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Advances in Extracellular Vesicle Research Over the Past Decade: Source and Isolation Method are Connected with Cargo and Function.
Poupardin, Rodolphe; Wolf, Martin; Maeding, Nicole; Paniushkina, Liliia; Geissler, Sven; Bergese, Paolo; Witwer, Kenneth W; Schallmoser, Katharina; Fuhrmann, Gregor; Strunk, Dirk.
Afiliación
  • Poupardin R; Cell Therapy Institute, Paracelsus Medical University, Salzburg, 5020, Austria.
  • Wolf M; Cell Therapy Institute, Paracelsus Medical University, Salzburg, 5020, Austria.
  • Maeding N; Cell Therapy Institute, Paracelsus Medical University, Salzburg, 5020, Austria.
  • Paniushkina L; Cell Therapy Institute, Paracelsus Medical University, Salzburg, 5020, Austria.
  • Geissler S; Departments of Molecular and Comparative Pathobiology and Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
  • Bergese P; BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité - Universitätsmedizin Berlin, 10178, Berlin, Germany.
  • Witwer KW; Department of Molecular and Translational Medicine, University of Brescia, Brescia, 25121, Italy.
  • Schallmoser K; INSTM - National Interuniversity Consortium of Materials Science and Technology, Firenze, 50121, Italy.
  • Fuhrmann G; National Center for Gene Therapy and Drugs based on RNA Technology - CN3, Padova, 35122, Italy.
  • Strunk D; Departments of Molecular and Comparative Pathobiology and Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
Adv Healthc Mater ; 13(19): e2303941, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38270559
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 20 364 original research articles selected from all 40 684 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vesículas Extracelulares / Aprendizaje Automático Límite: Animals / Humans Idioma: En Revista: Adv Healthc Mater Año: 2024 Tipo del documento: Article País de afiliación: Austria

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vesículas Extracelulares / Aprendizaje Automático Límite: Animals / Humans Idioma: En Revista: Adv Healthc Mater Año: 2024 Tipo del documento: Article País de afiliación: Austria