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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [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: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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Karimi A, Kalhor A, Sadeghi Tabrizi M. Forward layer-wise learning of convolutional neural networks through separation index maximizing. Sci Rep 2024; 14:8576. [PMID: 38615041 DOI: 10.1038/s41598-024-59176-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 04/08/2024] [Indexed: 04/15/2024] Open
Abstract
This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems. The algorithm utilizes the Separation Index (SI) as a supervised complexity measure to evaluate and train each layer in a forward manner. The proposed method explains that gradually increasing the SI through layers reduces the input data's uncertainties and disturbances, achieving a better feature space representation. Hence, by approximating the SI with a variant of local triplet loss at each layer, a gradient-based learning algorithm is suggested to maximize it. Inspired by the NGRAD (Neural Gradient Representation by Activity Differences) hypothesis, the proposed algorithm operates in a forward manner without explicit error information from the last layer. The algorithm's performance is evaluated on image classification tasks using VGG16, VGG19, AlexNet, and LeNet architectures with CIFAR-10, CIFAR-100, Raabin-WBC, and Fashion-MNIST datasets. Additionally, the experiments are applied to text classification tasks using the DBPedia and AG's News datasets. The results demonstrate that the proposed layer-wise learning algorithm outperforms state-of-the-art methods in accuracy and time complexity.
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Affiliation(s)
- Ali Karimi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ahmad Kalhor
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Melika Sadeghi Tabrizi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Yang Z, Guo J, Wang L, Zhang J, Ding L, Liu H, Yu X. Nanozyme-Enhanced Electrochemical Biosensors: Mechanisms and Applications. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2307815. [PMID: 37985947 DOI: 10.1002/smll.202307815] [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: 09/07/2023] [Revised: 10/22/2023] [Indexed: 11/22/2023]
Abstract
Nanozymes, as innovative materials, have demonstrated remarkable potential in the field of electrochemical biosensors. This article provides an overview of the mechanisms and extensive practical applications of nanozymes in electrochemical biosensors. First, the definition and characteristics of nanozymes are introduced, emphasizing their significant role in constructing efficient sensors. Subsequently, several common categories of nanozyme materials are delved into, including metal-based, carbon-based, metal-organic framework, and layered double hydroxide nanostructures, discussing their applications in electrochemical biosensors. Regarding their mechanisms, two key roles of nanozymes are particularly focused in electrochemical biosensors: selective enhancement and signal amplification, which crucially support the enhancement of sensor performance. In terms of practical applications, the widespread use of nanozyme-based electrochemical biosensors are showcased in various domains. From detecting biomolecules, pollutants, nucleic acids, proteins, to cells, providing robust means for high-sensitivity detection. Furthermore, insights into the future development of nanozyme-based electrochemical biosensors is provided, encompassing improvements and optimizations of nanozyme materials, innovative sensor design and integration, and the expansion of application fields through interdisciplinary collaboration. In conclusion, this article systematically presents the mechanisms and applications of nanozymes in electrochemical biosensors, offering valuable references and prospects for research and development in this field.
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Affiliation(s)
- Zhongwei Yang
- Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
| | - Jiawei Guo
- Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
| | - Longwei Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety & CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, University of Chinese Academy of Science, Beijing, 100190, P. R. China
| | - Jian Zhang
- Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Göteborg, 41296, Sweden
| | - Longhua Ding
- Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
| | - Hong Liu
- Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
| | - Xin Yu
- Institute for Advanced Interdisciplinary Research (iAIR), School of Chemistry and Chemical Engineering, University of Jinan, Jinan, 250022, P. R. China
- Key Laboratory of Optic-electric Sensing and Analytical Chemistry for Life Science, MOE, Qingdao University of Science and Technology, Qingdao, 266042, P. R. China
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Lee S, Park JS, Woo H, Yoo YK, Lee D, Chung S, Yoon DS, Lee KB, Lee JH. Rapid deep learning-assisted predictive diagnostics for point-of-care testing. Nat Commun 2024; 15:1695. [PMID: 38402240 PMCID: PMC10894262 DOI: 10.1038/s41467-024-46069-2] [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] [Received: 09/19/2023] [Accepted: 02/12/2024] [Indexed: 02/26/2024] Open
Abstract
Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.
