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

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.

Keywords

aggregate materials / biomarkers / extracellular vesicles / machine learning / pathogens

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