The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Current understanding of biological identity at the nanoscale and future prospects
Open accessAbstract
Nanoscale objects are processed by living organisms using highly evolved and sophisticated endogenous cellular networks, specifically designed to manage objects of this size. While these processes potentially allow nanostructures unique access to and control over key biological machineries, they are also highly protected by cell or host defence mechanisms at all levels. A thorough understanding of bionanoscale recognition events, including the molecules involved in the cell recognition machinery, the nature of information transferred during recognition processes and the coupled downstream cellular processing, would allow us to achieve a qualitatively novel form of biological control and advanced therapeutics. Here we discuss evolving fundamental microscopic and mechanistic understanding of biological nanoscale recognition. We consider the interface between a nanostructure and a target cell membrane, outlining the categories of nanostructure properties that are recognized, and the associated nanoscale signal transduction and cellular programming mechanisms that constitute biological recognition. This Perspective reflects current understanding and future prospects of nanoscale biological recognition at the bio–nano interface and the opportunity that such insights provide for advancing nanoscale therapies. The authors describe the key mechanisms and propose a framework of principles that could guide future research efforts in the area.
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Drug delivery systems for RNA therapeutics
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Metadata
- Authors
- Dawson, K. A., & Yan, Y.
- Venue
- Nature Nanotechnology
- Published
- 2021-02-17
- Volume
- 16
- Issue
- 3
- Pages
- 229-242
- ISSN
- 1748-3387 (ISSN-L)
- 1748-3395
- OpenAlex
- W3133400804
- Cited by
- 204
- References
- 8
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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