The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Nanoparticle Functionalization with Platelet Membrane Enables Multifactored Biological Targeting and Detection of Atherosclerosis
Open accessAbstract
Cardiovascular disease represents one of the major causes of death across the global population. Atherosclerosis, one of its most common drivers, is characterized by the gradual buildup of arterial plaque over time, which can ultimately lead to life-threatening conditions. Given the impact of the disease on public health, there is a great need for effective and noninvasive imaging modalities that can provide valuable information on its biological underpinnings during development. Here, we leverage the role of platelets in atherogenesis to design nanocarriers capable of targeting multiple biological elements relevant to plaque development. Biomimetic nanoparticles are prepared by coating platelet membrane around a synthetic nanoparticulate core, the product of which is capable of interacting with activated endothelium, foam cells, and collagen. The effects are shown to be exclusive to platelet membrane-coated nanoparticles. These biomimetic nanocarriers are not only capable of efficiently localizing to well-developed atherosclerotic plaque, but can also target subclinical regions of arteries susceptible to plaque formation. Using a commonly employed magnetic resonance imaging contrast agent, live detection is demonstrated using an animal model of atherosclerosis. Ultimately, this strategy may be leveraged to better assess the development of atherosclerosis, offering additional information to help clinicians better manage the disease.
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.
Integrity signals
Registry
Cites
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.
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
Macrophages in atherosclerosis: a dynamic balance
Moore, K. J., Sheedy, F. J., & Fisher, E. A. (2013) · Nature reviews. Immunology · doi:10.1038/nri3520 · Cited by 2,559
Mechanisms of plaque formation and rupture
Bentzon, J. F., Otsuka, F., Virmani, R., & Falk, E. (2014) · Circulation Research · doi:10.1161/circresaha.114.302721 · Cited by 2,391
Recent insights into the cellular biology of atherosclerosis
Tabas, I., García-Cardeña, G., & Owens, G. K. (2015) · Journal of Cell Biology · doi:10.1083/jcb.201412052 · Cited by 1,016
Zhu, J.-Y., Zheng, D.-W., Zhang, M.-K., Yu, W.-Y., Qiu, W.-X., Hu, J.-J., Feng, J., & Zhang, X.-Z. (2016) · Nano Letters · doi:10.1021/acs.nanolett.6b02786 · Cited by 498
Baber, U., Mehran, R., Sartori, S., Schoos, M. M., Sillesen, H., Muntendam, P., Garcia, M. J., Gregson, J., Pocock, S., Falk, E., et al. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.01.017 · Cited by 461
Amirbekian, V., Lipinski, M. J., Briley-Saebo, K. C., Amirbekian, S., Aguinaldo, J. G. S., Weinreb, D. B., Vucic, E., Frias, J. C., Hyafil, F., Mani, V., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606281104 · Cited by 356
Cited by
Macrophage-targeted nanomedicine for the diagnosis and treatment of atherosclerosis
Chen, W., Schilperoort, M., Cao, Y., Shi, J., Tabas, I., & Tao, W. (2022) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00629-x · Cited by 518
Platelet membrane-camouflaged magnetic nanoparticles for ferroptosis-enhanced cancer immunotherapy
Jiang, Q., Wang, K., Zhang, X., Ouyang, B., Liu, H., Pang, Z., & Yang, W. (2020) · Small · doi:10.1002/smll.202001704 · Cited by 426
Wang, Y., Zhang, K., Li, T., Maruf, A., Qin, X., Luo, L., Zhong, Y., Qiu, J., McGinty, S., Pontrelli, G., et al. (2021) · Theranostics · doi:10.7150/thno.47841 · Cited by 409
Wang, Y., Li, L., Zhao, W., Dou, Y., An, H., Tao, H., Xu, X., Jia, Y., Lu, S., Zhang, J., et al. (2018) · ACS Nano · doi:10.1021/acsnano.8b02037 · Cited by 361
Biomimetic Nanotechnology toward Personalized Vaccines
Zhou, J., Kroll, A. V., Holay, M., Fang, R. H., & Zhang, L. (2020) · Advanced Materials · doi:10.1002/adma.201901255 · Cited by 328
Self-citation
Metadata
- Authors
- Wei, X., Ying, M., Dehaini, D., Su, Y., Kroll, A. V., Zhou, J., Gao, W., Fang, R. H., Chien, S., & Zhang, L.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-12-12
- Volume
- 12
- Issue
- 1
- Pages
- 109-116
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2771272215
- Cited by
- 329
- References
- 6
- FWCI
- 6.64
- 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.