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使用机器学习辅助表面增强拉曼光谱学诊断和分类移植排斥,使用单滴血清

Sanghwa Lee¹, Jin-Myung Kim², Kwanhee Lee³

¹Department of Convergence Medicine, Asan Institute for Life Science, Asan Medical Center, Seoul, 05505, South Korea.

Biosensors & bioelectronics

|June 26, 2024

概括

使用纳米感应芯片,拉曼光谱和AI的新型非侵入性诊断系统,可以从血清样本中准确检测移植排斥 (ABMR和TCMR).

科学领域:

生物医学工程生物医学工程
纳米技术纳米技术
频谱学是一种光谱学.

背景情况:

移植排斥,包括抗体介导 (ABMR) 和T细胞介导 (TCMR) 类型,需要及早检测以防止移植损伤.
目前的诊断方法依赖于侵入性活检,突出显示了对非侵入性替代品的需求.
由于ABMR和TCMR具有不同的管理协议,因此需要精确的区分.

研究的目的:

开发和验证一种新的融合系统,用于移植拒绝的非侵入性诊断.
使用血清样本区分无重大异常,ABMR和TCMR.
评估人工智能算法结合纳米传感和拉曼光谱的诊断准确性.

主要方法:

采用了一个融合系统,集成了一个纳米感应芯片 (Au-ZnO纳米棒),拉曼光谱和AI.
分析了移植患者的血清样本 (没有异常,ABMR,TCMR).
组织活检和班夫得分分析用于验证.
表面增强拉曼散射 (SERS) 信号来自5μL血清样本.

主要成果:

机器学习算法实现了高诊断准确率: 93.53% (PC-LDA) 和98.82% (PC-PLS-DA). 机器学习算法实现了高诊断准确率: 93.53% (PC-LDA) 和98.82% (PC-PLS-DA). 机器学习算法实现了高诊断准确率: 93.53% (PC-LDA) 和98.82% (PC-PLS-DA).
促进准确性的关键生物标志物包括原蛋白 (脏损伤),肌素和氨基酸衍生物 (脏功能).
该系统的优势在于它能够分析广泛的生物标志物.

结论:

开发的纳米传感芯片,拉曼光谱和人工智能系统为诊断移植拒绝提供了高度准确,非侵入性的方法.
这项技术有可能取代用于监测移植受体的侵入性活检程序.
可实现ABMR和TCMR的早期和精确诊断,为及时和适当的治疗铺平道路.

关键词:

抗体介导排斥 (ABMR) 是一种移植脏的拒绝脏的拒绝机器学习算法机器学习算法纳米大小的生物标志物表面增强的拉曼光谱学由T细胞介导的排斥 (TCMR)

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