The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Cancer Treatment through Nanoparticle-Facilitated Fenton Reaction
Abstract
Currently, cancer is the second largest cause of death worldwide and has reached critical levels. In spite of all the efforts, common treatments including chemotherapy, photodynamic therapy, and photothermal therapy suffer from various problems which limit their efficiency and performance. For this reason, different strategies are being explored which improve the efficiency of these traditional therapeutic methods or treat the tumor cells directly. One such strategy utilizing the Fenton reaction has been investigated by many groups for the possible treatment of cancer cells. This approach is based on the knowledge that high levels of hydrogen peroxide exist within cancer cells and can be used to catalyze the Fenton reaction, leading to cancer-killing reactive oxygen species. Analysis of the current literature has shown that, due to the diverse morphologies, different sizes, various chemical properties, and the tunable structure of nanoparticles, nanotechnology offers the most promising method to facilitate the Fenton reaction with cancer therapy. This review aims to highlight the use of the Fenton reaction using different nanoparticles to improve traditional cancer therapies and the emerging Fenton-based therapy, highlighting the obstacles, challenges, and promising developments in each of these areas.
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
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015) · International Journal of Cancer · doi:10.1002/ijc.29210 · Cited by 28,766
Free radicals, metals and antioxidants in oxidative stress-induced cancer
Valko, M., Rhodes, C. J., Moncol, J., Izakovic, M., & Mazur, M. (2006) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2005.12.009 · Cited by 6,680
Nanoparticle therapeutics: an emerging treatment modality for cancer
Davis, M. E., Chen, Z., & Shin, D. M. (2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2614 · Cited by 4,233
ROS as signalling molecules: Mechanisms that generate specificity in ROS homeostasis
D'Autréaux, B., & Toledano, M. B. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2256 · Cited by 3,642
Reactive oxygen species, cellular redox systems, and apoptosis
Circu, M. L., & Aw, T. Y. (2010) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2009.12.022 · Cited by 3,340
Signal transduction by reactive oxygen species
Finkel, T. (2011) · Journal of Cell Biology · doi:10.1083/jcb.201102095 · Cited by 2,322
Biju, V. (2014) · Chem. Soc. Rev. · doi:10.1039/c3cs60273g · Cited by 1,186
Chen, Q., Espey, M. G., Sun, A. Y., Pooput, C., Kirk, K. L., Krishna, M. C., Khosh, D. B., Drisko, J., & Levine, M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804226105 · Cited by 854
Cancer cell killing via ROS: To increase or decrease, that is the question
Wang, J., & Yi, J. (2008) · Cancer Biology & Therapy · doi:10.4161/cbt.7.12.7067 · Cited by 790
Degradation of dyes from aqueous solution by Fenton processes: a review
Nidheesh, P. V., Gandhimathi, R., & Ramesh, S. T. (2013) · Environmental Science and Pollution Research · doi:10.1007/s11356-012-1385-z · Cited by 696
�ber die Katalyse des Hydroperoxydes
Haber, F., & Weiss, J. (1932) · Die Naturwissenschaften · doi:10.1007/bf01504715 · Cited by 513
Cited by
Fu, L., Wan, Y., Qi, C., He, J., Li, C., Yang, C., Xu, H., Lin, J., & Huang, P. (2021) · Advanced Materials · doi:10.1002/adma.202006892 · Cited by 820
Conquering the Hypoxia Limitation for Photodynamic Therapy
Wan, Y., Fu, L., Li, C., Lin, J., & Huang, P. (2021) · Advanced Materials · doi:10.1002/adma.202103978 · Cited by 705
Liu, C., Wang, D., Zhang, S., Cheng, Y., Yang, F., Xing, Y., Xu, T., Dong, H., & Zhang, X. (2019) · ACS Nano · doi:10.1021/acsnano.8b09387 · Cited by 641
Yang, B., Chen, Y., & Shi, J. (2019) · Advanced Materials · doi:10.1002/adma.201901778 · Cited by 601
Zhou, Y., Fan, S., Feng, L., Huang, X., & Chen, X. (2021) · Advanced Materials · doi:10.1002/adma.202104223 · Cited by 463
Metadata
- Authors
- Ranji-Burachaloo, H., Gurr, P. A., Dunstan, D. E., & Qiao, G. G.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-11-20
- Volume
- 12
- Issue
- 12
- Pages
- 11819-11837
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2901748905
- Cited by
- 559
- References
- 11
- FWCI
- 25.20
- Type
- journal article
- Language
- en
- Open access
- closed
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.