Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 253

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Self-luminescing BRET-FRET near-infrared dots for in vivo lymph-node mapping and tumour imaging

Open access
Xiong, Liqin, Shuhendler, Adam J., Rao, Jianghong·2012·Nature Communications

Abstract

Strong autofluorescence from living tissues, and the scattering and absorption of short-wavelength light in living tissues, significantly reduce sensitivity of in vivo fluorescence imaging. These issues can be tackled by using imaging probes that emit in the near-infrared wavelength range. Here we describe self-luminescing near-infrared-emitting nanoparticles employing an energy transfer relay that integrates bioluminescence resonance energy transfer and fluorescence resonance energy transfer, enabling in vivo near-infrared imaging without external light excitation. Nanoparticles were 30–40 nm in diameter, contained no toxic metals, exhibited long circulation time and high serum stability, and produced strong near-infrared emission. Using these nanoparticles, we successfully imaged lymphatic networks and vasculature of xenografted tumours in living mice. The self-luminescing feature provided excellent tumour-to-background ratio (>100) for imaging very small tumours (2–3 mm in diameter). Our results demonstrate that these new nanoparticles are well suited to in vivo imaging applications such as lymph-node mapping and cancer imaging. Fluorescence imaging in vivo is hampered by autofluorescence and the scattering and absorption of short-wavelength light. To address these problems, Xiong et al. produce self-luminescing nanoparticles that enable in vivonear-infrared imaging without external light excitation.

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 · 10.1038/ncomms2197

Cites

33 of 33 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

Cited by

See all 253

Metadata

Authors
Xiong, L., Shuhendler, A. J., & Rao, J.
Venue
Nature Communications
Published
2012-11-13
Volume
3
Issue
1
Pages
1193-1193
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2046487009
Cited by
253
References
33
FWCI
8.69
Type
journal article
Language
en
Open access
bronze

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.