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固体相提取与自动离心微流体相结合 SERS:改善人类血清中治疗药物的量化

Gohar Soufi¹, Isidro Badillo-Ramírez¹, Laura Serioli¹

¹Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kongens Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark.

Biosensors & bioelectronics

|September 4, 2024

概括

这项研究引入了一种自动化的微流体表面增强拉曼光谱法 (SERS) 方法,用于精确的,无标签的治疗药物的定量化,如人体血清中的甲醇和拉莫特里金.

科学领域:

分析化学分析化学
生物医学工程生物医学工程
频谱学是一种光谱学.

背景情况:

表面增强拉曼光谱 (SERS) 提供了强大的分析能力,但由于技术挑战,在现实世界的医疗应用中面临局限性.
自动化SERS测试对于提高可重复性和在临床环境中实现无标签量化至关重要.

研究的目的:

开发一个自动化的微流体SERS平台,用于人类血清中的治疗药物的可重复的,无标签的量化.
将小型化固相提取 (μ-SPE) 与离心机微流体盘SERS (CD-SERS) 平台集成.
实施一个机器学习模型,用于强大的数据分析和药物定量.

主要方法:

开发了一种带有集成SERS基板 (CD-SERS) 的离心微流体盘,用于控制样品湿和SERS映射.
一种小型化固相提取 (μ-SPE) 方法与CD-SERS平台相结合.
部分最小平方回归 (PLSR) 用于分析和量化甲状腺素 (MTX) 和拉姆素 (LTG) 的数据.

主要成果:

与CD-SERS相结合的μ-SPE方法 (μ-SPE与CD-SERS) 与其他样本准备方法相比,显著改善了信号噪声比.
在PLSR模型中,MTX在2.90μM和LTG在10.76μM的检测极限得到了实现.
对HPLC和免疫试验的验证显示了该方法对MTX的有效性,在患者样本中,LTG的恢复率 (80%) 是令人满意的.

结论:

开发的μ-SPE到CD-SERS方法为在临床环境中的自动化,无标签的SERS测定提供了一个有希望的策略.
这种自动化方法提高了可复制性,并使治疗药物的精确量化在复杂的生物矩阵,如人体血清.
微流体学,SERS和机器学习的整合为个性化医学的先进诊断工具铺平了道路.

关键词:

离心式微流体学离心式微流体学机器学习机器学习这就是 SERS SERS.固体相提取方法固体相提取方法

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