Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 306

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Organic Dye Based Nanoparticles for Cancer Phototheranostics

Cai, Yu, Si, Weili, Huang, Wei, Chen, Peng, Shao, Jinjun, Dong, Xiaochen·2018·Small

Abstract

Phototheranostics, which simultaneously combines photodynamic and/or photothermal therapy with deep‐tissue diagnostic imaging, is a promising strategy for the diagnosis and treatment of cancers. Organic dyes with the merits of strong near‐infrared absorbance, high photo‐to‐radical and/or photothermal conversion efficiency, great biocompatibility, ready chemical structure fine‐tuning capability, and easy metabolism, have been demonstrated as attractive candidates for clinical phototheranostics. These organic dyes can be further designed and fabricated into nanoparticles (NPs) using various strategies. Compared to free molecules, these NPs can be equipped with multiple synergistic functions and show longer lifetime in blood circulation and passive tumor‐targeting property via the enhanced permeability and retention effect. In this article, the recent progress of organic dye‐based NPs for cancer phototheranostic applications is summarized, which extends the anticancer arsenal and holds promise for clinical uses in the near future.

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 · 10.1002/smll.201704247

Cites

8 of 8 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 8 references

Cited by

See all 306

Metadata

Authors
Cai, Y., Si, W., Huang, W., Chen, P., Shao, J., & Dong, X.
Venue
Small
Publisher
Wiley
Published
2018-04-03
Volume
14
Issue
25
Pages
e1704247-e1704247
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2796279597
Cited by
306
References
8
FWCI
12.84
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: Organic Dye Based Nanoparticles for Cancer Phototheranostics. https://app.cochat.ai/papers/10.1002/smll.201704247
The paper
Cai, Y., Si, W., Huang, W., Chen, P., Shao, J., & Dong, X. (2018). Organic Dye Based Nanoparticles for Cancer Phototheranostics. Small. https://doi.org/10.1002/smll.201704247

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.