Simultaneous detection of Dengue and Zika virus RNA sequences with a three-dimensional Cu-based zwitterionic metal–organic framework, comparison of single and synchronous fluorescence analysis

Xie, Bingbing; Qiu, Guang; Hu, Peipei; Liang, Zhen; Liang, Yemei; Sun, Bin; Bai, Liping; Jiang, Zhi‐Hong; Chen, Jinxiang

doi:10.1016/j.snb.2017.06.085

Cited by 89 publications

(58 citation statements)

References 51 publications

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“… Virus Target viral nucleic acid sequences Probe DNA strands (P-DNA) Refs. HIV-1 ss-DNA 5′-GCTAGAGATTTTCCACACTGACT-3′ 5′-FAM-CATGTGTCCAGCTGATTGCC-3′ Pan et al (2018) HIV-1 ds-DNA 5′-CGAGTTAAGAAGAAAAAAGATTGAGC-3′ /5′-GCTCAATCTTTTTTCTTCTTAACTCG-3′ 5′-FAM-TTCTTCTTTTTTCT-3′ ( Li et al, 2016 ; Yang et al, 2015 ) SUDV RNA 5′-GAUGAGGACAAACUUUUUAA-3′ 5′-FAM-TTAAAAAGTTTGTCCTCATC-3′ ( Li et al, 2016 ; Yang et al, 2015 ) Ebola virus Ebola virus conserved sequence of RNA (T 1 ) 5′-GGCAAUCAGUUGGACACAUG-3′ The complementary sequence for T 1 as probe DNA-1 (P-DNA-1) 5′-CATGTGTCCAACTGATTGCC-FAM-3′ Qiu et al (2018) Ebola virus-encoded miRNA-like fragment (T 2 ) 5′-UGCUUCAUUAGCACUUUGGGGC-3′ The complementary sequence for T 2 as probe DNA-2 (P-DNA-2) 5′-GCCCCAAAGTGCTAATGAAGCA-ROX-3′ Zika virus (ZIKV) 5′-ACUUGGGUGGAUAGGUAGUCCAUGU-3′ 5′-TAMRA-ACATGGACTACCTATCCACCCAAGT-3′ Li et al (2019b) Dengue virus (DENV) 5′-UGGUGCUGUUGAAUCAACAGGUUCU-3′ 5′-FAM-AGAACCTGTTGATTCAACAGCACCA-3′ Xie et al (2018) Hepatitis B virus (HBV) 5′-TTGTCCTGGCTATCGCTGGATGTGTCTGC-3′ 5′-TATATAGCAGACACATCCAGCGATAGCCAGGACAATATATA-FAM-3′ Ye et al (2014) …”

Section: Viral Nucleic Acid Detection Based On Mofmentioning

confidence: 99%

“…4 ) and it's of great significance for effective prevention of virus proliferation in the clinic. Many viruses including ss-DNA (such as HIV ss-DNA ( Pan et al, 2018 ; Zhu et al, 2013 ), respiratory syncytial virus ( Guo et al, 2015 )), ds-DNA (such as HIV ds-DNA ( Chen et al, 2013 )), ss-RNA (such as Ebola virus ( Qin et al, 2016 ), Sudan virus ( Yang et al, 2015 ), Zika virus ( Xie et al, 2018 ), and Dengue virus ( Xie et al, 2018 )), have been successfully detected by MOF-based sensing platforms. In this section, nucleic acid detection is divided into single-virus detection and multi-virus detection.…”

Section: Viral Nucleic Acid Detection Based On Mofmentioning

confidence: 99%

“…The outbreak of the arisen viruses, such as human immunodeficiency virus (HIV) ( Chen et al, 2013 ), Ebola virus ( Qin et al, 2016 ; Yang et al, 2015 ), Zika virus ( Xie et al, 2018 ), Dengue virus ( Xie et al, 2018 ), as well as the ongoing novel coronavirus (SARS-CoV-2) ( Petrosillo et al, 2020 ), raised tremendous challenges to public health. The early detection of viruses is essential for the prevention and treatment of the associated diseases ( Ambinder et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

“…In 2013, Chen and coworkers ( Zhu et al, 2013 ) first reported two dimensional (2D) MOF ([Cu(H 2 DTOA)] n ) which was utilized as a sensing platform for effective detection of thrombin and HIV-1 DNA sequences with high selectivity and sensitivity. Currently, MOF-based sensing technology has been gradually used for detecting various viruses, involving HIV ss-DNA ( Pan et al, 2018 ; Zhu et al, 2013 ), HIV ds-DNA ( Chen et al, 2013 ), respiratory syncytial virus ( Guo et al, 2015 ), Ebola virus ( Qin et al, 2016 ), Sudan virus ( Yang et al, 2015 ), Zika virus ( Xie et al, 2018 ), Dengue virus ( Xie et al, 2018 ), H 5 N 1 virus ( Wei, 2013 ), hepatitis A virus (HAV) ( Luo et al, 2020b ), etc. And extensive MOFs or MOF-derived porous carbon materials including UiO-66-NH 2 ( Zhang et al, 2014a ), ZIF-8 ( Pan et al, 2018 ), MIL-88B ( Tian et al, 2015 ), MIL-101 ( Yang et al, 2020 ), H 2 dtoaCu ( Ye et al, 2014 ), NiCo 2 O 4 embedded with carbon nanotubes (NiCo-MOF) ( Jia et al, 2019 ), etc., have been applied for virus detection.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Metal-organic frameworks for virus detection

