The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Sheddable Ternary Nanoparticles for Tumor Acidity-Targeted siRNA Delivery
Abstract
Drug delivery systems for cancer therapy usually need to be sterically stabilized by a poly(ethylene glycol) (PEG) layer during blood circulation to minimize nonspecific interactions with serum components. However, PEGylation significantly reduces cellular uptake of the delivery systems after they accumulate at the tumor site, which markedly impairs the in vivo antitumor efficiency. Here, we develop a ternary small interfering RNA (siRNA) delivery system with tumor acidity-activated sheddable PEG layer to overcome the challenge. The sheddable nanoparticle is fabricated by introducing a tumor acidity-responsive PEGylated anionic polymer to the surface of positively charged polycation/siRNA complexes via electrostatic interaction. We show clear evidence that introducing the PEGylated anionic polymer to the surface of a nanoparticle markedly reduces its nonspecific interactions with protein. We further demonstrate that the nanoparticle is capable of deshielding the PEG layer at the slightly acidic tumor extracellular microenvironment to facilitate the delivery of siRNA to the tumor cells after accumulation at the tumor site. Accordingly, this promotes the RNA-interfering efficiencies and enhances the inhibition of tumor growth. Such delivery system with the ability to deshield the PEG layer at the target tissues has remarkable potential in cancer 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.
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
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
Nanoparticle therapeutics: an emerging treatment modality for cancer
Davis, M. E., Chen, Z., & Shin, D. M. (2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2614 · Cited by 4,233
The effect of particle design on cellular internalization pathways
Gratton, S. E. A., Ropp, P. A., Pohlhaus, P. D., Luft, J. C., Madden, V. J., Napier, M. E., & DeSimone, J. M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801763105 · Cited by 2,918
Ogris, M., Brunner, S., Schüller, S., Kircheis, R., & Wagner, E. (1999) · Gene Therapy · doi:10.1038/sj.gt.3300900 · Cited by 1,178
Layer-by-Layer Nanoparticles with a pH-Sheddable Layer for in Vivo Targeting of Tumor Hypoxia
Poon, Z., Chang, D., Zhao, X., & Hammond, P. T. (2011) · ACS Nano · doi:10.1021/nn200876f · Cited by 343
Nonviral methods for siRNA delivery
Gao, K., & Huang, L. (2009) · Molecular Pharmaceutics · doi:10.1021/mp800134q · Cited by 244
Cited by
The Smart Drug Delivery System and Its Clinical Potential
Liu, D., Yang, F., Xiong, F., & Gu, N. (2016) · Theranostics · doi:10.7150/thno.14858 · Cited by 1,066
Li, H.-J., Du, J.-Z., Liu, J., Du, X.-J., Shen, S., Zhu, Y.-H., Wang, X., Ye, X., Nie, S., & Wang, J. (2016) · ACS Nano · doi:10.1021/acsnano.6b02326 · Cited by 537
Self-citationZhang, J., Yuan, Z.-F., Wang, Y., Chen, W.-H., Luo, G.-F., Cheng, S.-X., Zhuo, R.-X., & Zhang, X.-Z. (2013) · Journal of the American Chemical Society · doi:10.1021/ja312004m · Cited by 517
Polymeric Nanostructures for Imaging and Therapy
Elsabahy, M., Heo, G. S., Lim, S.-M., Sun, G., & Wooley, K. L. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00135 · Cited by 508
The acidic tumor microenvironment: a target for smart cancer nano-theranostics
Feng, L., Dong, Z., Tao, D., Zhang, Y., & Liu, Z. (2018) · National Science Review · doi:10.1093/nsr/nwx062 · Cited by 426
Metadata
- Authors
- Yang, X.-Z., Du, J.-Z., Dou, S., Mao, C.-Q., Long, H.-Y., & Wang, J.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-12-09
- Volume
- 6
- Issue
- 1
- Pages
- 771-781
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W1983521413
- Cited by
- 284
- References
- 6
- FWCI
- 8.82
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sheddable Ternary Nanoparticles for Tumor Acidity-Targeted siRNA Delivery. https://app.cochat.ai/papers/10.1021/nn204240b
- The paper
- Yang, X.-Z., Du, J.-Z., Dou, S., Mao, C.-Q., Long, H.-Y., & Wang, J. (2012). Sheddable Ternary Nanoparticles for Tumor Acidity-Targeted siRNA Delivery. ACS Nano. https://doi.org/10.1021/nn204240b
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.