Abstract

The evolution of extracellular vesicle (EV) research has introduced nanotechnology into biomedical cell communication science while recognizing what is formerly considered cell "dust" as constituting an entirely new universe of cell signaling particles. To display the global EV research landscape, a systematic review of 20364 original research articles selected from all 40684 EV-related records identified in PubMed 2013-2022 is performed. Machine-learning is used 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, an open-source database is built to facilitate further analysis with conventional or AI tools to identify additional causative associations of interest.

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