Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 413

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Erythrocyte Membrane-Enveloped Polymeric Nanoparticles as Nanovaccine for Induction of Antitumor Immunity against Melanoma

Guo, Yuanyuan, Wang, Dong, Song, Qingle, Wu, Tingting, Zhuang, Xiangting, Bao, Yuling, Kong, Miao, Qi, Yan, Tan, Songwei, Zhang, Zhiping·2015·ACS Nano

Abstract

Cancer immunotherapy is mainly focused on manipulating patient's own immune system to recognize and destroy cancer cells. Vaccine formulations based on nanotechnology have been developed to target delivery antigens to antigen presenting cells (APCs), especially dendritic cells (DCs) for efficiently induction of antigen-specific T cells response. To enhance DC targeting and antigen presenting efficiency, we developed erythrocyte membrane-enveloped poly(d,l-lactide-co-glycolide) (PLGA) nanoparticles for antigenic peptide (hgp10025-33) and toll-like receptor 4 agonist, monophosphoryl lipid (MPLA). A Mannose-inserted membrane structure was constructed to actively target APCs in the lymphatic organ, and redox-sensitive peptide-conjugated PLGA nanoparticles were fabricated which prone to cleave in the intracellular milieu. The nanovaccine demonstrated the retained protein content in erythrocyte and enhanced in vitro cell uptake. An antigen-depot effect was observed in the administration site with promoted retention in draining lymph nodes. Compared with other formulations after intradermal injection, the nanovaccine prolonged tumor-occurring time, inhibited tumor growth, and suppressed tumor metastasis in prophylactic, therapeutic, and metastatic melanoma models, respectively. Additionally, we revealed that nanovaccine effectively enhanced IFN-γ secretion and CD8(+) T cell response. Taken together, these results demonstrated the great potential in applying an erythrocyte membrane-enveloped polymeric nanoplatform for an antigen delivery system in cancer immunotherapy.

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.5b01042

Cites

6 of 6 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 413

Metadata

Authors
Guo, Y., Wang, D., Song, Q., Wu, T., Zhuang, X., Bao, Y., Kong, M., Qi, Y., Tan, S., & Zhang, Z.
Venue
ACS Nano
Published
2015-07-14
Volume
9
Issue
7
Pages
6918-6933
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2335924114
Cited by
413
References
6
FWCI
11.82
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

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.