Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 638

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

Penetration of Lipid Membranes by Gold Nanoparticles: Insights into Cellular Uptake, Cytotoxicity, and Their Relationship

Lin, Jiaqi, Zhang, Hongwu, Chen, Zhen, Zheng, Yonggang·2010·ACS Nano

Abstract

Nanoparticle penetration into cell membranes is an interesting phenomenon that may have crucial implications on the nanoparticles' biomedical applications. In this paper, a coarse-grained model for gold nanoparticles (AuNPs) is developed (verified against experimental data available) to simulate their interactions with model lipid membranes. Simulations reveal that AuNPs with different signs and densities of surface charges spontaneously adhere to the bilayer surface or penetrate into the bilayer interior. The potential of mean force calculations show that the energy gains upon adhesion or penetration is significant. In the case of penetration, it is found that defective areas are induced across the entire surface of the upper leaflet of the bilayer and a hydrophilic pore that transports water molecules was formed with its surrounding lipids highly disordered. Penetration and its concomitant membrane disruptions can be a possible mechanism of the two observed phenomena in experiments: AuNPs bypass endocytosis during their internalization into cells and cytotoxicity of AuNPs. It is also found that both the level of penetration and membrane disruption increase as the charge density of the AuNP increases, but in different manners. The findings suggest a way of controlling the AuNP-cell interactions by manipulating surface charge densities of AuNPs to achieve designated goals in their biomedical applications, such as striking a balance between their cellular uptake and cytotoxicity in order to achieve optimal delivery efficiency as delivery agents.

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/nn1010792

Cites

4 of 4 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

Show all 4 references

Cited by

Metadata

Authors
Lin, J., Zhang, H., Chen, Z., & Zheng, Y.
Venue
ACS Nano
Published
2010-08-27
Volume
4
Issue
9
Pages
5421-5429
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2077142078
Cited by
638
References
4
FWCI
13.94
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.