The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.
Nanomaterials for Nanotheranostics: Tuning Their Properties According to Disease Needs
Open accessAbstract
Nanotheranostics is one of the biggest scientific breakthroughs in nanomedicine. Most of the currently available diagnosis and therapies are invasive, time-consuming, and associated with severe toxic side effects. Nanotheranostics, on the other hand, has the potential to bridge this gap by harnessing the capabilities of nanotechnology and nanomaterials for combined therapeutics and diagnostics with markedly enhanced efficacy. However, nanomaterial applications in nanotheranostics are still in its infancy. This is due to the fact that each disease has a particular microenvironment with well-defined characteristics, which promotes deeper selection criteria of nanomaterials to meet the disease needs. In this review, we have outlined how nanomaterials are designed and tailored for nanotheranostics of cancer and other diseases such as neurodegenerative, autoimmune (particularly on rheumatoid arthritis), and cardiovascular diseases. The penetrability and retention of a nanomaterial in the biological system, the therapeutic strategy used, and the imaging mode selected are some of the aspects discussed for each disease. The specific properties of the nanomaterials in terms of feasibility, physicochemical challenges, progress in clinical trials, its toxicity, and their future application on translational medicine are addressed. Our review meticulously and critically examines the applications of nanotheranostics with various nanomaterials, including graphene, across several diseases, offering a broader perspective of this emerging field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Items 201–209 of 209
Yang, T., Liu, Y., Zhang, C., Wang, Y., Ma, X., Wang, K., Luo, J. H., Yao, C., Wang, X., & Wang, X. (2016) · Inorganic Chemistry Frontiers · doi:10.1039/c6qi00268d · Cited by 27
Development and evaluation of fluorescent gold nanoparticles
Abdellatif, A. A. H., & Tawfeek, H. M. (2018) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2018.1483400 · Cited by 27
Functionalization of theranostic AGuIX® nanoparticles for PET/MRI/optical imaging
Thakare, V., Tran, V., Natuzzi, M., Thomas, E., Moreau, M., Romieu, A., Collin, B., Courteau, A., Vrigneaud, J., Louis, C., et al. (2019) · RSC Advances · doi:10.1039/c9ra00365g · Cited by 25
Maloney, E. J., DuFort, C. C., Provenzano, P. P., Farr, N., Carlson, M. A., Vohra, R., Park, J., Hingorani, S. R., & Lee, D. (2019) · Cancers · doi:10.3390/cancers11060772 · Cited by 23
Yan, Z., Wang, Q., Liu, X., Peng, J., Li, Q., Wu, M., & Lin, J. (2018) · RSC Advances · doi:10.1039/c8ra01071d · Cited by 17
Nagahama, K., Kumano, T., Oyama, N., & Kawakami, J. (2015) · Biomaterials Science · doi:10.1039/c5bm00212e · Cited by 14
Yao, M., Ma, M., Xu, H., Pan, X., Xu, G., & WU, R. T. (2018) · RSC Advances · doi:10.1039/c7ra12074e · Cited by 13
Atri, M., Zhang, Z., Marques, H. S., et al. (2014) · European Journal of Radiology Open · doi:10.1016/j.ejro.2014.11.002 · Cited by 10
Li, Y., Yang, S., Zheng, J., Zou, Z., Yang, R., & Tan, W. (2018) · Langmuir · doi:10.1021/acs.langmuir.8b02008 · Cited by 10
Cited by
Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic
Weiss, C., Carriere, M., Fusco, L., Capua, I., Regla-Nava, J. A., Pasquali, M., Scott, J. A., Vitale, F., Unal, M. A., Mattevi, C., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c03697 · Cited by 601
Self-citationDomingues, C., Santos, A., Alvarez-Lorenzo, C., Concheiro, A., Jarak, I., Veiga, F., Barbosa, I., Dourado, M., & Figueiras, A. (2022) · ACS Nano · doi:10.1021/acsnano.2c00128 · Cited by 235
Metadata
- Authors
- Wong, X. Y., Sena-Torralba, A., Álvarez-Diduk, R., Muthoosamy, K., & Merkoçi, A.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-02-07
- Volume
- 14
- Issue
- 3
- Pages
- 2585-2627
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3005183463
- Cited by
- 363
- References
- 209
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
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.
