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Cited by 1,675

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

New Technologies for Analysis of Extracellular Vesicles

Open access
Shao, Huilin, Im, Hyungsoon, Castro, Cesar M., Breakefield, Xandra, Weissleder, Ralph, Lee, Hakho·2018·Chemical Reviews

Abstract

Extracellular vesicles (EVs) are diverse, nanoscale membrane vesicles actively released by cells. Similar-sized vesicles can be further classified (e.g., exosomes, microvesicles) based on their biogenesis, size, and biophysical properties. Although initially thought to be cellular debris, and thus under-appreciated, EVs are now increasingly recognized as important vehicles of intercellular communication and circulating biomarkers for disease diagnoses and prognosis. Despite their clinical potential, the lack of sensitive preparatory and analytical technologies for EVs poses a barrier to clinical translation. New analytical platforms including molecular ones are thus actively being developed to address these challenges. Recent advances in the field are expected to have far-reaching impact in both basic and translational studies. This article aims to present a comprehensive and critical overview of emerging analytical technologies for EV detection and their clinical applications.

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Crossref · 10.1021/acs.chemrev.7b00534

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Metadata

Authors
Shao, H., Im, H., Castro, C. M., Breakefield, X., Weissleder, R., & Lee, H.
Venue
Chemical Reviews
Published
2018-01-31
Volume
118
Issue
4
Pages
1917-1950
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
OpenAlex
W2786202610
Cited by
1,675
References
33
FWCI
10.36
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: New Technologies for Analysis of Extracellular Vesicles. https://app.cochat.ai/papers/10.1021/acs.chemrev.7b00534
The paper
Shao, H., Im, H., Castro, C. M., Breakefield, X., Weissleder, R., & Lee, H. (2018). New Technologies for Analysis of Extracellular Vesicles. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.7b00534

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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.