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Cited by 281

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

Tailoring nanoparticle designs to target cancer based on tumor pathophysiology

Open access
Sykes, Edward A., Dai, Qin, Sarsons, Christopher D., Chen, Juan, Rocheleau, Jonathan V., Hwang, David M., Zheng, Gang, Cramb, David T., Rinker, Kristina D., Chan, Warren C. W.·2016·Proceedings of the National Academy of Sciences

Abstract

Significance: Nanotechnology is a promising approach for improving cancer diagnosis and treatment with reduced side effects. A key question that has emerged is: What is the ideal nanoparticle size, shape, or surface chemistry for targeting tumors? Here, we show that tumor pathophysiology and volume can significantly impact nanoparticle targeting. This finding presents a paradigm shift in nanomedicine away from identifying and using a universal nanoparticle design for cancer detection and treatment. Rather, our results suggest that future clinicians will be capable of tailoring nanoparticle designs according to the patient's tumor characteristics. This concept of “personalized nanomedicine” was tested for detection of prostate tumors and was successfully demonstrated to improve nanoparticle targeting by over 50%.

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Metadata

Authors
Sykes, E. A., Dai, Q., Sarsons, C. D., Chen, J., Rocheleau, J. V., Hwang, D. M., Zheng, G., Cramb, D. T., Rinker, K. D., & Chan, W. C. W.
Venue
Proceedings of the National Academy of Sciences
Published
2016-02-16
Volume
113
Issue
9
Pages
E1142-51
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2278304348
Cited by
281
References
4
FWCI
13.16
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Tailoring nanoparticle designs to target cancer based on tumor pathophysiology. https://app.cochat.ai/papers/10.1073/pnas.1521265113
The paper
Sykes, E. A., Dai, Q., Sarsons, C. D., Chen, J., Rocheleau, J. V., Hwang, D. M., Zheng, G., Cramb, D. T., Rinker, K. D., & Chan, W. C. W. (2016). Tailoring nanoparticle designs to target cancer based on tumor pathophysiology. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1521265113

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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.