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Li Z, Peng W, Zhou J, Shui S, Liu Y, Li T, Zhan X, Chen Y, Lan F, Ying B, Wu Y. Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling. Adv Mater 2024:e2312799. [PMID: 38263756 DOI: 10.1002/adma.202312799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/11/2024] [Indexed: 01/25/2024]
Abstract
It 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.
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Affiliation(s)
- Zhiyu Li
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Weili Peng
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Shaoxuan Shui
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yicheng Liu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Tan Li
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Xiaohui Zhan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yuanyuan Chen
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Fang Lan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Yao Wu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
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