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Cited by 303

The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.

Nanoparticle‐mediated double‐stranded RNA delivery system: A promising approach for sustainable pest management

Open access
Yan, Shuo, Ren, Bin‐Yuan, Shen, Jie·2021·Insect Science

Abstract

RNA interference (RNAi) targeting lethal genes in insects has great potential for sustainable crop protection. Compared with traditional double‐stranded (ds)RNA delivery systems, nanoparticles such as chitosan, liposomes, and cationic dendrimers offer advantages in delivering dsRNA/small interfering (si)RNA to improve RNAi efficiency, thus promoting the development and practice of RNAi‐based pest management strategies. Here, we illustrate the limitations of traditional dsRNA delivery systems, reveal the mechanism of nanoparticle‐mediated RNAi, summarize the recent progress and successful applications of nanoparticle‐mediated RNAi in pest management, and finally address the prospects of nanoparticle‐based RNA pesticides.

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Crossref · 10.1111/1744-7917.12822

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Metadata

Authors
Yan, S., Ren, B., & Shen, J.
Venue
Insect Science
Publisher
Wiley
Published
2020-07-20
Volume
28
Issue
1
Pages
21-34
ISSN
  • 1005-295X (ISSN-L)
  • 1672-9609
  • 1744-7917
OpenAlex
W3029369162
Cited by
303
References
111
FWCI
8.09
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Nanoparticle‐mediated double‐stranded RNA delivery system: A promising approach for sustainable pest management. https://app.cochat.ai/papers/10.1111/1744-7917.12822
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Yan, S., Ren, B., & Shen, J. (2021). Nanoparticle‐mediated double‐stranded RNA delivery system: A promising approach for sustainable pest management. Insect Science. https://doi.org/10.1111/1744-7917.12822

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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.