Favard
- Membrane
Domains and Viral Assembly, IRIM, UMR 9004 CNRS and University of Montpellier, 1919, route de Mende, 34293Montpellier, France
| | - Delphine Muriaux
- Membrane
Domains and Viral Assembly, IRIM, UMR 9004 CNRS and University of Montpellier, 1919, route de Mende, 34293Montpellier, France
- CEMIPAI, UMS 3725 CNRS and University of Montpellier, 1919, route de Mende, 34293Montpellier, France
| | - Monique Andersson
- Department
of Microbiology, Oxford University Hospitals
NHS Foundation Trust, OxfordOX3 9DU, United
Kingdom
| | - Sarah Oakley
- Department
of Microbiology, Oxford University Hospitals
NHS Foundation Trust, OxfordOX3 9DU, United
Kingdom
| | - Ali Vaughan
- Nuffield
Department of Medicine, University of Oxford, OxfordOX3 9DU, United Kingdom
- NIHR
Oxford Biomedical Research Centre, University
of Oxford, OxfordOX3 9DU, United
Kingdom
| | - Philippa C. Matthews
- Nuffield
Department of Medicine, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Nicole Stoesser
- Nuffield
Department of Medicine, University of Oxford, OxfordOX3 9DU, United Kingdom
- NIHR
Health Protection Research Unit in Healthcare Associated Infections
and Antimicrobial Resistance, in partnership with Public Health England, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Derrick W. Crook
- Nuffield
Department of Medicine, University of Oxford, OxfordOX3 9DU, United Kingdom
- NIHR
Oxford Biomedical Research Centre, University
of Oxford, OxfordOX3 9DU, United
Kingdom
- NIHR
Health Protection Research Unit in Healthcare Associated Infections
and Antimicrobial Resistance, in partnership with Public Health England, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Achillefs N. Kapanidis
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
- The
Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, OxfordOX1 3QU, United Kingdom
| | - Nicole C. Robb
- Biological
Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, OxfordOX1 3PU, United Kingdom
- Warwick
Medical School, University of Warwick, CoventryCV4 7AL, United Kingdom
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5
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Kulwa F, Li C, Grzegorzek M, Rahaman MM, Shirahama K, Kosov S. Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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6
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Treder KP, Huang C, Kim JS, Kirkland AI. Applications of deep learning in electron microscopy. Microscopy (Oxf) 2022; 71:i100-i115. [DOI: 10.1093/jmicro/dfab043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/30/2021] [Accepted: 11/08/2021] [Indexed: 12/25/2022] Open
Abstract
Abstract
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.
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Affiliation(s)
- Kevin P Treder
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
| | - Chen Huang
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Judy S Kim
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
| | - Angus I Kirkland
- Department of Materials, University of Oxford, Oxford, Oxfordshire OX1 3PH, UK
- Rosalind Franklin Institute, Harwell Research Campus, Didcot, Oxfordshire OX11 0FA, UK
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7
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Zhao H, Ni B, Jin X, Zhang H, Hou JJ, Hou L, Marsh JH, Dong L, Li S, Gao XW, Shi D, Liu X, Xiong J. Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. APPLIED OPTICS 2022; 61:546-553. [PMID: 35200896 DOI: 10.1364/ao.445953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution instruments such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are required. In this paper, we propose a new method for identifying virus particles based on polarization parametric indirect microscopic imaging (PIMI) and deep learning techniques. By introducing an abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), the strength of the photon scattering signal can be magnified. After acquiring the PIMI images, a deep learning method was applied to identify discriminating features and classify the virus particles, using electron microscopy (EM) images as the ground truth. Experimental results confirm that gold-virus particles can be identified in PIMI images with a high level of confidence.
