Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning  

作　　者：Zixing Xu Ruiying Chen Chuanming Hao Qionghong Xie Chunhui Deng Nianrong Sun 

机构地区：[1]Department of Gastroenterology and Hepatology,Zhongshan Hospital,and Department of Chemistry,Fudan University,Shanghai 200433,China [2]Division of Nephrology,Huashan Hospital,Fudan University,Shanghai 200040,China [3]School of Chemistry and Chemical Engineering,Nanchang University,Nanchang 330031,China

出　　处：《Chinese Chemical Letters》2024年第5期402-406,共5页中国化学快报（英文版）

基　　金：financially supported by National Key R&D Program of China(No.2018YFA0507501);the National Natural Science Foundation of China(Nos.22074019,21425518,22004017);Shanghai Sailing Program(No.20YF1405300)。

摘　　要：As the most common pathological type of nephrotic syndrome,membranous nephropathy(MN)presents diversity in progression trends,facing severe complications.The precise discrimination of MN from healthy people,other types of nephrotic syndrome or those with therapeutic remission has always been huge challenge in clinics,not to mention comprehensive individualized monitoring relied on minimally invasive molecular detection means.Herein,we construct a functionalized pore architecture to couple with machine learning to aid all-round peptidome enrichment and data profiling from hundreds of human serum samples,and finally establish a set of defined peptide panel consisting of 12 specific feature signals.In addition to the realization of above-mentioned precise discrimination with more than 97%of sensitivity,88%of accuracy and f1 score,the simultaneously comprehensive individualized monitoring for MN can also be achieved,including conventionally screening diagnosis,congeneric distinction and prognostic evaluation.This work greatly advances the development of peptidome data-driven individualized monitoring means for complex diseases and undoubtedly inspire more devotion into molecular detection field.

关 键 词：Membranous nephropathy Serum peptidome Machine learning Disease diagnosis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R692[自动化与计算机技术—控制科学与工程]