Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
Lihan Lai,
Yun Su,
Cong Hu,
Zehong Peng,
Wei Xue,
Liang Dong,
Tony Y. Hu
Affiliations
Lihan Lai
Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
Yun Su
Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai China
Cong Hu
Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
Zehong Peng
Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
Wei Xue
Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
Liang Dong
Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
Tony Y. Hu
Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine Tulane University New Orleans Louisiana USA
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.
