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• W4282968520 startingPage "9166" @default.
• W4282968520 abstract "Digital droplet technology has emerged as a powerful new tool for biomarker analysis. Temperature cycling, enzymes, and off-chip processes are, nevertheless, always required. Herein, we constructed a digital droplet auto-catalytic hairpin assembly (ddaCHA) microfluidic system to achieve digital quantification of single-molecule microRNA (miRNA). The designed continuous chip integrates droplet generation, incubation, and fluorescence imaging on the chip, avoiding the requirement for extra droplet re-collection and heating operations. Clearly, the digital readout was obtained by partitioning miRNA into many individual pL-sized small droplets in which the target molecule is either present (positive) or absent (negative). Importantly, the suggested enzyme-free auto-catalytic hairpin assembly (aCHA) in droplets successfully mitigated the effects of the external environment and thermal cycling on droplets, and its reaction rate is significantly superior to that of traditional CHA. We got excellent sensitivity with a linear correlation from 1 pM to 10 nM and a detection limit of 0.34 pM in the fluorescence spectrum section, as well as high selectivity to other miRNAs. Furthermore, the minimum target concentration could be reduced to 10 fM based on the high-throughput tracking computation of fluorescent droplets with a self-developed Python script, and the fluorescence intensity distribution agreed well with the theoretical value, demonstrating that it is feasible to detect miRNA efficiently and accurately, which has great potential applications in clinical diagnostics and biochemical research." @default.
• W4282968520 created "2022-06-17" @default.
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• W4282968520 date "2022-06-16" @default.
• W4282968520 modified "2023-10-18" @default.
• W4282968520 title "One-Step Digital Droplet Auto-Catalytic Nucleic Acid Amplification with High-Throughput Fluorescence Imaging and Droplet Tracking Computation" @default.
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• W4282968520 doi "https://doi.org/10.1021/acs.analchem.2c01754" @default.
• W4282968520 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35708271" @default.
• W4282968520 hasPublicationYear "2022" @default.
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