Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 489

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

Doxorubicin Loaded Magnetic Polymersomes: Theranostic Nanocarriers for MR Imaging and Magneto-Chemotherapy

Open access
Sanson, Charles, Diou, Odile, Thévenot, Julie, Ibarboure, Emmanuel, Soum, Alain, Brûlet, Annie, Miraux, Sylvain, Thiaudière, Eric, Tan, Sisareuth, Brisson, Alain, Dupuis, Vincent, Sandre, Olivier, Lecommandoux, Sébastien·2011·ACS Nano

Abstract

Hydrophobically modified maghemite (γ-Fe(2)O(3)) nanoparticles were encapsulated within the membrane of poly(trimethylene carbonate)-b-poly(l-glutamic acid) (PTMC-b-PGA) block copolymer vesicles using a nanoprecipitation process. This formation method gives simple access to highly magnetic nanoparticles (MNPs) (loaded up to 70 wt %) together with good control over the vesicles size (100-400 nm). The simultaneous loading of maghemite nanoparticles and doxorubicin was also achieved by nanoprecipitation. The deformation of the vesicle membrane under an applied magnetic field has been evidenced by small angle neutron scattering. These superparamagnetic hybrid self-assemblies display enhanced contrast properties that open potential applications for magnetic resonance imaging. They can also be guided in a magnetic field gradient. The feasibility of controlled drug release by radio frequency magnetic hyperthermia was demonstrated in the case of encapsulated doxorubicin molecules, showing the viability of the concept of magneto-chemotherapy. These magnetic polymersomes can be used as efficient multifunctional nanocarriers for combined therapy and imaging.

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/nn102762f

Cites

6 of 6 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 489

Metadata

Authors
Sanson, C., Diou, O., Thévenot, J., Ibarboure, E., Soum, A., Brûlet, A., Miraux, S., Thiaudière, E., Tan, S., Brisson, A., Dupuis, V., Sandre, O., & Lecommandoux, S.
Venue
ACS Nano
Published
2011-01-10
Volume
5
Issue
2
Pages
1122-1140
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2057255825
Cited by
489
References
6
FWCI
19.26
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.