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The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Progress in Exosome Isolation Techniques

Open access
Li, Pin, Kaslan, Melisa, Lee, Sze Han, Yao, Justin, Gao, Zhiqiang·2017·Theranostics

Abstract

Exosomes are one type of membrane vesicles secreted into extracellular space by most types of cells. In addition to performing many biological functions particularly in cell-cell communication, cumulative evidence has suggested that several biological entities in exosomes like proteins and microRNAs are closely associated with the pathogenesis of most human malignancies and they may serve as invaluable biomarkers for disease diagnosis, prognosis, and therapy. This provides a commanding impetus and growing demands for simple, efficient, and affordable techniques to isolate exosomes. Capitalizing on the physicochemical and biochemical properties of exosomes, a number of techniques have been developed for the isolation of exosomes. This article summarizes the advances in exosome isolation techniques with an emphasis on their isolation mechanism, performance, challenges, and prospects. We hope that this article will provide an overview of exosome isolation techniques, opening up new perspectives towards the development more innovative strategies and devices for more time saving, cost effective, and efficient isolations of exosomes from a wide range of biological matrices.

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Metadata

Authors
Li, P., Kaslan, M., Lee, S. H., Yao, J., & Gao, Z.
Venue
Theranostics
Published
2017
Volume
7
Issue
3
Pages
789-804
ISSN
1838-7640 (ISSN-L)
OpenAlex
W2589694509
Cited by
1,948
References
10
FWCI
53.48
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Progress in Exosome Isolation Techniques. https://app.cochat.ai/papers/10.7150/thno.18133
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Li, P., Kaslan, M., Lee, S. H., Yao, J., & Gao, Z. (2017). Progress in Exosome Isolation Techniques. Theranostics. https://doi.org/10.7150/thno.18133

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.