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Saara Laitinen

Researcher at Finnish Red Cross

Publications -  57
Citations -  13360

Saara Laitinen is an academic researcher from Finnish Red Cross. The author has contributed to research in topics: Mesenchymal stem cell & Stem cell. The author has an hindex of 25, co-authored 55 publications receiving 8720 citations.

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Minimal information for studies of extracellular vesicles 2018 (MISEV2018) : a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

Clotilde Théry, +417 more
TL;DR: The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities, and a checklist is provided with summaries of key points.
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Biological properties of extracellular vesicles and their physiological functions

María Yáñez-Mó, +72 more
TL;DR: A comprehensive overview of the current understanding of the physiological roles of EVs is provided, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia.
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Applying extracellular vesicles based therapeutics in clinical trials - an ISEV position paper

Thomas Lener, +57 more
TL;DR: In this paper, the authors summarize recent developments and the current knowledge of extracellular vesicles (EVs) and discuss safety and regulatory requirements that must be considered for pharmaceutical manufacturing and clinical application.
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Gene expression in human NAFLD.

TL;DR: The data show that multiple changes in gene expression characterize simple steatosis, and genes involved in hepatic glucose and lipid metabolism, insulin signaling, inflammation, coagulation, and cell adhesion are found to be significantly associated with liver fat content.
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Isolation and characterization of platelet-derived extracellular vesicles.

TL;DR: An EV isolation protocol is optimized and the quantity and protein content of EVs induced by different agonists are compared and activation-dependent variations render the use of protein content in sample normalization invalid.