The DOI matches its Crossref record; 204 of the 204 references it cites were found in a registry or an index.
Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic
Open accessAbstract
The COVID-19 outbreak has fueled a global demand for effective diagnosis and treatment as well as mitigation of the spread of infection, all through large-scale approaches such as specific alternative antiviral methods and classical disinfection protocols.Based on an abundance of engineered materials identifiable by their useful physicochemical properties through versatile chemical functionalization, nanotechnology offers a number of approaches to cope with this emergency.Here, through a multidisciplinary Perspective encompassing diverse fields such as virology, biology, medicine, engineering, chemistry, materials science, and computational science, we outline how nanotechnology-based strategies can support the fight against COVID-19, as well as infectious diseases in general, including future pandemics.Considering what we know so far about the life cycle of the virus, we envision key steps where nanotechnology could counter the disease.First, nanoparticles (NPs) can offer alternative methods to classical disinfection protocols used in healthcare settings, thanks to their intrinsic antipathogenic properties and/or their ability to inactivate viruses, bacteria, fungi, or yeasts either photothermally or via photocatalysis-induced reactive oxygen species (ROS) generation.Nanotechnology tools to inactivate SARS-CoV-2 in patients could also be explored.In this case, nanomaterials could be used to deliver drugs to the pulmonary system to inhibit interaction between angiotensin-converting enzyme 2 (ACE2) receptors and viral S protein.Moreover, the concept of "nanoimmunity by design" can help us to design materials for immune modulation, either stimulating or suppressing the immune response, which would find applications in the context of vaccine development for SARS-CoV-2 or in counteracting the cytokine storm, respectively.In addition to disease prevention and therapeutic potential, nanotechnology has important roles in diagnostics, with potential to support the development of simple, fast, and cost-effective nanotechnology-based assays to monitor the presence of SARS-CoV-2 and related biomarkers.In summary, nanotechnology is critical in counteracting COVID-19 and will be vital when preparing for future pandemics.
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Items 201–204 of 204
Next Generation Sequencing in Pharmacogenomics
Demkow, U. A. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801739-5.00011-8 · Cited by 15
Cheng, H.-W., Wang, H., Wong, T.-Y., Yeh, A. H.-W., Chen, Y.-C., Liu, D.-Z., & Liang, P. (2018) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2018.02.012 · Cited by 14
(2017) · CRC Press eBooks · doi:10.4324/9781315373669 · Cited by 10
Roy, A., & Chaguturu, R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801814-9.00003-9 · Cited by 8
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Nervous system involvement after infection with COVID-19 and other coronaviruses
Wu, Y., Xu, X., Chen, Z., Duan, J., Hashimoto, K., Yang, L., Liu, C., & Yang, C. (2020) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2020.03.031 · Cited by 2,046
Aerosol filtration efficiency of common fabrics used in respiratory cloth masks
Konda, A., Prakash, A., Moss, G. A., Schmoldt, M., Grant, G. D., & Guha, S. (2020) · ACS Nano · doi:10.1021/acsnano.0c03252 · Cited by 934
Seah, I., & Agrawal, R. (2020) · Ocular Immunology and Inflammation · doi:10.1080/09273948.2020.1738501 · Cited by 698
Pokrajac, L., Abbas, A., Chrzanowski, W., Dias, G. M., Eggleton, B. J., Maguire, S., Maine, E., Malloy, T., Nathwani, J., Nazar, L., et al. (2021) · ACS Nano · doi:10.1021/acsnano.1c10919 · Cited by 365
Pereira, P. F. S., de Paula e Silva, A. C. A., da Silva Pimentel, B. N. A., Pinatti, I. M., Simões, A. Z., Vergani, C. E., Barreto-Vieira, D. F., da Silva, M. A. N., Miranda, M. D., Monteiro, M. E. S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-11902-5 · Cited by 20
Metadata
- Authors
- Weiss, C., Carriere, M., Fusco, L., Capua, I., Regla-Nava, J. A., Pasquali, M., Scott, J. A., Vitale, F., Unal, M. A., Mattevi, C., Bedognetti, D., Merkoçi, A., Tasciotti, E., Yilmazer, A., Gogotsi, Y., Stellacci, F., & Delogu, L. G.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-06-10
- Volume
- 14
- Issue
- 6
- Pages
- 6383-6406
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3033019032
- Cited by
- 601
- References
- 204
- FWCI
- 14.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
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