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Cited by 209

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Recent Trends of the Bio-Inspired Nanoparticles in Cancer Theranostics

Open access
Madamsetty, Vijay Sagar, Mukherjee, Anubhab, Mukherjee, Sudip·2019·Frontiers in Pharmacology

Abstract

In recent years, various nanomaterials have emerged as an exciting tool in cancer theranostic applications due to their multifunctional property and intrinsic molecular property aiding effective diagnosis, imaging, and successful therapy. However, chemically synthesized nanoparticles have several issues related to the cost, toxicity and effectiveness. In this context, bio-inspired nanoparticles (NPs) held edges over conventionally synthesized nanoparticles due to their low cost, easy synthesis and low toxicity. In this present review article, a detailed overview of the cancer theranostics applications of various bio-inspired has been provided. This includes the recent examples of liposomes, lipid nanoparticles, protein nanoparticles, inorganic nanoparticles, and viral nanoparticles. Finally, challenges and the future scopes of these NPs in cancer therapy and diagnostics applications are highlighted.

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Metadata

Authors
Madamsetty, V. S., Mukherjee, A., & Mukherjee, S.
Venue
Frontiers in Pharmacology
Published
2019-10-25
Volume
10
Pages
1264-1264
ISSN
1663-9812 (ISSN-L)
OpenAlex
W2981443263
Cited by
209
References
6
FWCI
8.96
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Recent Trends of the Bio-Inspired Nanoparticles in Cancer Theranostics. https://app.cochat.ai/papers/10.3389/fphar.2019.01264
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Madamsetty, V. S., Mukherjee, A., & Mukherjee, S. (2019). Recent Trends of the Bio-Inspired Nanoparticles in Cancer Theranostics. Frontiers in Pharmacology. https://doi.org/10.3389/fphar.2019.01264

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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.