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Affiliation(s)
- Seungmin Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Jeong Soo Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul, 02841, Republic of Korea
| | - Hyowon Woo
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea
| | - Seok Chung
- School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul, 02841, Republic of Korea
| | - Dae Sung Yoon
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, Republic of Korea
- Astrion Inc, Seoul, 02841, Republic of Korea
| | - Ki-Baek Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea.
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea.
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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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Liu Y, Li Y, Hang Y, Wang L, Wang J, Bao N, Kim Y, Jang HW. Rapid assays of SARS-CoV-2 virus and noble biosensors by nanomaterials. NANO CONVERGENCE 2024; 11:2. [PMID: 38190075 PMCID: PMC10774473 DOI: 10.1186/s40580-023-00408-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024]
Abstract
The COVID-19 outbreak caused by SARS-CoV-2 in late 2019 has spread rapidly across the world to form a global epidemic of respiratory infectious diseases. Increased investigations on diagnostic tools are currently implemented to assist rapid identification of the virus because mass and rapid diagnosis might be the best way to prevent the outbreak of the virus. This critical review discusses the detection principles, fabrication techniques, and applications on the rapid detection of SARS-CoV-2 with three categories: rapid nuclear acid augmentation test, rapid immunoassay test and biosensors. Special efforts were put on enhancement of nanomaterials on biosensors for rapid, sensitive, and low-cost diagnostics of SARS-CoV-2 virus. Future developments are suggested regarding potential candidates in hospitals, clinics and laboratories for control and prevention of large-scale epidemic.
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Affiliation(s)
- Yang Liu
- School of Public Health, Nantong University, Nantong, 226019, Jiangsu, People's Republic of China
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- NantongEgens Biotechnology Co., LTD, Nantong, 226019, Jiangsu, People's Republic of China
| | - Yilong Li
- School of Public Health, Nantong University, Nantong, 226019, Jiangsu, People's Republic of China
| | - Yuteng Hang
- School of Public Health, Nantong University, Nantong, 226019, Jiangsu, People's Republic of China
| | - Lei Wang
- NantongEgens Biotechnology Co., LTD, Nantong, 226019, Jiangsu, People's Republic of China
| | - Jinghan Wang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ning Bao
- School of Public Health, Nantong University, Nantong, 226019, Jiangsu, People's Republic of China
| | - Youngeun Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
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7
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Ha Y. Exploiting the Potential of Magnetic Nanoparticles for Rapid Diagnosis Tests (RDTs): Nanoparticle-Antibody Conjugates and Color Development Strategies. Diagnostics (Basel) 2023; 13:3033. [PMID: 37835776 PMCID: PMC10572869 DOI: 10.3390/diagnostics13193033] [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: 08/03/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Magnetic nanoparticles (MNPs) have emerged as a promising material in disease diagnostics due to their potential to enhance detection sensitivity, facilitate concentration and purification of target substances in diverse samples, and enable favorable color-based detection. In this study, antibody-conjugated MNPs were successfully synthesized and validated through two appropriate methods: the measurement of MNPs' size and the use of phosphatase methods. Additionally, three methods were suggested and implemented for developing color in MNPs-based immunoassay, including the formation of MNP aggregations, utilization of MNPs' peroxidase-like activity, and synthesis of dually-conjugated MNPs with both enzyme and antibody. In particular, color development utilizing nanoparticle aggregations was demonstrated to result in a more yellowish color as virus concentration increased, while the peroxidase activity of MNPs exhibited a proportional increase in color intensity as the MNP concentration increased. This observation suggests the potential applicability of quantitative analysis using these methods. Furthermore, effective concentration and purification of target substances were demonstrated through the collection of MNPs using an external magnetic field, irrespective of factors such as antibody conjugation, dispersion medium, or virus binding. Finally, based on the key findings of this study, a design proposal for MNPs-based immunoassay is presented. Overall, MNPs-based immunoassays hold significant potential for advancing disease diagnostics.
