多模态磁调制QCM用于基于运动的生物分子浓度和基液粘度检测。

IF 6.1 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Talanta Pub Date : 2026-01-01 Epub Date: 2025-06-30 DOI:10.1016/j.talanta.2025.128532
Dongyu Chen, Yumei Wen, Ping Li, Can Zuo, Yao Wang, Zhiyi Wu
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引用次数: 0

摘要

准确和同步的生化参数评估,如生物标志物浓度和体液粘度,对于推进疾病的早期发现和健康管理至关重要。传统的生物分子多参数检测方法通常依赖于多个传感器或分析技术,这引入了传感模式之间的串扰、数据不一致和复杂的校准要求,最终影响了检测精度和适应性。我们提出了一种简化的检测方法,利用单个无涂层石英晶体微天平(QCM)传感器来监测多模态磁场调制下生物分子的动态磁化运动。与传统的依赖于静态质量负载效应的QCM方法不同,该方法使传感器能够捕获编码生物分子浓度和基础液体粘度信息的运动信号。采用反向传播(BP)神经网络对这些运动信号特征与目标生化参数之间的非线性耦合进行建模。提出的方法是验证使用前列腺特异性抗原(PSA)作为生物分子模型分析物。在浓度和粘度同时未知的情况下,盲测的实验结果表明,在0.01至1000 ng/mL的浓度范围内,bp - mm - qcm技术的预测精度为90%,在1至6 cP之间的粘度预测精度为87%。通过将多模态磁调制与基于qcm的运动传感和机器学习相结合,bp - mm - qcm技术为生物分子分析提供了一种通用的高精度解决方案。准确检测生物分子浓度对于早期疾病诊断以及监测疾病进展和治疗反应至关重要。该方法克服了传统QCM方法的局限性,能够在一次分析中实现实时、多参数检测,使其成为疾病诊断和健康监测应用的有前途的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal magnetic modulation QCM for motion-based detection of biomolecule concentration and base liquid viscosity.

Accurate and synchronized assessment of biochemical parameters, such as biomarker concentration and body fluid viscosity, is crucial for advancing early disease detection and health management. Conventional biomolecular multiparameter detection methods often rely on multiple sensors or analytical techniques, which introduce cross-talk between sensing modalities, data inconsistencies, and complex calibration requirements, ultimately compromising detection precision and adaptability. We propose a streamlined detection approach that leverages a single uncoated Quartz Crystal Microbalance (QCM) sensor to monitor the dynamic magnetized motion of biomolecules under multimodal magnetic field modulation. Unlike conventional QCM methods that rely on static mass loading effects, this approach enables the sensor to capture motion signals that encode information about biomolecule concentration and base liquid viscosity. A backpropagation (BP) neural network is employed to model the nonlinear coupling between these motion-derived signal characteristics and the target biochemical parameters. The proposed method is validated using prostate-specific antigen (PSA) as a biomolecular model analyte. Experimental results from blind tests, where both concentration and viscosity were simultaneously unknown, demonstrate a prediction accuracy of 90 % for concentrations ranging from 0.01 to 1000 ng/mL and 87 % for viscosities between 1 and 6 cP. By integrating multimodal magnetic modulation with QCM-based motion sensing and machine learning, the BP-MMM-QCM technique provides a versatile and high-precision solution for biomolecule analysis. Accurate detection of biomolecule concentrations is essential for early disease diagnosis as well as monitoring disease progression and therapeutic responses. This approach overcomes the limitations of conventional QCM methods and enables real-time, multi-parameter detection in a single assay, making it a promising tool for disease diagnostics and health monitoring applications.

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来源期刊
Talanta
Talanta 化学-分析化学
CiteScore
12.30
自引率
4.90%
发文量
861
审稿时长
29 days
期刊介绍: Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.
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