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8
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Rey JS, Li W, Bryer AJ, Beatson H, Lantz C, Engelman AN, Perilla JR. Deep-learning in situ classification of HIV-1 virion morphology. Comput Struct Biotechnol J 2021; 19:5688-5700. [PMID: 34765089 PMCID: PMC8554174 DOI: 10.1016/j.csbj.2021.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 01/04/2023] Open
Abstract
Efficient classifier capable of overcoming inherent problems of small-data training sets. Automated detection and classification of HIV-1 particle morphology from transmission electron micrographs. Three orders of magnitude speed increase in data processing with negligible loss in accuracy.
Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist’s toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.
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Affiliation(s)
- Juan S Rey
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Wen Li
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Alexander J Bryer
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Hagan Beatson
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Christian Lantz
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Alan N Engelman
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Juan R Perilla
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States.,Data Science Institute, University of Delaware, Newark, DE, United States
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9
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Yakimovich A. Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case. Infect Drug Resist 2021; 14:3319-3326. [PMID: 34456575 PMCID: PMC8385421 DOI: 10.2147/idr.s292743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/03/2021] [Indexed: 01/27/2023] Open
Abstract
The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.
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10
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Morris C, Lee YS, Yoon S. Adventitious agent detection methods in bio-pharmaceutical applications with a focus on viruses, bacteria, and mycoplasma. Curr Opin Biotechnol 2021; 71:105-114. [PMID: 34325176 DOI: 10.1016/j.copbio.2021.06.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
Adventitious agents present significant complications to biopharmaceutical manufacturing. Adventitious agents include numerous lifeforms such as bacteria, fungi, viruses, mycoplasma, and others that are inadvertently introduced into biological systems. They present significant problems to the stability of cell cultures and the sterility of manufacturing products. In this review, detection methods for bacteria, viruses, and mycoplasma are comprehensively addressed. Detection methods for viruses include traditional culture-based methods, electron microscopy studies, in vitro molecular and antibody assays, sequencing methods (massive parallel or next generation sequencing), and degenerate PCR (polymerase chain reaction). Bacteria, on the other hand, can be detected with culture-based approaches, PCR, and biosensor-based methods. Mycoplasma can be detected via PCR (including specific kits), microbiological culture methods, and enzyme-linked immunosorbent assays (ELISA). This review highlights the advantages and weaknesses of current detection methods while exploring potential avenues for further development and improvement of novel detection methods. Additionally, a brief evaluation of the transition of these methods into the gene therapy production realm with a focus on viral titer monitoring will be presented.
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Affiliation(s)
- Caitlin Morris
- Pharmaceutical Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Yong Suk Lee
- Pharmaceutical Sciences, University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Seongkyu Yoon
- Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
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11
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Atabakhsh P, Kargar M, Doosti A. Detection and evaluation of rotavirus surveillance methods as viral indicator in the aquatic environments. Braz J Microbiol 2021; 52:811-820. [PMID: 33599964 PMCID: PMC8105488 DOI: 10.1007/s42770-020-00417-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 12/21/2020] [Indexed: 12/27/2022] Open
Abstract
Group A rotaviruses (RVAs) have been introduced as the most important causative agents of acute gastroenteritis in the young children. One of every 260 children born globally will die due to rotavirus (RV) before 5 years old. The RV is widely known as a viral indicator for health (fecal contamination) because this pathogen has a high treatment resistance nature, which has been listed as a relevant waterborne pathogen by the World Health Organization (WHO). Therefore, monitoring of environmental is important, and RV is one of the best-known indicators for monitoring. It has been proved that common standards for microbiological water quality do not guarantee the absence of viruses. On the other hand, in order to recover and determine RV quantity within water, standard methods are scarce. Therefore, dependable prediction of RV quantities in water sample is crucial to be able to improve supervision efficiency of the treatment procedure, precise quantitative evaluation of the microbial risks as well as microbiological water safety. Hence, this study aimed to introduce approaches to detecting and controlling RV in environmental waters, and discussed the challenges faced to enable a clear perception on the ubiquity of the RV within different types of water across the world.
