Abstract：
The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 μL of serum obtained at the same time was added onto the Au-ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.
摘要：
减少肾移植排斥反应的追求强调了发展非侵入性，精确的诊断技术。这些技术旨在检测抗体介导的排斥反应（ABMR）和T细胞介导的排斥反应（TCMR）。无症状，有潜在肾损害的风险。管理由ABMR和TCMR引起的拒绝的协议不同，和诊断传统上依赖于侵入性活检程序。因此,使用纳米传感芯片的会聚系统，拉曼光谱,并引入AI技术，以便于使用从没有重大异常的患者获得的血清样本进行诊断，ABMR,和肾移植后的TCMR。组织活检和Banff评分分析在组间进行验证，将同时获得的5μL血清添加到基于Au-ZnO纳米棒的表面增强拉曼散射传感芯片上,获得拉曼光谱信号。机器学习算法对主成分-线性判别分析和主成分-偏最小二乘判别分析的准确率分别为93.53%和98.82%,分别。胶原蛋白（肾损伤的指示），肌酐,和氨基酸衍生的信号（肾功能标志物）有助于这种准确性；然而，这种高准确性主要是由于该系统能够分析广谱的各种生物标志物.
