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: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Aggregate |
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| Online Access: | https://doi.org/10.1002/agt2.70018 |
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| _version_ | 1849733583080521728 |
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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 |
| id | doaj-art-3dd5baf3f61e4ad796f6d75dda957d8c |
| institution | DOAJ |
| issn | 2692-4560 |
| language | English |
| 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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