The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Cellular mechanisms and treatments for chemobrain: insight from aging and neurodegenerative diseases
Open accessAbstract
Chemotherapy is a life-saving treatment for cancer patients, but also causes long-term cognitive impairment, or "chemobrain", in survivors. However, several challenges, including imprecise diagnosis criteria, multiple confounding factors, and unclear and heterogeneous molecular mechanisms, impede effective investigation of preventions and treatments for chemobrain. With the rapid increase in the number of cancer survivors, chemobrain is an urgent but unmet clinical need. Here, we leverage the extensive knowledge in various fields of neuroscience to gain insights into the mechanisms for chemobrain. We start by outlining why the post-mitotic adult brain is particularly vulnerable to chemotherapy. Next, through drawing comparisons with normal aging, Alzheimer's disease, and traumatic brain injury, we identify universal cellular mechanisms that may underlie the cognitive deficits in chemobrain. We further identify existing neurological drugs targeting these cellular mechanisms that can be repurposed as treatments for chemobrain, some of which were already shown to be effective in animal models. Finally, we briefly describe future steps to further advance our understanding of chemobrain and facilitate the development of effective preventions and treatments.
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Cites
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Cited by
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Metadata
- Authors
- Nguyen, L. D., & Ehrlich, B. E.
- Venue
- EMBO Molecular Medicine
- Published
- 2020-04-29
- Volume
- 12
- Issue
- 6
- Pages
- e12075-e12075
- ISSN
- 1757-4676 (ISSN-L)
- 1757-4684
- OpenAlex
- W3019023480
- Cited by
- 175
- References
- 24
- FWCI
- 10.84
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
