Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling

Author:

Li Zhiyu1,Peng Weili2,Zhou Juan3,Shui Shaoxuan1,Liu Yicheng1,Li Tan3,Zhan Xiaohui1,Chen Yuanyuan2,Lan Fang1,Ying Binwu3,Wu Yao1ORCID

Affiliation:

1. National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu 610064 China

2. Machine Intelligence Lab College of Computer Science Sichuan University Chengdu 610064 China

3. Department of Laboratory Medicine West China Hospital Sichuan University Chengdu 610064 China

Abstract

AbstractIt is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix‐assisted laser desorption/ionization mass spectrometry (NMALDI‐MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high‐throughput detection of multiple liver diseases. MXene serves as a multi‐binding site and an electron‐hole source for ionization during NMALDI‐MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally‐driven desorption, and construct a multi‐interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Graduate Student's Research and Innovation Fund of Sichuan University

Publisher

Wiley

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