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Cited by 197

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Differential uptake of nanoparticles by endothelial cells through polyelectrolytes with affinity for caveolae

Open access
Voigt, Julia, Christensen, Jon, Shastri, V. Prasad·2014·Proceedings of the National Academy of Sciences

Abstract

Significance: Nanoparticles (NPs) can serve as containers for the targeting of therapeutics to tumors. Tumors comprise many cell types including endothelial cells that form the blood vessels. Developing new strategies to target information preferentially to endothelial cells can have major implications in the development of targeted therapeutics. We have discovered that charged polymers containing aromatic sulfonate have pronounced affinity for caveolae, which are highly expressed by endothelial cells. By engineering the surface of lipid NPs to bear sulfonate-containing polymers, lipid NPs that are preferentially taken up by endothelial cells have been demonstrated.

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Crossref · 10.1073/pnas.1322356111

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Metadata

Authors
Voigt, J., Christensen, J., & Shastri, V. P.
Venue
Proceedings of the National Academy of Sciences
Published
2014-02-10
Volume
111
Issue
8
Pages
2942-2947
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1986032553
Cited by
197
References
2
FWCI
6.41
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Differential uptake of nanoparticles by endothelial cells through polyelectrolytes with affinity for caveolae. https://app.cochat.ai/papers/10.1073/pnas.1322356111
The paper
Voigt, J., Christensen, J., & Shastri, V. P. (2014). Differential uptake of nanoparticles by endothelial cells through polyelectrolytes with affinity for caveolae. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1322356111

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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.