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Affiliation(s)
- Paymaneh Atabakhsh
- Department of Microbiology, Isfahan Water and Wastewater Company, Isfahan, Iran
- Department of Microbiology, Jahrom Branch, Islamic Azad University, Jahrom, Iran
| | - Mohammad Kargar
- Department of Microbiology, Jahrom Branch, Islamic Azad University, Jahrom, Iran
| | - Abbas Doosti
- Biotechnology Research Center, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
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12
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Kalinin SV, Zhang S, Valleti M, Pyles H, Baker D, De Yoreo JJ, Ziatdinov M. Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations. ACS NANO 2021; 15:6471-6480. [PMID: 33861068 DOI: 10.1021/acsnano.0c08914] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.
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Affiliation(s)
- Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Shuai Zhang
- Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mani Valleti
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Harley Pyles
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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13
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Deep Learning for Imaging and Detection of Microorganisms. Trends Microbiol 2021; 29:569-572. [PMID: 33531192 DOI: 10.1016/j.tim.2021.01.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 01/03/2023]
Abstract
Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms.
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Xiao C, Chen X, Xie Q, Li G, Xiao H, Song J, Han H. Virus identification in electron microscopy images by residual mixed attention network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105766. [PMID: 33059061 DOI: 10.1016/j.cmpb.2020.105766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis and re-emerging infectious agents. However, the existing methods either focused on the detection of a single virus or required large amounts of manual labeling work to segment virus. In this work, we focus on the task of virus classification and propose an effective and simple method to identify different viruses. METHODS We put forward a residual mixed attention network (RMAN) for virus classification. The proposed network uses channel attention, bottom-up and top-down attention, and incorporates a residual architecture in an end-to-end training manner, which is suitable for dealing with EM virus images and reducing the burden of manual annotation. RESULTS We validate the proposed network through extensive experiments on a transmission electron microscopy virus image dataset. The top-1 error rate of our RMAN on 12 virus classes is 4.285%, which surpasses that of state-of-the-art networks and even human experts. In addition, the ablation study and the visualization of class activation mapping (CAM) further demonstrate the effectiveness of our method. CONCLUSIONS The proposed automated method contributes to the development of medical virology, which provides virologists with a high-accuracy approach to recognize viruses as well as assist in the diagnosis of viruses.
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Affiliation(s)
- Chi Xiao
- School of Biomedical Engineering, Hainan University, Haikou, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Chen
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qiwei Xie
- Data Mining Lab, Beijing University of Technology, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guoqing Li
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Xiao
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; College of Physics and Information Science, Key Laboratory of Low-dimensional Quantum Structures, And Quantum Control of the Ministry of Education, Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha, China
| | - Jingdong Song
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China.
| | - Hua Han
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
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Matuszewski DJ, Sintorn IM. Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:31-39. [PMID: 31416558 DOI: 10.1016/j.cmpb.2019.05.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/13/2019] [Accepted: 05/28/2019] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their prediction. However, most of the proposed CNNs require an expensive state-of-the-art hardware which substantially limits their use in practical scenarios and commercial systems, especially for clinical, biomedical and other applications that require on-the-fly analysis. In this paper, we investigate the possibility of making CNNs lighter by parametrizing the architecture and decreasing the number of trainable weights of a popular CNN: U-Net. METHODS In order to demonstrate that comparable results can be achieved with substantially less trainable weights than the original U-Net we used a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images with minimal annotations (i.e. consisting only of the virus particle centers or centerlines). We explored 4 U-Net hyper-parameters: the number of base feature maps, the feature maps multiplier, the number of the encoding-decoding levels and the number of feature maps in the last 2 convolutional layers. RESULTS Our experiments lead to two main conclusions: 1) the architecture hyper-parameters are pivotal if less trainable weights are to be used, and 2) if there is no restriction on the trainable weights number using a deeper network generally gives better results. However, training larger networks takes longer, typically requires more data and such networks are also more prone to overfitting. Our best model achieved an accuracy of 82.2% which is similar to the original U-Net while using nearly 4 times less trainable weights (7.8 M in comparison to 31.0 M). We also present a network with < 2 M trainable weights that achieved an accuracy of 76.4%. CONCLUSIONS The proposed U-Net hyper-parameter exploration can be adapted to other CNNs and other applications. It allows a comprehensive CNN architecture designing with the aim of a more efficient trainable weight use. Making the networks faster and lighter is crucial for their implementation in many practical applications. In addition, a lighter network ought to be less prone to over-fitting and hence generalize better.
