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Smart Nanomaterials in Cancer Theranostics: Challenges and Opportunities
Open accessAbstract
Cancer is ranked as the second leading cause of death globally. Traditional cancer therapies including chemotherapy are flawed, with off-target and on-target toxicities on the normal cells, requiring newer strategies to improve cell selective targeting. The application of nanomaterial has been extensively studied and explored as chemical biology tools in cancer theranostics. It shows greater applications toward stability, biocompatibility, and increased cell permeability, resulting in precise targeting, and mitigating the shortcomings of traditional cancer therapies. The nanoplatform offers an exciting opportunity to gain targeting strategies and multifunctionality. The advent of nanotechnology, in particular the development of smart nanomaterials, has transformed cancer diagnosis and treatment. The large surface area of nanoparticles is enough to encapsulate many molecules and the ability to functionalize with various biosubstrates such as DNA, RNA, aptamers, and antibodies, which helps in theranostic action. Comparatively, biologically derived nanomaterials perceive advantages over the nanomaterials produced by conventional methods in terms of economy, ease of production, and reduced toxicity. The present review summarizes various techniques in cancer theranostics and emphasizes the applications of smart nanomaterials (such as organic nanoparticles (NPs), inorganic NPs, and carbon-based NPs). We also critically discussed the advantages and challenges impeding their translation in cancer treatment and diagnostic applications. This review concludes that the use of smart nanomaterials could significantly improve cancer theranostics and will facilitate new dimensions for tumor detection and therapy.
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Metadata
- Authors
- Kashyap, B. K., Singh, V. V., Solanki, M. K., Kumar, A., Ruokolainen, J., & Kesari, K. K.
- Venue
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-04-10
- Volume
- 8
- Issue
- 16
- Pages
- 14290-14320
- ISSN
- 2470-1343 (ISSN-L)
- OpenAlex
- W4364381146
- Cited by
- 348
- References
- 319
- FWCI
- 25.45
- 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.
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 ›
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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.
