The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Directing Nanoparticle Biodistribution through Evasion and Exploitation of Stab2-Dependent Nanoparticle Uptake
Open accessAbstract
Up to 99% of systemically administered nanoparticles are cleared through the liver. Within the liver, most nanoparticles are thought to be sequestered by macrophages (Kupffer cells), although significant nanoparticle interactions with other hepatic cells have also been observed. To achieve effective cell-specific targeting of drugs through nanoparticle encapsulation, improved mechanistic understanding of nanoparticle-liver interactions is required. Here, we show the caudal vein of the embryonic zebrafish ( Danio rerio) can be used as a model for assessing nanoparticle interactions with mammalian liver sinusoidal (or scavenger) endothelial cells (SECs) and macrophages. We observe that anionic nanoparticles are primarily taken up by SECs and identify an essential requirement for the scavenger receptor, stabilin-2 ( stab2) in this process. Importantly, nanoparticle-SEC interactions can be blocked by dextran sulfate, a competitive inhibitor of stab2 and other scavenger receptors. Finally, we exploit nanoparticle-SEC interactions to demonstrate targeted intracellular drug delivery resulting in the selective deletion of a single blood vessel in the zebrafish embryo. Together, we propose stab2 inhibition or targeting as a general approach for modifying nanoparticle-liver interactions of a wide range of nanomedicines.
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
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
Principles of nanoparticle design for overcoming biological barriers to drug delivery
Blanco, E., Shen, H., & Ferrari, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3330 · Cited by 7,002
Liposomal drug delivery systems: From concept to clinical applications
Allen, T. M., & Cullis, P. R. (2013) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2012.09.037 · Cited by 4,839
New-Concept Chemotherapy by Nanoparticles of Biodegradable Polymers: Where Are We Now?
Feng, S.-S. (2006) · Nanomedicine · doi:10.2217/17435889.1.3.297 · Cited by 1,951
Cited by
Twarda-Clapa, A., Olczak, A., Białkowska, A. M., & Koziołkiewicz, M. (2022) · Cells · doi:10.3390/cells11081312 · Cited by 746
Huang, Y., Guo, X., Wu, Y., Chen, X., Feng, L., Xie, N., & Shen, G. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01745-z · Cited by 490
Lipid nanoparticle technology for therapeutic gene regulation in the liver
Witzigmann, D., Kulkarni, J. A., Leung, J., Chen, S., Cullis, P. R., & van der Meel, R. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.06.026 · Cited by 376
Live Tracking of Inter-organ Communication by Endogenous Exosomes In Vivo
Verweij, F. J., Revenu, C., Arras, G., Dingli, F., Loew, D., Pegtel, D. M., Follain, G., Allio, G., Goetz, J. G., Zimmermann, P., et al. (2019) · Developmental Cell · doi:10.1016/j.devcel.2019.01.004 · Cited by 346
Lipid-Based DNA Therapeutics: Hallmarks of Non-Viral Gene Delivery
Buck, J., Grossen, P., Cullis, P. R., Huwyler, J., & Witzigmann, D. (2019) · ACS Nano · doi:10.1021/acsnano.8b07858 · Cited by 312
Self-citation
Metadata
- Authors
- Campbell, F., Bos, F. L., Sieber, S., Arias-Alpizar, G., Koch, B. E., Huwyler, J., Kros, A., & Bussmann, J.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-01-10
- Volume
- 12
- Issue
- 3
- Pages
- 2138-2150
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2783978729
- Cited by
- 233
- References
- 5
- FWCI
- 7.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.