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Cited by 193

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

Chemically engineered persistent luminescence nanoprobes for bioimaging

Open access
Lécuyer, Thomas, Teston, Eliott, Ramirez-Garcia, Gonzalo, Maldiney, Thomas, Viana, Bruno, Seguin, Johanne, Mignet, Nathalie, Scherman, Daniel, Richard, Cyrille·2016·Theranostics

Abstract

Imaging nanoprobes are a group of nanosized agents developed for providing improved contrast for bioimaging. Among various imaging probes, optical sensors capable of following biological events or progresses at the cellular and molecular levels are actually actively developed for early detection, accurate diagnosis, and monitoring of the treatment of diseases. The optical activities of nanoprobes can be tuned on demand by chemists by engineering their composition, size and surface nature. This review will focus on researches devoted to the conception of nanoprobes with particular optical properties, called persistent luminescence, and their use as new powerful bioimaging agents in preclinical assays.

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Metadata

Authors
Lécuyer, T., Teston, E., Ramirez-Garcia, G., Maldiney, T., Viana, B., Seguin, J., Mignet, N., Scherman, D., & Richard, C.
Venue
Theranostics
Published
2016
Volume
6
Issue
13
Pages
2488-2523
ISSN
1838-7640 (ISSN-L)
OpenAlex
W2555483779
Cited by
193
References
5
FWCI
12.08
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Chemically engineered persistent luminescence nanoprobes for bioimaging. https://app.cochat.ai/papers/10.7150/thno.16589
The paper
Lécuyer, T., Teston, E., Ramirez-Garcia, G., Maldiney, T., Viana, B., Seguin, J., Mignet, N., Scherman, D., & Richard, C. (2016). Chemically engineered persistent luminescence nanoprobes for bioimaging. Theranostics. https://doi.org/10.7150/thno.16589

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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.