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Pang S, Wang M, Yuan J, Yang Z, Yu H, Zhang H, Dong T, Liu A. Sensitive Dual-Signal ELISA Based on Specific Phage-Displayed Double Peptide Probes with Internal Filtering Effect to Assay Monkeypox Virus Antigen. Anal Chem 2024; 96:10064-10073. [PMID: 38842443 DOI: 10.1021/acs.analchem.4c01802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
The global spread of monkeypox has become a worldwide public healthcare issue. Therefore, there is an urgent need for accurate and sensitive detection methods to effectively control its spreading. Herein, we screened by phage display two peptides M4 (sequence: DPCGERICSIAL) and M6 (sequence: SCSSFLCSLKVG) with good affinity and specificity to monkeypox virus (MPXV) B21R protein. To simulate the state of the peptide in the phage and to avoid spatial obstacles of the peptide, GGGSK was added at the C terminus of M4 and named as M4a. Molecular docking shows that peptide M4a and peptide M6 are bound to different epitopes of B21R by hydrogen bonds and salt-bridge interactions, respectively. Then, peptide M4a was selected as the capture probe, phage M6 as the detection probe, and carbonized polymer dots (CPDs) as the fluorescent probe, and a colorimetric and fluorescent double-signal capture peptide/antigen/signal peptide-displayed phage sandwich ELISA triggered by horseradish peroxidase (HRP) through a simple internal filtration effect (IFE) was constructed. HRP catalyzes H2O2 to oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to generate blue oxidized TMB, which can further quench the fluorescence of CPDs through IFE, enabling to detect MPXV B21R in colorimetric and fluorescent modes. The proposed simple immunoassay platform shows good sensitivity and reliability in MPXV B21R detection. The limit of detection for colorimetric and fluorescent modes was 27.8 and 9.14 pg/mL MPXV B21R, respectively. Thus, the established double-peptide sandwich-based dual-signal immunoassay provides guidance for the development of reliable and sensitive antigen detection capable of mutual confirmation, which also has great potential for exploring various analytical strategies for other respiratory virus surveillance.
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Affiliation(s)
- Shuang Pang
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Mingyang Wang
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Jinlong Yuan
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Zhonghuang Yang
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Haipeng Yu
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Haohan Zhang
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Tao Dong
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
| | - Aihua Liu
- Institute for Chemical Biology & Biosensing, College of Life Sciences, Qingdao University, 308 Ningxia Road, Qingdao 266071, China
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Lee S, Yoo YK, Han SI, Lee D, Cho SY, Park C, Lee D, Yoon DS, Lee JH. Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection. Analyst 2023; 148:6001-6010. [PMID: 37882491 DOI: 10.1039/d3an01189e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.
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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.
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do 25601, Republic of Korea
| | - Sung Il Han
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi 13449, Republic of Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi 13449, Republic of Korea
| | - Sung-Yeon Cho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chulmin Park
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dongtak Lee
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea.
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - 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, South Korea
- Astrion Inc., Seoul 02841, Republic of Korea
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul 01897, Republic of Korea.
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Lee S, Kim S, Yoon DS, Park JS, Woo H, Lee D, Cho SY, Park C, Yoo YK, Lee KB, Lee JH. Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay. Nat Commun 2023; 14:2361. [PMID: 37095107 PMCID: PMC10124933 DOI: 10.1038/s41467-023-38104-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 04/14/2023] [Indexed: 04/26/2023] Open
Abstract
Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMARTAI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.
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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
| | - Sunmok Kim
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, 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
| | - Jeong Soo Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Hyowon Woo
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea
| | - Sung-Yeon Cho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chulmin Park
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, 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.
| | - 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.
