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Cited by 354

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

Nanotechnology and Cancer

Open access
Heath, James R., Davis, Mark E.·2008·Annual Review of Medicine

Abstract

The biological picture of cancer is rapidly advancing from models built from phenomenological descriptions to network models derived from systems biology, which can capture the evolving pathophysiology of the disease at the molecular level. The translation of this (still academic) picture into a clinically relevant framework can be enabling for the war on cancer, but it is a scientific and technological challenge. In this review, we discuss emerging in vitro diagnostic technologies and therapeutic approaches that are being developed to handle this challenge. Our discussion of in vitro diagnostics is guided by the theme of making large numbers of measurements accurately, sensitively, and at very low cost. We discuss diagnostic approaches based on microfluidics and nanotechnology. We then review the current state of the art of nanoparticle-based therapeutics that have reached the clinic. The goal of the presentation is to identify nanotherapeutic strategies that are designed to increase efficacy while simultaneously minimizing the toxic side effects commonly associated with cancer chemotherapies.

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Crossref · 10.1146/annurev.med.59.061506.185523

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Metadata

Authors
Heath, J. R., & Davis, M. E.
Venue
Annual Review of Medicine
Published
2008-02-01
Volume
59
Issue
1
Pages
251-265
ISSN
  • 0066-4219 (ISSN-L)
  • 1545-326X
OpenAlex
W2156429223
Cited by
354
References
4
FWCI
1.59
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Nanotechnology and Cancer. https://app.cochat.ai/papers/10.1146/annurev.med.59.061506.185523
The paper
Heath, J. R., & Davis, M. E. (2008). Nanotechnology and Cancer. Annual Review of Medicine. https://doi.org/10.1146/annurev.med.59.061506.185523

Data sources

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.