Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 207

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Recent Advances in Biosensors for Detecting Cancer-Derived Exosomes

Cheng, Nan, Du, Dan, Wang, Xinxian, Liu, Dong, Xu, Wentao, Luo, Yunbo, Lin, Yuehe·2019·Trends in Biotechnology

Abstract

Early detection and effective treatments are two of the greatest challenges in the fight against cancer. Cancer-derived exosomes are attractive biomarkers for the early diagnosis and therapeutic response evaluation of cancer. Here, we review recent advances in biosensors for the detection of cancer-derived exosomes. We discuss the potential exosomal biomarkers of various cancers, which can be applied as indicative targets in the design of biosensors. We further describe the fabrication of exosome detection biosensors with respect to biological recognition strategies and signal transduction techniques, which involve integrated scientific and technological aspects of analytical chemistry and nanotechnology. Furthermore, future research directions and challenges in using cancer-derived exosomes for point-of-care (POC) testing are presented.

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 · 10.1016/j.tibtech.2019.04.008

Cites

2 of 2 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 2 references

Cited by

See all 207

Metadata

Authors
Cheng, N., Du, D., Wang, X., Liu, D., Xu, W., Luo, Y., & Lin, Y.
Venue
Trends in Biotechnology
Publisher
Elsevier BV
Published
2019-11
Volume
37
Issue
11
Pages
1236-1254
ISSN
0167-7799 (ISSN-L)
OpenAlex
W2946257502
Cited by
207
References
2
FWCI
2.04
Type
journal article
Language
en
Open access
closed

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: Recent Advances in Biosensors for Detecting Cancer-Derived Exosomes. https://app.cochat.ai/papers/10.1016/j.tibtech.2019.04.008
The paper
Cheng, N., Du, D., Wang, X., Liu, D., Xu, W., Luo, Y., & Lin, Y. (2019). Recent Advances in Biosensors for Detecting Cancer-Derived Exosomes. Trends in Biotechnology. https://doi.org/10.1016/j.tibtech.2019.04.008

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.