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Park SJ, Lee S, Lee D, Lee NE, Park JS, Hong JH, Jang JW, Kim H, Roh S, Lee G, Lee D, Cho SY, Park C, Lee DG, Lee R, Nho D, Yoon DS, Yoo YK, Lee JH. PCR-like performance of rapid test with permselective tunable nanotrap. Nat Commun 2023; 14:1520. [PMID: 36934093 PMCID: PMC10024276 DOI: 10.1038/s41467-023-37018-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/24/2023] [Indexed: 03/20/2023] Open
Abstract
Highly sensitive rapid testing for COVID-19 is essential for minimizing virus transmission, especially before the onset of symptoms and in asymptomatic cases. Here, we report bioengineered enrichment tools for lateral flow assays (LFAs) with enhanced sensitivity and specificity (BEETLES2), achieving enrichment of SARS-CoV-2 viruses, nucleocapsid (N) proteins and immunoglobulin G (IgG) with 3-minute operation. The limit of detection is improved up to 20-fold. We apply this method to clinical samples, including 83% with either intermediate (35%) or low viral loads (48%), collected from 62 individuals (n = 42 for positive and n = 20 for healthy controls). We observe diagnostic sensitivity, specificity, and accuracy of 88.1%, 100%, and 91.9%, respectively, compared with commercial LFAs alone achieving 14.29%, 100%, and 41.94%, respectively. BEETLES2, with permselectivity and tunability, can enrich the SARS-CoV-2 virus, N proteins, and IgG in the nasopharyngeal/oropharyngeal swab, saliva, and blood serum, enabling reliable and sensitive point-of-care testing, facilitating fast early diagnosis.
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Affiliation(s)
- Seong Jun Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - 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
| | - Dongtak Lee
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul, 02841, Republic of Korea
| | - Na Eun Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, 02841, Republic of Korea
| | - Jeong Soo Park
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea
| | - Ji Hye Hong
- 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
| | - Jae Won Jang
- 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
| | - Hyunji Kim
- 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
| | - Seokbeom Roh
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Korea
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi, 13449, Republic of Korea
| | - Sung-Yeon Cho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chulmin Park
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong-Gun Lee
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Raeseok Lee
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dukhee Nho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, 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.
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do, 25601, Republic of Korea.
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul, 01897, Republic of Korea.
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Tarim EA, Anil Inevi M, Ozkan I, Kecili S, Bilgi E, Baslar MS, Ozcivici E, Oksel Karakus C, Tekin HC. Microfluidic-based technologies for diagnosis, prevention, and treatment of COVID-19: recent advances and future directions. Biomed Microdevices 2023; 25:10. [PMID: 36913137 PMCID: PMC10009869 DOI: 10.1007/s10544-023-00649-z] [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] [Accepted: 02/21/2023] [Indexed: 03/14/2023]
Abstract
The COVID-19 pandemic has posed significant challenges to existing healthcare systems around the world. The urgent need for the development of diagnostic and therapeutic strategies for COVID-19 has boomed the demand for new technologies that can improve current healthcare approaches, moving towards more advanced, digitalized, personalized, and patient-oriented systems. Microfluidic-based technologies involve the miniaturization of large-scale devices and laboratory-based procedures, enabling complex chemical and biological operations that are conventionally performed at the macro-scale to be carried out on the microscale or less. The advantages microfluidic systems offer such as rapid, low-cost, accurate, and on-site solutions make these tools extremely useful and effective in the fight against COVID-19. In particular, microfluidic-assisted systems are of great interest in different COVID-19-related domains, varying from direct and indirect detection of COVID-19 infections to drug and vaccine discovery and their targeted delivery. Here, we review recent advances in the use of microfluidic platforms to diagnose, treat or prevent COVID-19. We start by summarizing recent microfluidic-based diagnostic solutions applicable to COVID-19. We then highlight the key roles microfluidics play in developing COVID-19 vaccines and testing how vaccine candidates perform, with a focus on RNA-delivery technologies and nano-carriers. Next, microfluidic-based efforts devoted to assessing the efficacy of potential COVID-19 drugs, either repurposed or new, and their targeted delivery to infected sites are summarized. We conclude by providing future perspectives and research directions that are critical to effectively prevent or respond to future pandemics.
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Affiliation(s)
- E Alperay Tarim
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - Muge Anil Inevi
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - Ilayda Ozkan
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - Seren Kecili
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - Eyup Bilgi
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - M Semih Baslar
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | - Engin Ozcivici
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey
| | | | - H Cumhur Tekin
- Department of Bioengineering, Izmir Institute of Technology, Izmir, Turkey.
- METU MEMS Center, Ankara, Turkey.