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Affiliation(s)
- Yeonjeong Ha
- ICT Environment Convergence, Department of ICT Convergence, College of IT Engineering, Pyeongtaek University, 3825 Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Republic of Korea
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Harrou F, Dairi A, Dorbane A, Kadri F, Sun Y. Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests. Diagnostics (Basel) 2023; 13:diagnostics13081466. [PMID: 37189568 DOI: 10.3390/diagnostics13081466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.
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Affiliation(s)
- Fouzi Harrou
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Abdelkader Dairi
- Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Oran 31000, Algeria
| | - Abdelhakim Dorbane
- Smart Structures Laboratory (SSL), Department of Mechanical Engineering, Belhadj Bouchaib University of Ain Temouchent, Ain Temouchent 46000, Algeria
| | - Farid Kadri
- Aeroline DATA & CET, Agence 1031, Sopra Steria Group, 31770 Colomiers, France
| | - Ying Sun
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing. Viruses 2023; 15:v15020304. [PMID: 36851519 PMCID: PMC9966023 DOI: 10.3390/v15020304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.
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Wang L, Li Z. Smart Nanostructured Materials for SARS-CoV-2 and Variants Prevention, Biosensing and Vaccination. BIOSENSORS 2022; 12:1129. [PMID: 36551096 PMCID: PMC9775677 DOI: 10.3390/bios12121129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has raised great concerns about human health globally. At the current stage, prevention and vaccination are still the most efficient ways to slow down the pandemic and to treat SARS-CoV-2 in various aspects. In this review, we summarize current progress and research activities in developing smart nanostructured materials for COVID-19 prevention, sensing, and vaccination. A few established concepts to prevent the spreading of SARS-CoV-2 and the variants of concerns (VOCs) are firstly reviewed, which emphasizes the importance of smart nanostructures in cutting the virus spreading chains. In the second part, we focus our discussion on the development of stimuli-responsive nanostructures for high-performance biosensing and detection of SARS-CoV-2 and VOCs. The use of nanostructures in developing effective and reliable vaccines for SARS-CoV-2 and VOCs will be introduced in the following section. In the conclusion, we summarize the current research focus on smart nanostructured materials for SARS-CoV-2 treatment. Some existing challenges are also provided, which need continuous efforts in creating smart nanostructured materials for coronavirus biosensing, treatment, and vaccination.
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Affiliation(s)
- Lifeng Wang
- Suzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou 215000, China
| | - Zhiwei Li
- Department of Chemistry, International Institute of Nanotechnology, Northwestern University, Evanston, IL 60208-3113, USA
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Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, James CA, Lin S, Mandl KD, Matheny ME, Sendak MP, Shachar C, Williams A. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect 2022; 2022:202209c. [PMID: 36713769 PMCID: PMC9875857 DOI: 10.31478/202209c] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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12
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Peng L, Wang C, Tian G, Liu G, Li G, Lu Y, Yang J, Chen M, Li Z. Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet. Front Microbiol 2022; 13:995323. [PMID: 36212877 PMCID: PMC9539545 DOI: 10.3389/fmicb.2022.995323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Abstract
COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
- College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
| | - Chang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Gan Li
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Yuankang Lu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | | | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
- *Correspondence: Min Chen, ; Zejun Li,
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
- *Correspondence: Min Chen, ; Zejun Li,
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Dall'Alba G, Casa PL, Abreu FPD, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. BIG DATA 2022; 10:279-297. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.
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Affiliation(s)
- Gabriel Dall'Alba
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
- Genome Science and Technology Program, Faculty of Science, The University of British Columbia, Vancouver, Canada
| | - Pedro Lenz Casa
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Fernanda Pessi de Abreu
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Daniel Luis Notari
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Scheila de Avila E Silva
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
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