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Nanotechnology-Based Precision Tools for the Detection and Treatment of Cancer

Overview of attention for book
Attention for Chapter 8: Hybrid Nanoparticles for Cancer Imaging and Therapy
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Chapter title
Hybrid Nanoparticles for Cancer Imaging and Therapy
Chapter number 8
Book title
Nanotechnology-Based Precision Tools for the Detection and Treatment of Cancer
Published in
Cancer treatment and research, January 2015
DOI 10.1007/978-3-319-16555-4_8
Pubmed ID
Book ISBNs
978-3-31-916554-7, 978-3-31-916555-4
Authors

Chunbai He, Wenbin Lin, He, Chunbai, Lin, Wenbin

Abstract

Hybrid nanoparticles, composed of both inorganic and organic components, have been exploited as promising platforms for cancer imaging and therapy. This class of nanoparticles can not only retain the beneficial features of both inorganic and organic materials, but also allow systematic fine-tuning of their properties through the judicious combination of functional components. This chapter summarizes recent advances in the design and synthesis of hybrid nanomaterials, with particular emphasis on two main categories of hybrid nanoparticles: Nanoscale metal-organic frameworks (also known as nanoscale coordination polymers) and polysilsesquioxane nanoparticles. Preliminary applications of these hybrid nanoparticles in cancer imaging and therapy are described.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 17%
Student > Ph. D. Student 4 17%
Student > Bachelor 3 13%
Professor > Associate Professor 2 9%
Student > Master 2 9%
Other 3 13%
Unknown 5 22%
Readers by discipline Count As %
Chemistry 5 22%
Medicine and Dentistry 4 17%
Pharmacology, Toxicology and Pharmaceutical Science 2 9%
Agricultural and Biological Sciences 2 9%
Chemical Engineering 1 4%
Other 2 9%
Unknown 7 30%