Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen-Derived Extracellular Vesicles

IF 13.7 Q1 CHEMISTRY, MULTIDISCIPLINARY
Lihan Lai, Yun Su, Cong Hu, Zehong Peng, Wei Xue, Liang Dong, Tony Y. Hu
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引用次数: 0

Abstract

Extracellular vesicles (EVs) are essential for host–pathogen interactions, mediating processes such as immune modulation and pathogen survival. Pathogen-derived EVs hold significant diagnostic potential because of their unique cargo, offering a wealth of potential biomarkers. In this review, we first discuss the roles of EVs derived from various pathogens in host–pathogen interactions and summarize the latest advancements in pathogen detection based on EVs. Then, we highlight innovative strategies, including novel aggregate materials and machine learning approaches, for enhancing EV detection and analysis. Finally, we discuss challenges in the field and future directions for advancing EV-based diagnostics, aiming to translate these insights into clinical applications.

Abstract Image

整合聚合材料和机器学习算法:推进病原体来源的细胞外囊泡的检测
细胞外囊泡(EVs)在宿主-病原体相互作用、介导免疫调节和病原体存活等过程中至关重要。病原体衍生的电动汽车具有重要的诊断潜力,因为它们具有独特的货物,提供了丰富的潜在生物标志物。本文首先讨论了各种病原体衍生的ev在宿主-病原体相互作用中的作用,并对基于ev的病原体检测的最新进展进行了综述。然后,我们强调了创新策略,包括新型聚合材料和机器学习方法,以增强EV检测和分析。最后,我们讨论了该领域的挑战以及推进基于ev的诊断的未来方向,旨在将这些见解转化为临床应用。
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来源期刊
CiteScore
17.40
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0.00%
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审稿时长
7 weeks
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