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Affiliation(s)
| | - Ida-Maria Sintorn
- Department of Information Technology, Uppsala University, Uppsala, Sweden; Vironova AB, Gävlegatan 22, Stockholm, Sweden.
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Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images. Micron 2019; 120:113-119. [PMID: 30844638 DOI: 10.1016/j.micron.2019.02.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 02/19/2019] [Accepted: 02/20/2019] [Indexed: 12/29/2022]
Abstract
With the growing amount of high resolution microscopy images automatic nano-particle detection, shape analysis and size determination have gained importance for providing quantitative support that gives important information for the evaluation of the material. In this paper, we present a new method for detection of nano-particles and determination of their shapes and sizes simultaneously with deep learning. The proposed method employs multiple output convolutional neural networks (MO-CNN) and has two outputs: first is the detection output that gives the locations of the particles and the other one is the segmentation output for providing the boundaries of the nano-particles. The final sizes of particles are determined with the modified Hough algorithm that runs on the segmentation output. The proposed method is tested and evaluated on a dataset containing 17 TEM images of Fe3O4 and silica coated nano-particles. Also, we compared these results with U-net algorithm which is a popular deep learning method. The experiments showed that the proposed method has 98.23% accuracy for detection and 96.59% accuracy for segmentation of nano-particles.
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Devan KS, Walther P, von Einem J, Ropinski T, Kestler HA, Read C. Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning. Histochem Cell Biol 2018; 151:101-114. [PMID: 30488339 DOI: 10.1007/s00418-018-1759-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2018] [Indexed: 10/27/2022]
Abstract
The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infectious virions. As a step towards a software-based quantification of different stages of HCMV virion morphogenesis in TEM, we developed a transfer learning approach based on convolutional neural networks (CNNs) that automatically detects HCMV nucleocapsids in TEM images. In contrast to existing image analysis techniques that require time-consuming manual definition of structural features, our method automatically learns discriminative features from raw images without the need for extensive pre-processing. For this a constantly growing TEM image database of HCMV infected cells was available which is unique regarding image quality and size in the terms of virological EM. From the two investigated types of transfer learning approaches, namely feature extraction and fine-tuning, the latter enabled us to successfully detect HCMV nucleocapsids in TEM images. Our detection method has outperformed some of the existing image analysis methods based on discriminative textural indicators and radial density profiles for virus detection in TEM images. In summary, we could show that the method of transfer learning can be used for an automated detection of viral capsids in TEM images with high specificity using standard computers. This method is highly adaptable and in future could be easily extended to automatically detect and classify virions of other viruses and even distinguish different virion maturation stages.
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Affiliation(s)
- K Shaga Devan
- Central Facility for Electron Microscopy, Ulm University, Ulm, Germany
| | - P Walther
- Central Facility for Electron Microscopy, Ulm University, Ulm, Germany.
| | - J von Einem
- Institute of Virology, Ulm University Medical Center, Ulm, Germany
| | - T Ropinski
- Institute of Media Informatics, Ulm University, Ulm, Germany
| | - H A Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - C Read
- Central Facility for Electron Microscopy, Ulm University, Ulm, Germany.,Institute of Virology, Ulm University Medical Center, Ulm, Germany
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