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...

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Main Authors: Lihan Lai, Yun Su, Cong Hu, Zehong Peng, Wei Xue, Liang Dong, Tony Y. Hu
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Aggregate
Subjects:
Online Access:https://doi.org/10.1002/agt2.70018
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author Lihan Lai
Yun Su
Cong Hu
Zehong Peng
Wei Xue
Liang Dong
Tony Y. Hu
author_facet Lihan Lai
Yun Su
Cong Hu
Zehong Peng
Wei Xue
Liang Dong
Tony Y. Hu
author_sort Lihan Lai
collection DOAJ
description 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.
format Article
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institution DOAJ
issn 2692-4560
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publishDate 2025-05-01
publisher Wiley
record_format Article
series Aggregate
spelling doaj-art-3dd5baf3f61e4ad796f6d75dda957d8c2025-08-20T03:08:00ZengWileyAggregate2692-45602025-05-0165n/an/a10.1002/agt2.70018Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular VesiclesLihan Lai0Yun Su1Cong Hu2Zehong Peng3Wei Xue4Liang Dong5Tony Y. Hu6Department of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai ChinaDepartment of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaDepartment of Urology Renji Hospital, Shanghai Jiao Tong University School of Medicine Shanghai ChinaCenter for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine Tulane University New Orleans Louisiana USAABSTRACT 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.https://doi.org/10.1002/agt2.70018aggregate materialsbiomarkersextracellular vesiclesmachine learningpathogens
spellingShingle Lihan Lai
Yun Su
Cong Hu
Zehong Peng
Wei Xue
Liang Dong
Tony Y. Hu
Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
Aggregate
aggregate materials
biomarkers
extracellular vesicles
machine learning
pathogens
title Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
title_full Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
title_fullStr Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
title_full_unstemmed Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
title_short Integrating Aggregate Materials and Machine Learning Algorithms: Advancing Detection of Pathogen‐Derived Extracellular Vesicles
title_sort integrating aggregate materials and machine learning algorithms advancing detection of pathogen derived extracellular vesicles
topic aggregate materials
biomarkers
extracellular vesicles
machine learning
pathogens
url https://doi.org/10.1002/agt2.70018
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AT yunsu integratingaggregatematerialsandmachinelearningalgorithmsadvancingdetectionofpathogenderivedextracellularvesicles
AT conghu integratingaggregatematerialsandmachinelearningalgorithmsadvancingdetectionofpathogenderivedextracellularvesicles
AT zehongpeng integratingaggregatematerialsandmachinelearningalgorithmsadvancingdetectionofpathogenderivedextracellularvesicles
AT weixue integratingaggregatematerialsandmachinelearningalgorithmsadvancingdetectionofpathogenderivedextracellularvesicles
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