Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 561

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Microfluidic Electroporation-Facilitated Synthesis of Erythrocyte Membrane-Coated Magnetic Nanoparticles for Enhanced Imaging-Guided Cancer Therapy

Rao, Lang, Cai, Bo, Bu, Lin-Lin, Liao, Qing-Quan, Guo, Shi-Shang, Zhao, Xing-Zhong, Dong, Wen-Fei, Liu, Wei·2017·ACS Nano

Abstract

Biomimetic cell membrane-coated nanoparticles (CM-NPs) with superior biochemical properties have been broadly utilized for various biomedical applications. Currently, researchers primarily focus on using ultrasonic treatment and mechanical extrusion to improve the synthesis of CM-NPs. In this work, we demonstrate that microfluidic electroporation can effectively facilitate the synthesis of CM-NPs. To test it, Fe 3 O 4 magnetic nanoparticles (MNs) and red blood cell membrane-derived vesicles (RBC-vesicles) are infused into a microfluidic device. When the mixture of MNs and RBC-vesicles flow through the electroporation zone, the electric pulses can effectively promote the entry of MNs into RBC-vesicles. After that, the resulting RBC membrane-capped MNs (RBC-MNs) are collected from the chip and injected into experimental animals to test the in vivo performance. Owing to the superior magnetic and photothermal properties of the MN cores and the long blood circulation characteristic of the RBC membrane shells, core–shell RBC-MNs were used for enhanced tumor magnetic resonance imaging (MRI) and photothermal therapy (PTT). Due to the completer cell membrane coating, RBC-MNs prepared by microfluidic electroporation strategy exhibit significantly better treatment effect than the one fabricated by conventional extrusion. We believe the combination of microfluidic electroporation and CM-NPs provides an insight into the synthesis of bioinpired nanoparticles to improve cancer diagnosis and therapy.

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/acsnano.7b00133

Cites

59 of 59 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 561

Metadata

Authors
Rao, L., Cai, B., Bu, L.-L., Liao, Q.-Q., Guo, S.-S., Zhao, X.-Z., Dong, W.-F., & Liu, W.
Venue
ACS Nano
Published
2017-03-13
Volume
11
Issue
4
Pages
3496-3505
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2593852474
Cited by
561
References
59
FWCI
15.36
Type
journal article
Language
en
Open access
closed

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.