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Cited by 516

The DOI matches its Crossref record; 165 of the 165 references it cites were found in a registry or an index.

Topography, Cell Response, and Nerve Regeneration

Open access
Hoffman-Kim, Diane, Mitchel, Jennifer A., Bellamkonda, Ravi V.·2010·Annual Review of Biomedical Engineering

Abstract

In the body, cells encounter a complex milieu of signals, including topographical cues, in the form of the physical features of their surrounding environment. Imposed topography can affect cells on surfaces by promoting adhesion, spreading, alignment, morphological changes, and changes in gene expression. Neural response to topography is complex, and it depends on the dimensions and shapes of physical features. Looking toward repair of nerve injuries, strategies are being explored to engineer guidance conduits with precise surface topographies. How neurons and other cell types sense and interpret topography remains to be fully elucidated. Studies reviewed here include those of topography on cellular organization and function as well as potential cellular mechanisms of response.

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Crossref · 10.1146/annurev-bioeng-070909-105351

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Metadata

Authors
Hoffman-Kim, D., Mitchel, J. A., & Bellamkonda, R. V.
Venue
Annual Review of Biomedical Engineering
Published
2010-07-01
Volume
12
Issue
1
Pages
203-231
ISSN
  • 1523-9829 (ISSN-L)
  • 1545-4274
OpenAlex
W2139997147
Cited by
516
References
165
FWCI
1.64
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Topography, Cell Response, and Nerve Regeneration. https://app.cochat.ai/papers/10.1146/annurev-bioeng-070909-105351
The paper
Hoffman-Kim, D., Mitchel, J. A., & Bellamkonda, R. V. (2010). Topography, Cell Response, and Nerve Regeneration. Annual Review of Biomedical Engineering. https://doi.org/10.1146/annurev-bioeng-070909-105351

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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.