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6
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Li Q, Zhou X, Wang Q, Liu W, Chen C. Microfluidics for COVID-19: From Current Work to Future Perspective. BIOSENSORS 2023; 13:163. [PMID: 36831930 PMCID: PMC9953302 DOI: 10.3390/bios13020163] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/07/2023] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
Spread of coronavirus disease 2019 (COVID-19) has significantly impacted the public health and economic sectors. It is urgently necessary to develop rapid, convenient, and cost-effective point-of-care testing (POCT) technologies for the early diagnosis and control of the plague's transmission. Developing POCT methods and related devices is critical for achieving point-of-care diagnosis. With the advantages of miniaturization, high throughput, small sample requirements, and low actual consumption, microfluidics is an essential technology for the development of POCT devices. In this review, according to the different driving forces of the fluid, we introduce the common POCT devices based on microfluidic technology on the market, including paper-based microfluidic, centrifugal microfluidic, optical fluid, and digital microfluidic platforms. Furthermore, various microfluidic-based assays for diagnosing COVID-19 are summarized, including immunoassays, such as ELISA, and molecular assays, such as PCR. Finally, the challenges of and future perspectives on microfluidic device design and development are presented. The ultimate goals of this paper are to provide new insights and directions for the development of microfluidic diagnostics while expecting to contribute to the control of COVID-19.
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Affiliation(s)
- Qi Li
- Department of Pharmacy, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410017, China
| | - Xingchen Zhou
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410017, China
| | - Qian Wang
- Department of Pharmacy, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410017, China
| | - Wenfang Liu
- Department of Pharmacy, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410017, China
| | - Chuanpin Chen
- Department of Pharmacy, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410017, China
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Jamiruddin MR, Meghla BA, Islam DZ, Tisha TA, Khandker SS, Khondoker MU, Haq MA, Adnan N, Haque M. Microfluidics Technology in SARS-CoV-2 Diagnosis and Beyond: A Systematic Review. Life (Basel) 2022; 12:649. [PMID: 35629317 PMCID: PMC9146058 DOI: 10.3390/life12050649] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 12/22/2022] Open
Abstract
With the progression of the COVID-19 pandemic, new technologies are being implemented for more rapid, scalable, and sensitive diagnostics. The implementation of microfluidic techniques and their amalgamation with different detection techniques has led to innovative diagnostics kits to detect SARS-CoV-2 antibodies, antigens, and nucleic acids. In this review, we explore the different microfluidic-based diagnostics kits and how their amalgamation with the various detection techniques has spearheaded their availability throughout the world. Three other online databases, PubMed, ScienceDirect, and Google Scholar, were referred for articles. One thousand one hundred sixty-four articles were determined with the search algorithm of microfluidics followed by diagnostics and SARS-CoV-2. We found that most of the materials used to produce microfluidics devices were the polymer materials such as PDMS, PMMA, and others. Centrifugal force is the most commonly used fluid manipulation technique, followed by electrochemical pumping, capillary action, and isotachophoresis. The implementation of the detection technique varied. In the case of antibody detection, spectrometer-based detection was most common, followed by fluorescence-based as well as colorimetry-based. In contrast, antigen detection implemented electrochemical-based detection followed by fluorescence-based detection, and spectrometer-based detection were most common. Finally, nucleic acid detection exclusively implements fluorescence-based detection with a few colorimetry-based detections. It has been further observed that the sensitivity and specificity of most devices varied with implementing the detection-based technique alongside the fluid manipulation technique. Most microfluidics devices are simple and incorporate the detection-based system within the device. This simplifies the deployment of such devices in a wide range of environments. They can play a significant role in increasing the rate of infection detection and facilitating better health services.
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Affiliation(s)
| | - Bushra Ayat Meghla
- Department of Microbiology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; (B.A.M.); (D.Z.I.); (T.A.T.)
| | - Dewan Zubaer Islam
- Department of Microbiology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; (B.A.M.); (D.Z.I.); (T.A.T.)
| | - Taslima Akter Tisha
- Department of Microbiology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; (B.A.M.); (D.Z.I.); (T.A.T.)
| | - Shahad Saif Khandker
- Gonoshasthaya-RNA Molecular Diagnostic & Research Center, Dhanmondi, Dhaka 1205, Bangladesh; (S.S.K.); (M.A.H.)
| | - Mohib Ullah Khondoker
- Department of Community Medicine, Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka 1344, Bangladesh;
| | - Md. Ahsanul Haq
- Gonoshasthaya-RNA Molecular Diagnostic & Research Center, Dhanmondi, Dhaka 1205, Bangladesh; (S.S.K.); (M.A.H.)
| | - Nihad Adnan
- Department of Microbiology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh; (B.A.M.); (D.Z.I.); (T.A.T.)
| | - Mainul Haque
- The Unit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kem Perdana Sugai Besi, Kuala Lumpur 57000, Malaysia
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