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29. In Situ Array Microextraction and Metabolic Profiling of Small Extracellular Vesicles to Guide and Monitor Maternal Delivery.

Author

Yang, Chenyu, Li, Ning, Chen, Haolin, Zhang, Man, Chen, Yijie, Zhang, Xiangmin, Huang, Shaoqiang, Sun, Nianrong, and Deng, Chunhui

Subjects
MACHINE learning, DISEASE risk factors, EXTRACELLULAR vesicles, DISEASE management, EPIDURAL analgesia, CHILDBIRTH
Abstract

The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large‐scale extraction and high‐throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on‐plate aptamer array (16×24 spots) is developed for continuous scale‐up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5 h) and sample volume by 95% (only 0.125 µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post‐administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post‐administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real‐time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large‐scale clinical samples for medicine innovation and advancement. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Hierarchical Porous Microspheres-Assisted Serum Metabolic Profile for the Early Diagnosis and Surveillance of Postmenopausal Osteoporosis

Author

Qi, Jia, Chen, Gang, Deng, Zhaoqun, Ji, Yiquan, An, Shuai, Chen, Bao, Fan, Guoming, Fang, Caiyun, Yang, Kun, Shi, Fangying, and Deng, Chunhui

Abstract

With the aging global population, the incidence of osteoporosis (OP) is increasing, putting more individuals at risk. Since postmenopausal osteoporosis (PMOP) often remains asymptomatic until a fracture occurs, making the early clinical diagnosis of PMOP particularly challenging. In this work, the AuNPs-anchored hierarchical porous ZrO2microspheres (Au/HPZOMs) is designed to assist laser desorption/ionization mass spectrometry (LDI-MS) for the requirement of serum metabolic fingerprints of PMOP, postmenopausal osteopenia (PMON), and healthy controls (HC) and realize the early diagnosis and surveillance of PMOP. With its large surface area, suitable surface roughness, and enhanced UV absorbance, the LDI efficiency of Au/HPZOMs is significantly enhanced. Combining machine learning, PMOP and non-PMOP (HC and PMON) are clearly distinguished with the area under the receiver operating characteristic curves reaching up to 1.000. Furthermore, seven key m/zfeatures are identified, facilitating the specific detection of PMON and two stages of PMOP. The precision of distinguishing PMON and PMOP at different stages based on these features exceeds 86.5% in both the training and validation sets, aiding in the early diagnosis and monitoring of PMOP. This work sheds light on the metabolic profile for large-scale screening, early detection, and monitoring of PMOP, which will promote the application of fluid metabolism-driven precision medicine into practical clinical use.

Published
2025
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37. Deep Learning-Enabled Rapid Metabolic Decoding of Small Extracellular Vesicles via Dual-Use Mass Spectroscopy Chip Array

Author

Yang, Chenyu, Chen, He, Wu, Yun, Shen, Xiangguo, Liu, Hongchun, Liu, Taotao, Shen, Xizhong, Xue, Ruyi, Sun, Nianrong, and Deng, Chunhui

Abstract

The increasing focus of small extracellular vesicles (sEVs) in liquid biopsy has created a significant demand for streamlined improvements in sEV isolation methods, efficient collection of high-quality sEV data, and powerful rapid analysis of large data sets. Herein, we develop a high-throughput dual-use mass spectroscopic chip array (DUMSCA) for the rapid isolation and detection of plasma sEVs. The DUMSCA realizes more than a 50% increase in speed compared to traditional method and confirms proficiency in robust storage, reuse, high-efficiency desorption/ionization, and metabolite quantification. With the collected metabolic data matrix of sEVs, a deep learning model achieves high-performance diagnosis of Crohn’s disease. Furthermore, discovered biomarkers by feature sparsification and tandem mass spectrometry experiments also exhibited remarkable performance in diagnosis. This work demonstrates the rapidity and validity of DUMSCA for disease diagnosis, enabling the diagnosis of diseases without the necessity for prior knowledge and providing a high-throughput technology for sEV-based liquid biopsy that will empower its vigorous development.

Published
2025
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43. Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype‐Dependent Screening of Early and Small Hepatocellular Carcinoma.

Author

Shi, Fangying, Ning, Liuxin, Sun, Nianrong, Yao, Qunyan, and Deng, Chunhui

Subjects
RECEIVER operating characteristic curves, METABOLOMIC fingerprinting, HEPATOCELLULAR carcinoma, MEDICAL screening, HETEROJUNCTIONS, METALLIC surfaces
Abstract

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well‐defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface‐driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high‐quality urinary metabolic fingerprints using high‐throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high‐risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision‐recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small‐tumor, alpha‐fetoprotein‐negative HCC (93.3%–97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting‐edge matrices‐based metabolic phenotyping in practical clinical diagnostics. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Conclusion

Author

Sun, Nianrong, Deng, Chunhui, Shen, Xizhong, Lockwood, David J., Series Editor, Sun, Nianrong, Deng, Chunhui, and Shen, Xizhong

Published
2021
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