Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 582

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Surface-Engineered Magnetic Nanoparticle Platforms for Cancer Imaging and Therapy

Open access
Xie, Jin, Liu, Gang, Eden, Henry S., Ai, Hua, Chen, Xiaoyuan·2011·Accounts of Chemical Research

Abstract

Enormous efforts have been made toward the translation of nanotechnology into medical practice, including cancer management. Generally the applications have fallen into two categories: diagnosis and therapy. Because the targets are often the same, the development of separate approaches can miss opportunities to improve efficiency and effectiveness. The unique physical properties of nanomaterials enable them to serve as the basis for superior imaging probes to locate and report cancerous lesions and as vehicles to deliver therapeutics preferentially to those lesions. These technologies for probes and vehicles have converged in the current efforts to develop nanotheranostics, nanoplatforms with both imaging and therapeutic functionalities. These new multimodal platforms are highly versatile and valuable components of the emerging trend toward personalized medicine, which emphasizes tailoring treatments to the biology of individual patients to optimize outcomes. The close coupling of imaging and treatment within a theranostic agent and the data about the evolving course of an illness that these agents provide can facilitate informed decisions about modifications to treatment. Magnetic nanoparticles, especially superparamagnetic iron oxide nanoparticles (IONPs), have long been studied as contrast agents for magnetic resonance imaging (MRI). Owing to recent progress in synthesis and surface modification, many new avenues have opened for this class of biomaterials. Such nanoparticles are not merely tiny magnetic crystals, but potential platforms with large surface-to-volume ratios. By taking advantage of the well-developed surface chemistry of these materials, researchers can load a wide range of functionalities, such as targeting, imaging and therapeutic features, onto their surfaces. This versatility makes magnetic nanoparticles excellent scaffolds for the construction of theranostic agents, and many efforts have been launched toward this goal. In this Account, we introduce the surface engineering techniques that we and others have developed, with an emphasis on how these techniques affect the role of nanoparticles as imaging or therapeutic agents. We and others have developed a set of chemical methods to prepare magnetic nanoparticles that possess accurate sizes, shapes, compositions, magnetizations, relaxivities, and surface charges. These features, in turn, can be harnessed to adjust the toxicity and stability of the nanoparticles and, further, to load functionalities, via various mechanisms, onto the nanoparticle surfaces.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/ar200044b

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 582

Metadata

Authors
Xie, J., Liu, G., Eden, H. S., Ai, H., & Chen, X.
Venue
Accounts of Chemical Research
Published
2011-05-06
Volume
44
Issue
10
Pages
883-892
ISSN
  • 0001-4842 (ISSN-L)
  • 1520-4898
OpenAlex
W2035957302
Cited by
582
References
3
FWCI
33.91
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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.