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Cardona F, Pérez-Tur J. Special Issue "Molecular and Genetic Aspects of SARS-CoV-2 Infection and COVID-19 Disease". Int J Mol Sci 2024; 25:4670. [PMID: 38731889 PMCID: PMC11083453 DOI: 10.3390/ijms25094670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
We are pleased to present the first and second editions of this Special Issue, titled "Molecular and Genetic Aspects of SARS-CoV-2 Infection and COVID-19 Disease", of the International Journal of Molecular Sciences [...].
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Affiliation(s)
- Fernando Cardona
- Unitat de Genètica Molecular, Institut de Biomedicina de València-CSIC, Jaume Roig 11, 46010 València, Spain
- CIBERNED, ISCIII, 46010 València, Spain
| | - Jordi Pérez-Tur
- Unitat de Genètica Molecular, Institut de Biomedicina de València-CSIC, Jaume Roig 11, 46010 València, Spain
- CIBERNED, ISCIII, 46010 València, Spain
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Zhang J, Heath LS. Adaptive group testing strategy for infectious diseases using social contact graph partitions. Sci Rep 2023; 13:12102. [PMID: 37495642 PMCID: PMC10372051 DOI: 10.1038/s41598-023-39326-9] [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: 02/23/2023] [Accepted: 07/24/2023] [Indexed: 07/28/2023] Open
Abstract
Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman's method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman's method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman's method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.
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Affiliation(s)
- Jingyi Zhang
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA.
| | - Lenwood S Heath
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA
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Fan G, Jin Y, Wang Q, Yue Y. Assessing the comparability of cycle threshold values derived from five external quality assessment rounds for omicron nucleic acid testing. Virol J 2023; 20:119. [PMID: 37291570 PMCID: PMC10249569 DOI: 10.1186/s12985-023-02032-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/07/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND A variety of open-system real-time reverse transcriptase polymerase chain reaction (RT-PCR) assays for several acute respiratory syndrome coronavirus 2 are currently in use. This study aimed to ensure the quality of omicron nucleic acid testing and to assess the comparability of cycle threshold (Ct) values derived from RT-PCR. METHODS Five external quality assessment (EQA) rounds using the omicron virus-like particles were organized between February 2022 and June 2022. RESULTS A total of 1401 qualitative EQA reports have been collected. The overall positive percentage agreement was 99.72%, the negative percentage agreement was 99.75%, and the percent agreement was 99.73%. This study observed a significant variance in Ct values derived from different test systems. There was a wide heterogeneity in PCR efficiency among different RT-PCR kits and inter-laboratories. CONCLUSION There was strong concordance among laboratories performing qualitative omicron nucleic acid testing. Ct values from qualitative RT-PCR tests should not be used for clinical or epidemiological decision-making to avoid the potential for misinterpretation of the results.
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Affiliation(s)
- Gaowei Fan
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, China
- Beijing Medical Laboratory Quality Control and Improvement Center, Beijing, China
| | - Yali Jin
- Beijing Center for Clinical Laboratory, Beijing, China
| | - Qingtao Wang
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, China.
- Beijing Center for Clinical Laboratory, Beijing, China.
- Beijing Medical Laboratory Quality Control and Improvement Center, Beijing, China.
| | - Yuhong Yue
- Department of Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, China.
- Beijing Center for Clinical Laboratory, Beijing, China.
- Beijing Medical Laboratory Quality Control and Improvement Center, Beijing, China.
