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Cited by 267

The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.

Magnetic Nanoparticles for Precision Oncology: Theranostic Magnetic Iron Oxide Nanoparticles for Image-Guided and Targeted Cancer Therapy

Open access
Zhu, Lei, Zhou, Zhiyang, Mao, Hui, Yang, Lily·2017·Nanomedicine

Abstract

Recent advances in the development of magnetic nanoparticles (MNPs) have shown promise in the development of new personalized therapeutic approaches for clinical management of cancer patients. The unique physicochemical properties of MNPs endow them with novel multifunctional capabilities for imaging, drug delivery and therapy, which are referred to as theranostics. To facilitate the translation of those theranostic MNPs into clinical applications, extensive efforts have been made on designing and improving biocompatibility, stability, safety, drug-loading ability, targeted delivery, imaging signal and thermal- or photodynamic response. In this review, we provide an overview of the physicochemical properties, toxicity and theranostic applications of MNPs with a focus on magnetic iron oxide nanoparticles.

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Metadata

Authors
Zhu, L., Zhou, Z., Mao, H., & Yang, L.
Venue
Nanomedicine
Published
2016-11-23
Volume
12
Issue
1
Pages
73-87
ISSN
  • 1743-5889 (ISSN-L)
  • 1748-6963
OpenAlex
W2553579136
Cited by
267
References
112
FWCI
8.36
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Magnetic Nanoparticles for Precision Oncology: Theranostic Magnetic Iron Oxide Nanoparticles for Image-Guided and Targeted Cancer Therapy. https://app.cochat.ai/papers/10.2217/nnm-2016-0316
The paper
Zhu, L., Zhou, Z., Mao, H., & Yang, L. (2017). Magnetic Nanoparticles for Precision Oncology: Theranostic Magnetic Iron Oxide Nanoparticles for Image-Guided and Targeted Cancer Therapy. Nanomedicine. https://doi.org/10.2217/nnm-2016-0316

Data sources

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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.