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Neural stem cells: balancing self-renewal with differentiation
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Stem cells are captivating because they have the potential to make multiple cell types yet maintain their undifferentiated state. Recent studies of Drosophila and mammalian neural stem cells have shed light on how stem cells regulate self-renewal versus differentiation and have revealed the proteins, processes and pathways that all converge to regulate neural progenitor self-renewal. If we can better understand how stem cells balance self-renewal versus differentiation, we will significantly advance our knowledge of embryogenesis, cancer biology and brain evolution, as well as the use of stem cells for therapeutic purposes.
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Metadata
- Authors
- Doe, C. Q.
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2008-05-01
- Volume
- 135
- Issue
- 9
- Pages
- 1575-1587
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- OpenAlex
- W2151777177
- Cited by
- 420
- References
- 161
- FWCI
- 11.53
- Type
- journal article
- Language
- en
- Open access
- bronze
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.