Abstract

The evolution of extracellular vesicle (EV) research has introduced nanotechnology into biomedical cell communication science while recognizing what was formerly considered cell "dust" as constituting an entirely new universe of cell signaling particles. To display the global EV research landscape, we performed a systematic review of 20,364 original research articles selected from all 40,684 EV-related records identified in PubMed 2013 - 2022. We used machine-learning to categorize the high-dimensional data and further dissected significant associations between EV source, isolation method, cargo and function. Unexpected correlations between these four categories indicate prevalent experimental strategies based on cargo connectivity with function of interest being associated with certain EV sources or isolation strategies. Conceptually relevant association of size-based EV isolation with protein cargo and uptake function will guide strategic conclusions enhancing future EV research and product development. Based on this study, we built an open-source database to facilitate further analysis with conventional or AI tools to identify additional causative associations of interest. This article is protected by copyright. All rights reserved.

OriginalspracheEnglisch
Seiten (von - bis)e2303941
Seitenumfang14
FachzeitschriftADVANCED HEALTHCARE MATERIALS
Jahrgang13
Ausgabenummer19
Frühes Online-DatumApr. 2024
DOIs
PublikationsstatusVeröffentlicht - Juli 2024

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