DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics DOI Creative Commons

Hongyang Zhao,

Yumin Yan,

Linghao Zhang

et al.

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Abstract The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for detection. Enzyme‐free signal amplification DNA circuits have facilitated the detection low‐abundance miRNAs. However, these methods may neglect diagnostic value (or weight) different Here, a molecular computing approach with weighted is presented. Polymerase‐mediated strand displacement employed assign weights target miRNAs, reflecting miRNAs’ values, followed by signals using localized catalytic hairpin assembly. This method applied diagnose miRNAs non‐small cell lung (NSCLC). Machine learning used identify NSCLC‐specific and corresponding optimum classification healthy individuals. With output simplified as single channel fluorescence intensity. Cancer tissues ( n = 18) adjacent 10) successfully classified within 2.5 h (sample‐to‐result) an accuracy 92.86%. strategy has potential extend digital multidimensional biomarkers, advancing personalized disease diagnostics in point‐of‐care settings.

Language: Английский

DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics DOI Creative Commons

Hongyang Zhao,

Yumin Yan,

Linghao Zhang

et al.

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Abstract The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for detection. Enzyme‐free signal amplification DNA circuits have facilitated the detection low‐abundance miRNAs. However, these methods may neglect diagnostic value (or weight) different Here, a molecular computing approach with weighted is presented. Polymerase‐mediated strand displacement employed assign weights target miRNAs, reflecting miRNAs’ values, followed by signals using localized catalytic hairpin assembly. This method applied diagnose miRNAs non‐small cell lung (NSCLC). Machine learning used identify NSCLC‐specific and corresponding optimum classification healthy individuals. With output simplified as single channel fluorescence intensity. Cancer tissues ( n = 18) adjacent 10) successfully classified within 2.5 h (sample‐to‐result) an accuracy 92.86%. strategy has potential extend digital multidimensional biomarkers, advancing personalized disease diagnostics in point‐of‐care settings.

Language: Английский

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