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机器学习辅助的纳米孔传感解决方案用于确定矩阵金属蛋白酶

Zhen Guan¹, Yunze Sun¹, Yingdi He¹

¹School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.

Biosensors & bioelectronics

|July 11, 2025

概括

一种新的纳米孔方法准确地测量了用于糖尿病病 (DKD) 诊断的矩阵金属蛋白酶2 (MMP2). 这种简单的方法有助于比传统方法更快地检测DKD并发症.

科学领域:

生物医学工程生物医学工程
分析化学分析化学
分子诊断学分子诊断学

背景情况:

糖尿病病 (DKD) 是一种严重的糖尿病并发症,通常被诊断为白蛋白尿或降低的eGFR.
矩阵金属蛋白酶2 (MMP2) 是DKD的潜在生物标志物,但其测量是复杂和耗时的.
开发一种更简单,更可靠的MMP2检测方法对于及时诊断DKD至关重要.

研究的目的:

开发一种易于使用的基于纳米孔的方法,用于单分子水平的定量MMP2测量.
使用机器学习来准确分类MMP2活动.
评估该方法在DKD诊断方面的潜力,包括其在模拟尿液中的性能.

主要方法:

使用α-hemolysin纳米孔用于单分子检测MMP2.
采用机器学习算法来分类由MMP2消化的基质和产品.
验证了MMP2检测的定量范围 (50-400 ng/ml),并测试了各种化学品的抑制作用.
在模拟尿样中评估MMP2测量.

主要成果:

在使用机器学习对基质和产品进行分类时实现了100%的准确性.
确定了MMP2的定量检测范围,从50到400 ng/ml.
在模拟尿液中证明了MMP2测量的可行性,显示了临床应用的潜力.

结论:

一个新的,用户友好的纳米孔传感平台,能够精确,单分子量化MMP2活动.
机器学习的整合显著提高了MMP2检测的准确性.
该方法为DKD诊断和监测提供了一个有希望的,高效的工具,在尿液等复杂的生物矩阵中具有潜在的应用.

关键词:

机器学习是机器学习.矩阵金属蛋白酶2的产生 α-血溶素的使用情况

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