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Pei F, Feng S, Hu W, Liu B, Mu X, Hao Q, Cao Y, Lei W, Tong Z. Sandwich mode lateral flow assay for point-of-care detecting SARS-CoV-2. Talanta 2023; 253. [PMCID: PMC9612878 DOI: 10.1016/j.talanta.2022.124051] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The global corona virus disease 2019 (COVID-19) has been announced a pandemic outbreak, and has threatened human life and health seriously. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as its causative pathogen, is widely detected in the screening of COVID-19 patients, infected people and contaminated substances. Lateral flow assay (LFA) is a popular point-of-care detection method, possesses advantages of quick response, simple operation mode, portable device, and low cost. Based on the above advantages, LFA has been widely developed for detecting SARS-CoV-2. In this review, we summarized the articles about the sandwich mode LFA detecting SARS-CoV-2, classified according to the target detection objects indicating genes, nucleocapsid protein, spike protein, and specific antibodies of SARS-CoV-2. In each part, LFA is further classified and summarized according to different signal detection types. Additionally, the properties of the targets were introduced to clarify their detection significance. The review is expected to provide a helpful guide for LFA sensitization and marker selection of SARS-CoV-2.
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Affiliation(s)
- Fubin Pei
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China,State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Shasha Feng
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China,State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Wei Hu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bing Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Xihui Mu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Qingli Hao
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Yang Cao
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
| | - Wu Lei
- School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China,Corresponding author
| | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China,Corresponding author
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Fast and Sensitive Detection of SARS-CoV-2 Nucleic Acid Using a Rapid Detection System Free of RNA Extraction. Int J Anal Chem 2023; 2023:8053524. [PMID: 36714173 PMCID: PMC9883100 DOI: 10.1155/2023/8053524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/14/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
Objectives To establish and evaluate the analytical and clinical performance of the Flash20 SARS-CoV-2 nucleic acid rapid detection system free of RNA extraction. Methods The limit of detection (LoD) was determined using a negative nasopharyngeal swab matrix spiked with different concentrations of SARS-CoV-2 virus; a total of 734,337 reference sequences of viral genomes from GenBank were used for the in-silico analysis to assess the inclusivity of the assay. The specificity of the system was evaluated by testing 27 medically relevant organisms. A total of 115 clinical specimens were collected and tested on the Flash20 SARS-CoV-2 detection system and with an FDA-approved comparator test to assess the clinical performance of the system. Results The LoD of the Flash20 SARS-CoV-2 detection system is 250 copies/mL with a positive rate ≥90% (n = 20); alignments results showed that over 99% identity of the primer and probe of the Flash20 SARS-CoV-2 nucleic acid rapid detection system to the available SARS-CoV-2 sequences; the omicron samples tested 100% positive. None of the 27 organisms showed cross-reactivity with the Flash20 SARS-CoV-2 nucleic acid rapid detection system. Among all the 215 clinical samples, the Flash20 SARS-CoV-2 nucleic acid rapid detection system exhibits a high sensitivity of 99.24% (131/132) and 100% (83/83) specificity. Conclusion The nucleic acid rapid detection system provides sensitive and accurate detection of SARS-CoV-2 free of RNA extraction. The high sensitivity and short time to results of approximately 35 minutes may impact earlier infection control and disease management.
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Fu YX, Guo WY, Wang N, Dai YJ, Zhang ZY, Sun XL, Yang WC, Yang GF. Diagnosis of Bacterial Plant Diseases via a Nitroreductase-Activated Fluorescent Sensor. Anal Chem 2022; 94:17692-17699. [PMID: 36469707 DOI: 10.1021/acs.analchem.2c04614] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Plant diseases caused by bacteria have become one of the serious problems that threaten human food security, which led to the remarkable reduction of agricultural yields and economic loss. Nitroreductase (NTR), as an important biomarker, is highly expressed in bacteria, and the level of NTR is closely related to the progression of pathogen infection. Therefore, the design of small-molecule fluorescent sensors targeting NTR is of great significance for the detection and diagnosis of plant pathogenic bacteria. In this study, a new fluorescent sensor targeting NTR was discovered and then successfully applied to the imaging of zebrafish and pathogenic bacteria. Most importantly, the developed sensor achieved the real-time diagnosis of Brassica napus L. infected with bacteria, which provides a promising tool for examining the temporal and spatial infection of plant pathogens in precision agriculture.
