细胞外小泡
分娩
计算机科学
医学
色谱法
产科
怀孕
化学
生物
细胞生物学
遗传学
作者
Chenyu Yang,Ning Li,Haolin Chen,Man Zhang,Yijie Chen,Xiangmin Zhang,Shaoqiang Huang,Nianrong Sun,Chunhui Deng
标识
DOI:10.1002/smtd.202400261
摘要
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.
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