Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 332

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Ultrasensitive Nanosensors Based on Upconversion Nanoparticles for Selective Hypoxia Imaging in Vivo upon Near-Infrared Excitation

Liu, Jianan, Liu, Yong, Bu, Wenbo, Bu, Jiwen, Sun, Yong, Du, Jiulin, Shi, Jianlin·2014·Journal of the American Chemical Society

Abstract

Hypoxia is a distinct feature of malignant solid tumors, which is a possible causative factor for the serious resistance to chemo- and radiotherapy or the development of invasion and metastasis. The exploration of nanosensors with the capabilities like the accurate diagnosis of hypoxic level will be helpful to estimate the malignant degree of tumor and subsequently implement more effective personalized treatment. Here, we report the design and synthesis of nanosensors that can selectively and reversibly detect the level of hypoxia both in vitro and in vivo. The designed nanosensor is composed of two main moieties: oxygen indicator [Ru(dpp)3](2+)Cl2 for detection of hypoxia and upconversion nanoparticles for offering the excitation light of [Ru(dpp)3](2+)Cl2 by upconversion process under 980 nm exposure. The results show that the nanosensors can reversibly become quenched or luminescent under hyperoxic or hypoxic conditions, respectively. Compared with free [Ru(dpp)3](2+)Cl2, the designed nanosensors exhibit enhanced sensitivity for the detection of oxygen in hypoxic regions. More attractively, the nanosensors can image hypoxic regions with high penetration depth because the absorption and emission wavelength are within the NIR and far-red region, respectively. Most importantly, nanosensors display a high selectivity for detection of relevant oxygen changes in cells and zebrafish.

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 · 10.1021/ja5042989

Cites

5 of 5 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 5 references

Cited by

Metadata

Authors
Liu, J., Liu, Y., Bu, W., Bu, J., Sun, Y., Du, J., & Shi, J.
Venue
Journal of the American Chemical Society
Published
2014-06-27
Volume
136
Issue
27
Pages
9701-9709
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2314305710
Cited by
332
References
5
FWCI
17.55
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

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.