Wang

et al. 2020

Biosensors and Bioelectronics

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Virus severely endangers human life and health, and the detection of viruses is essential for the prevention and treatment of associated diseases. Metal-organic framework (MOF), a novel hybrid porous material which is bridged by the metal clusters and organic linkers, has become a promising biosensor platform for virus detection due to its outstanding properties including high surface area, adjustable pore size, easy modification, etc. However, the MOF-based sensing platforms for virus detection are rarely summarized. This review systematically divided the detection platforms into nucleic acid and immunological (antigen and antibody) detection, and the underlying sensing mechanisms were interpreted. The nucleic acid sensing was discussed based on the properties of MOF (such as metal ion, functional group, geometry structure, size, porosity, stability, etc.), revealing the relationship between the sensing performance and properties of MOF. Moreover, antibodies sensing based on the fluorescence detection and antigens sensing based on molecular imprinting or electrochemical immunoassay were highlighted. Furthermore, the remaining challenges and future development of MOF for virus detection were further discussed and proposed. This review will provide valuable references for the construction of sophisticated sensing platform for the detection of viruses, especially the 2019 coronavirus.

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Section: Viral Nucleic Acid Detection Based On Mofmentioning

confidence: 99%

Section: Viral Nucleic Acid Detection Based On Mofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metal-organic frameworks for virus detection

Wang

et al. 2020

Biosensors and Bioelectronics

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“…The detection limit for synchronous fluorescence analysis reported to be 184 and 121 pM, respectively. This Cu-based MOF has an affinity toward carboxyfluorescein (FAM), and 5(6)-carboxyrhodamine, triethylammonium salt-tagged single-stranded probe DNA and the study reported no cross-reaction between the two probes during synchronous detection [148]. Additionally, a Cu-based MOF relying on its affinity for 5'6-FAM (as a fluorescent DNA probe) was designed to detect the H1N1 antibody in a serum sample virus with a detection limit of 1.6 nM [149].…”

Section: Based On Metal-organic Frameworkmentioning

confidence: 99%

Point-of-Use Rapid Detection of SARS-CoV-2: Nanotechnology-Enabled Solutions for the COVID-19 Pandemic

Rabiee

Bagherzadeh

Ghasemi

et al. 2020

IJMS

113

103

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic that has been spreading around the world since December 2019. More than 10 million affected cases and more than half a million deaths have been reported so far, while no vaccine is yet available as a treatment. Considering the global healthcare urgency, several techniques, including whole genome sequencing and computed tomography imaging have been employed for diagnosing infected people. Considerable efforts are also directed at detecting and preventing different modes of community transmission. Among them is the rapid detection of virus presence on different surfaces with which people may come in contact. Detection based on non-contact optical techniques is very helpful in managing the spread of the virus, and to aid in the disinfection of surfaces. Nanomaterial-based methods are proven suitable for rapid detection. Given the immense need for science led innovative solutions, this manuscript critically reviews recent literature to specifically illustrate nano-engineered effective and rapid solutions. In addition, all the different techniques are critically analyzed, compared, and contrasted to identify the most promising methods. Moreover, promising research ideas for high accuracy of detection in trace concentrations, via color change and light-sensitive nanostructures, to assist fingerprint techniques (to identify the virus at the contact surface of the gas and solid phase) are also presented.

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Polyphenol‐Containing Nanoparticles: Synthesis, Properties, and Therapeutic Delivery

et al. 2021

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Polyphenols, the phenolic hydroxyl group-containing organic molecules, are widely found in natural plants and have shown beneficial effects on human health. Recently, polyphenol-containing nanoparticles have attracted extensive research attention due to their antioxidation property, anticancer activity, and universal adherent affinity, and thus have shown great promise in the preparation, stabilization, and modification of multifunctional nanoassemblies for bioimaging, therapeutic delivery, and other biomedical applications. Additionally, the metal−polyphenol networks, formed by the coordination interactions between polyphenols and metal ions, have been used to prepare an important class of polyphenol-containing nanoparticles for surface modification, bioimaging, drug delivery, and disease treatments. By focusing on the interactions between polyphenols and different materials (e.g., metal ions, inorganic materials, polymers, proteins, and nucleic acids), a comprehensive review on the synthesis and properties of the polyphenol-containing nanoparticles is provided. Moreover, the remarkable versatility of polyphenol-containing nanoparticles in different biomedical applications, including biodetection, multimodal bioimaging, protein and gene delivery, bone repair, antibiosis, and cancer theranostics is also demonstrated. Finally, the challenges faced by future research regarding the polyphenol-containing nanoparticles are discussed.

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Simultaneous detection of Dengue and Zika virus RNA sequences with a three-dimensional Cu-based zwitterionic metal–organic framework, comparison of single and synchronous fluorescence analysis

Cited by 89 publications

References 51 publications

Metal-organic frameworks for virus detection

Metal-organic frameworks for virus detection

Point-of-Use Rapid Detection of SARS-CoV-2: Nanotechnology-Enabled Solutions for the COVID-19 Pandemic

Polyphenol‐Containing Nanoparticles: Synthesis, Properties, and Therapeutic Delivery

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