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Mohapatra RK, Singh PK, Branda F, Mishra S, Kutikuppala LVS, Suvvari TK, Kandi V, Ansari A, Desai DN, Alfaresi M, Kaabi NAA, Fares MAA, Garout M, Halwani MA, Alissa M, Rabaan AA. Transmission dynamics, complications and mitigation strategies of the current mpox outbreak: A comprehensive review with bibliometric study. Rev Med Virol 2024; 34:e2541. [PMID: 38743385 DOI: 10.1002/rmv.2541] [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: 10/23/2023] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
As the mankind counters the ongoing COVID-19 pandemic by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), it simultaneously witnesses the emergence of mpox virus (MPXV) that signals at global spread and could potentially lead to another pandemic. Although MPXV has existed for more than 50 years now with most of the human cases being reported from the endemic West and Central African regions, the disease is recently being reported in non-endemic regions too that affect more than 50 countries. Controlling the spread of MPXV is important due to its potential danger of a global spread, causing severe morbidity and mortality. The article highlights the transmission dynamics, zoonosis potential, complication and mitigation strategies for MPXV infection, and concludes with suggested 'one health' approach for better management, control and prevention. Bibliometric analyses of the data extend the understanding and provide leads on the research trends, the global spread, and the need to revamp the critical research and healthcare interventions. Globally published mpox-related literature does not align well with endemic areas/regions of occurrence which should ideally have been the scenario. Such demographic and geographic gaps between the location of the research work and the endemic epicentres of the disease need to be bridged for greater and effective translation of the research outputs to pubic healthcare systems, it is suggested.
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Affiliation(s)
- Ranjan K Mohapatra
- Department of Chemistry, Government College of Engineering, Keonjhar, Odisha, India
| | - Puneet K Singh
- School of Biotechnology, Campus-11, KIIT Deemed-to-be-University, Bhubaneswar, Odisha, India
| | - Francesco Branda
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Snehasish Mishra
- School of Biotechnology, Campus-11, KIIT Deemed-to-be-University, Bhubaneswar, Odisha, India
| | | | - Tarun K Suvvari
- Department of Medicine, Rangaraya Medical College, Kakinada, Andhra Pradesh, India
| | - Venkataramana Kandi
- Department of Microbiology, Prathima Institute of Medical Sciences, Karimnagar, Telangana, India
| | - Azaj Ansari
- Department of Chemistry, Central University of Haryana, Mahendergarh, Haryana, India
| | - Dhruv N Desai
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mubarak Alfaresi
- Department of Microbiology, National Reference Laboratory, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
- Department of Pathology, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Nawal A Al Kaabi
- College of Medicine and Health Science, Khalifa University, Abu Dhabi, United Arab Emirates
- Sheikh Khalifa Medical City, Abu Dhabi Health Services Company (SEHA), Abu Dhabi, United Arab Emirates
| | - Mona A Al Fares
- Department of Internal Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Muhammad A Halwani
- Department of Medical Microbiology, Faculty of Medicine, Al Baha University, Al Baha, Saudi Arabia
| | - Mohammed Alissa
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
- Department of Public Health and Nutrition, The University of Haripur, Haripur, Pakistan
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Nayak T, Chadaga K, Sampathila N, Mayrose H, Gokulkrishnan N, Bairy G M, Prabhu S, S SK, Umakanth S. Deep learning based detection of monkeypox virus using skin lesion images. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2023; 18:100243. [PMID: 37293134 PMCID: PMC10236906 DOI: 10.1016/j.medntd.2023.100243] [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: 03/07/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/10/2023] Open
Abstract
As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.
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Affiliation(s)
- Tushar Nayak
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Hilda Mayrose
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Nitila Gokulkrishnan
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Muralidhar Bairy G
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Swathi K S
- Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Shashikiran Umakanth
- Department of Medicine, Dr. T.M.A. Pai Hospital, Manipal Academy of Higher Education, Udupi, Karnataka, 576101, India
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Sadad T, Aurangzeb RA, Safran M, Alfarhood S, Kim J. Classification of Highly Divergent Viruses from DNA/RNA Sequence Using Transformer-Based Models. Biomedicines 2023; 11:biomedicines11051323. [PMID: 37238994 DOI: 10.3390/biomedicines11051323] [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: 02/28/2023] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences.
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Affiliation(s)
- Tariq Sadad
- Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan
| | - Raja Atif Aurangzeb
- Department of Computer Science & Software Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Jungsuk Kim
- Department of Biomedical Engineering, Gachon University, Seongnam-si 13120, Republic of Korea
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Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050824. [PMID: 36899968 PMCID: PMC10000611 DOI: 10.3390/diagnostics13050824] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread.
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Uzun Ozsahin D, Mustapha MT, Uzun B, Duwa B, Ozsahin I. Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework. Diagnostics (Basel) 2023; 13:diagnostics13020292. [PMID: 36673101 PMCID: PMC9858137 DOI: 10.3390/diagnostics13020292] [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: 12/01/2022] [Revised: 01/07/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state-of-the-art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Mubarak Taiwo Mustapha
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Berna Uzun
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Statistics, Carlos III University of Madrid, 28903 Getafe, Spain
| | - Basil Duwa
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Ilker Ozsahin
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
- Correspondence:
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