表面增强拉曼光谱（SERS）是医学领域潜力巨大的分析方法。基于特定形态和/或化学修饰的SERS基底的设计，使得能够以接近单分子检测的精确度识别特定分析物的存在。然而，实际样品的SERS分析因存在大量可能会遮蔽目标分析物信号并导致无法实际确定其存在的“次要”分子而变得相当复杂。本研究使用先进的SERS方法检测与癌症相关的miRNA-21在作为分子模型背景的血浆中的存在。该方法基于仿生式等离子体活性SERS基底、其调控的表面化学性质以及运用人工机器学习的先进SERS数据分析相结合。首先，利用蝴蝶翅膀作为起始模板，创建了仿生式SERS基底。将基底覆盖薄Au层，并通过共价方式接枝疏水化学基团以引入超疏水和亲水性质。通过最小化分析物滴液与基底之间的接触面积，利用表面超疏水性，并通过翻转基底上的液滴蒸发进一步增强，实现了分析物在基底上的自浓缩。由于存在癌症miRNA和血浆背景，测量到的SERS光谱呈现出干扰峰的复杂结构。因此，通过特别训练的机器学习模型对其进行了解释。结果，可靠而可重复地在人类血浆背景下下检测到低于飞排模达到femtomolar级别(miRNAs，即10-16 M)的miRNAs。版权所有 © 2023 Elsevier B.V. 保留所有权利。

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of "minor" molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10-16 M) on the background of human blood plasma becomes possible.Copyright © 2023 Elsevier B.V. All rights reserved.