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Progress and Promise of Nitric Oxide‐Releasing Platforms
Open accessAbstract
Nitric oxide (NO) is a highly potent radical with a wide spectrum of physiological activities. Depending on the concentration, it can enhance endothelial cell proliferation in a growth factor‐free medium, mediate angiogenesis, accelerate wound healing, but may also lead to tumor progression or induce inflammation. Due to its multifaceted role, NO must be administered at a right dose and at the specific site. Many efforts have focused on developing NO‐releasing biomaterials; however, NO short half‐life in human tissues only allows this molecule to diffuse over short distances, and significant challenges remain before the full potential of NO can be realized. Here, an overview of platforms that are engineered to release NO via catalytic or noncatalytic approaches is presented, with a specific emphasis on progress reported in the past five years. A number of NO donors, natural enzymes, and enzyme mimics are highlighted, and recent promising developments of NO‐releasing scaffolds, particles, and films are presented. In particular, key parameters of NO delivery are discussed: 1) NO payload, 2) maximum NO flux, 3) NO release half‐life, 4) time required to reach maximum flux, and 5) duration of NO release. Advantages and drawbacks are reviewed, and possible further developments are suggested.
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Metadata
- Authors
- Yang, T., Zelikin, A. N., & Chandrawati, R.
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2018-04-23
- Volume
- 5
- Issue
- 6
- Pages
- 1701043-1701043
- ISSN
- 2198-3844 (ISSN-L)
- OpenAlex
- W2800356932
- Cited by
- 309
- References
- 129
- FWCI
- 12.92
- Type
- journal article
- Language
- en
- Open access
- gold
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.
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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.