Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 342

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Extracellular Vesicle Quantification and Characterization: Common Methods and Emerging Approaches

Open access
Hartjes, Thomas A., Mytnyk, Serhii, Jenster, Guido W., van Steijn, Volkert, van Royen, Martin E.·2019·Bioengineering

Abstract

Extracellular vesicles (EVs) are a family of small membrane vesicles that carry information about cells by which they are secreted. Growing interest in the role of EVs in intercellular communication, but also in using their diagnostic, prognostic and therapeutic potential in (bio) medical applications, demands for accurate assessment of their biochemical and physical properties. In this review, we provide an overview of available technologies for EV analysis by describing their working principles, assessing their utility in EV research and summarising their potential and limitations. To emphasise the innovations in EV analysis, we also highlight the unique possibilities of emerging technologies with high potential for further development.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/bioengineering6010007

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Cited by

See all 342

Metadata

Authors
Hartjes, T. A., Mytnyk, S., Jenster, G. W., van Steijn, V., & van Royen, M. E.
Venue
Bioengineering
Publisher
MDPI AG
Published
2019-01-16
Volume
6
Issue
1
Pages
7
ISSN
2306-5354 (ISSN-L)
OpenAlex
W2910661483
Cited by
342
References
9
FWCI
14.11
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Extracellular Vesicle Quantification and Characterization: Common Methods and Emerging Approaches. https://app.cochat.ai/papers/10.3390/bioengineering6010007
The paper
Hartjes, T. A., Mytnyk, S., Jenster, G. W., van Steijn, V., & van Royen, M. E. (2019). Extracellular Vesicle Quantification and Characterization: Common Methods and Emerging Approaches. Bioengineering. https://doi.org/10.3390/bioengineering6010007

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.