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• W4387164515 startingPage "47415" @default.
• W4387164515 abstract "Signal amplification methods based on DNA molecular interactions are promising tools for detecting various biomarkers in low abundance. The entropy-driven circuit (EDC), as an enzyme-free signal amplification method, has been used in detecting and imaging a variety of biomarkers. The localization strategy can effectively increase the local concentration of the DNA reaction modules to improve the signal amplification effect. However, the localization strategy may also amplify the leak reaction of the EDC, and effective signal amplification can be limited by the unclear structure–function relationship. Herein, we utilized locked nucleic acid (LNA) modification to enhance the stability of the localized entropy-driven circuit (LEDC), which suppressed a 94.6% leak signal. The coarse-grained model molecular simulation was used to guide the structure design of the LEDC, and the influence of critical factors such as the localized distance and spacer length was analyzed at the molecular level to obtain the best reaction performance. The sensitivities of miR-21 and miR-141 detected by a simulation-guided optimal LEDC probe were 17.45 and 65 pM, 1345 and 521 times higher than free-EDC, respectively. The LEDC was further employed for the fluorescence imaging of miRNA in cancer cells, showing excellent specificity and sensitivity. This work utilizes LNA and molecular simulations to comprehensively improve the performance of a localized DNA signal amplification circuit, providing an advanced DNA probe design strategy for biosensing and imaging as well as valuable information for the designers of DNA-based probes." @default.
• W4387164515 created "2023-09-30" @default.
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• W4387164515 date "2023-09-29" @default.
• W4387164515 modified "2023-10-14" @default.
• W4387164515 title "Enhanced DNA Entropy-Driven Circuit by Locked Nucleic Acids and Simulation-Guided Localization" @default.
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• W4387164515 doi "https://doi.org/10.1021/acsami.3c11189" @default.
• W4387164515 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37773989" @default.
• W4387164515 hasPublicationYear "2023" @default.
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