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Affiliation(s)
- Yi-Xuan Fu
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Wu-Yingzheng Guo
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Nan Wang
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Yi-Jie Dai
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Zi-Ye Zhang
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Xin-Lin Sun
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Wen-Chao Yang
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
| | - Guang-Fu Yang
- Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, College of Chemistry, Central China Normal University, Wuhan430079, P.R. China
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Wang G, Wang L, Meng Z, Su X, Jia C, Qiao X, Pan S, Chen Y, Cheng Y, Zhu M. Visual Detection of COVID-19 from Materials Aspect. ADVANCED FIBER MATERIALS 2022; 4:1304-1333. [PMID: 35966612 PMCID: PMC9358106 DOI: 10.1007/s42765-022-00179-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 05/25/2022] [Indexed: 05/25/2023]
Abstract
Abstract In the recent COVID-19 pandemic, World Health Organization emphasized that early detection is an effective strategy to reduce the spread of SARS-CoV-2 viruses. Several diagnostic methods, such as reverse transcription-polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA), have been applied based on the mechanism of specific recognition and binding of the probes to viruses or viral antigens. Although the remarkable progress, these methods still suffer from inadequate cellular materials or errors in the detection and sampling procedure of nasopharyngeal/oropharyngeal swab collection. Therefore, developing accurate, ultrafast, and visualized detection calls for more advanced materials and technology urgently to fight against the epidemic. In this review, we first summarize the current methodologies for SARS-CoV-2 diagnosis. Then, recent representative examples are introduced based on various output signals (e.g., colorimetric, fluorometric, electronic, acoustic). Finally, we discuss the limitations of the methods and provide our perspectives on priorities for future test development. Graphical Abstract
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Affiliation(s)
- Gang Wang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Le Wang
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Zheyi Meng
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Xiaolong Su
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Chao Jia
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Xiaolan Qiao
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Shaowu Pan
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Yinjun Chen
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Yanhua Cheng
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
| | - Meifang Zhu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620 China
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Wang Y, Xu H, Dong Z, Wang Z, Yang Z, Yu X, Chang L. Micro/nano biomedical devices for point-of-care diagnosis of infectious respiratory diseases. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2022; 14:100116. [PMID: 35187465 PMCID: PMC8837495 DOI: 10.1016/j.medntd.2022.100116] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/14/2021] [Accepted: 01/18/2022] [Indexed: 12/12/2022] Open
Abstract
Corona Virus Disease 2019 (COVID-19) has developed into a global pandemic in the last two years, causing significant impacts on our daily life in many countries. Rapid and accurate detection of COVID-19 is of great importance to both treatments and pandemic management. Till now, a variety of point-of-care testing (POCT) approaches devices, including nucleic acid-based test and immunological detection, have been developed and some of them has been rapidly ruled out for clinical diagnosis of COVID-19 due to the requirement of mass testing. In this review, we provide a summary and commentary on the methods and biomedical devices innovated or renovated for the quick and early diagnosis of COVID-19. In particular, some of micro and nano devices with miniaturized structures, showing outstanding analytical performances such as ultra-sensitivity, rapidness, accuracy and low cost, are discussed in this paper. We also provide our insights on the further implementation of biomedical devices using advanced micro and nano technologies to meet the demand of point-of-care diagnosis and home testing to facilitate pandemic management. In general, our paper provides a comprehensive overview of the latest advances on the POCT device for diagnosis of COVID-19, which may provide insightful knowledge for researcher to further develop novel diagnostic technologies for rapid and on-site detection of pathogens including SARS-CoV-2.
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Affiliation(s)
- Yang Wang
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Huiren Xu
- School of Biomedical Information and Engineering, Hainan Medical University, Haikou, 471100, China
| | - Zaizai Dong
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Zhiying Wang
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Zhugen Yang
- School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL, United Kingdom,Corresponding author
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China,Corresponding author.
| | - Lingqian Chang
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China,Corresponding author.
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