Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 456

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Upconversion Nanoparticles for Photodynamic Therapy and Other Cancer Therapeutics

Open access
Wang, Chao, Cheng, Liang, Liu, Zhuang·2013·Theranostics

Abstract

Photodynamic therapy (PDT) is a non-invasive treatment modality for a variety of diseases including cancer. PDT based on upconversion nanoparticles (UCNPs) has received much attention in recent years. Under near-infrared (NIR) light excitation, UCNPs are able to emit high-energy visible light, which can activate surrounding photosensitizer (PS) molecules to produce singlet oxygen and kill cancer cells. Owing to the high tissue penetration ability of NIR light, NIR-excited UCNPs can be used to activate PS molecules in much deeper tissues compared to traditional PDT induced by visible or ultraviolet (UV) light. In addition to the application of UCNPs as an energy donor in PDT, via similar mechanisms, they could also be used for the NIR light-triggered drug release or activation of 'caged' imaging or therapeutic molecules. In this review, we will summarize the latest progresses regarding the applications of UCNPs for photodynamic therapy, NIR triggered drug and gene delivery, as well as several other UCNP-based cancer therapeutic approaches. The future prospects and challenges in this emerging field will be also discussed.

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 · 10.7150/thno.5284

Cites

1 of 1 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

Cited by

See all 456

Metadata

Authors
Wang, C., Cheng, L., & Liu, Z.
Venue
Theranostics
Published
2013
Volume
3
Issue
5
Pages
317-330
ISSN
1838-7640 (ISSN-L)
OpenAlex
W2172145178
Cited by
456
References
1
FWCI
15.84
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: Upconversion Nanoparticles for Photodynamic Therapy and Other Cancer Therapeutics. https://app.cochat.ai/papers/10.7150/thno.5284
The paper
Wang, C., Cheng, L., & Liu, Z. (2013). Upconversion Nanoparticles for Photodynamic Therapy and Other Cancer Therapeutics. Theranostics. https://doi.org/10.7150/thno.5284

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.