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https://doi.org/10.1038/s41467-018-05733-0
Title: | Visual and modular detection of pathogen nucleic acids with enzyme–DNA molecular complexes | Authors: | Ho, N.R.Y Lim, G.S Sundah, N.R Lim, D Loh, T.P Shao, H |
Keywords: | DNA enzyme genomic DNA nucleic acid virus DNA virus RNA DNA horseradish peroxidase nanomaterial nucleic acid cell detection method DNA enzyme genome molecular analysis nanoparticle nucleic acid pathogen Article controlled study DNA conformation DNA structure enzyme activity false positive result female gene locus human human cell Human papillomavirus type 16 Human papillomavirus type 18 limit of detection microfluidics molecular recognition molecular typing nonhuman nucleic acid analysis polymerase chain reaction signal detection virus genome bioassay chemistry genetics isolation and purification metabolism Papillomaviridae Biological Assay DNA Horseradish Peroxidase Humans Nanostructures Nucleic Acids Papillomaviridae |
Issue Date: | 2018 | Publisher: | Nature Publishing Group | Citation: | Ho, N.R.Y, Lim, G.S, Sundah, N.R, Lim, D, Loh, T.P, Shao, H (2018). Visual and modular detection of pathogen nucleic acids with enzyme–DNA molecular complexes. Nature Communications 9 (1) : 3238. ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-018-05733-0 | Abstract: | Rapid, visual detection of pathogen nucleic acids has broad applications in infection management. Here we present a modular detection platform, termed enzyme-assisted nanocomplexes for visual identification of nucleic acids (enVision). The system consists of an integrated circuit of enzyme–DNA nanostructures, which function as independent recognition and signaling elements, for direct and versatile detection of pathogen nucleic acids from infected cells. The built-in enzymatic cascades produce a rapid color readout for the naked eye; the assay is thus fast (<2 h), sensitive (<10 amol), and readily quantified with smartphones. When implemented on a configurable microfluidic platform, the technology demonstrates superior programmability to perform versatile computations, for detecting diverse pathogen targets and their virus–host genome integration loci. We further design the enVision platform for molecular-typing of infections in patient endocervical samples. The technology not only improves the clinical inter-subtype differentiation, but also expands the intra-subtype coverage to identify previously undetectable infections. © 2018, The Author(s). | Source Title: | Nature Communications | URI: | https://scholarbank.nus.edu.sg/handle/10635/174207 | ISSN: | 2041-1723 | DOI: | 10.1038/s41467-018-05733-0 |
Appears in Collections: | Elements Staff Publications |
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