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基于识别竞争策略的无泵表面增强拉曼散射微流控芯片用于超灵敏高效同时检测肝癌相关微小核糖核酸

Pump-free SERS microfluidic chip based on an identification-competition strategy for ultrasensitive and efficient simultaneous detection of liver cancer-related microRNAs.

作者信息

Zhou Ruoyu, Bai Guangfu, Zhu Dongxu, Xu Qiong, Zhang Xudong, Li Tianran, Qian Yayun, Bu Chiwen

机构信息

Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou 225001, China.

Affiliated Huishan Hospital of Medical College, Yangzhou University, Wuxi Huishan District People's Hospital, Wuxi 214187, Jiangsu Province, China.

出版信息

Biomed Opt Express. 2024 Oct 24;15(11):6469-6485. doi: 10.1364/BOE.542523. eCollection 2024 Nov 1.

DOI:10.1364/BOE.542523
PMID:39553886
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11563321/
Abstract

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a FeO@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of FeO@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of FeO, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.

摘要

在本研究中,我们展示了一种无泵表面增强拉曼散射(SERS)微流控芯片,它能够以超灵敏度和高效率同时检测与肝癌相关的miR-21和miR-155。我们采用了FeO@cDNA-AuNPs@拉曼报告分子@H复合结构和识别竞争策略。当测试液中存在目标微小核糖核酸(miRNAs,即miR-21和miR-155)时,它们会与FeO@cDNA-AuNPs@拉曼报告分子@H的核酸互补链(H)特异性竞争,导致金纳米粒子(AuNPs)竞争性地从FeO表面脱离,从而使SERS信号减弱。因此,这种无泵SERS微流控芯片能够在复杂环境中更快速、准确地检测目标miRNAs。该方法为miRNAs的同时高效检测提供了一种途径,在肝癌早期诊断中具有广阔的应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bc0/11563321/4bfd7715ba9c/boe-15-11-6469-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bc0/11563321/2c2ced2c1717/boe-15-11-6469-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bc0/11563321/4bfd7715ba9c/boe-15-11-6469-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bc0/11563321/2c2ced2c1717/boe-15-11-6469-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bc0/11563321/4bfd7715ba9c/boe-15-11-6469-g002.jpg

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用于基于深度学习的肝癌检测的表面增强拉曼光谱数据的多尺度表示
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