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Cited by 597

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Passive Tumor Targeting of Renal-Clearable Luminescent Gold Nanoparticles: Long Tumor Retention and Fast Normal Tissue Clearance

Open access
Liu, Jinbin, Yu, Mengxiao, Zhou, Chen, Yang, Shengyang, Ning, Xuhui, Zheng, Jie·2013·Journal of the American Chemical Society

Abstract

Glutathione-coated luminescent gold nanoparticles (GS-AuNPs) with diameters of ∼2.5 nm behave like small dye molecules (IRDye 800CW) in physiological stability and renal clearance but exhibit a much longer tumor retention time and faster normal tissue clearance, indicating that the well-known enhanced permeability and retention effect, a unique strength of conventional NPs in tumor targeting, still exists in such small NPs. These merits enable the AuNPs to detect tumor more rapidly than the dye molecules without severe accumulation in reticuloendothelial system organs, making them very promising for cancer diagnosis and therapy.

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Crossref · 10.1021/ja401612x

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Metadata

Authors
Liu, J., Yu, M., Zhou, C., Yang, S., Ning, X., & Zheng, J.
Venue
Journal of the American Chemical Society
Published
2013-03-22
Volume
135
Issue
13
Pages
4978-4981
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2007355570
Cited by
597
References
4
FWCI
27.46
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Passive Tumor Targeting of Renal-Clearable Luminescent Gold Nanoparticles: Long Tumor Retention and Fast Normal Tissue Clearance. https://app.cochat.ai/papers/10.1021/ja401612x
The paper
Liu, J., Yu, M., Zhou, C., Yang, S., Ning, X., & Zheng, J. (2013). Passive Tumor Targeting of Renal-Clearable Luminescent Gold Nanoparticles: Long Tumor Retention and Fast Normal Tissue Clearance. Journal of the American Chemical Society. https://doi.org/10.1021/ja401612x

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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.