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Application of the Micropipette Technique to the Measurement of Cultured Porcine Aortic Endothelial Cell Viscoelastic Properties

Sato, M., Theret, D. P., Wheeler, L. T., Ohshima, N., Nerem, R. M.·1990·Journal of Biomechanical Engineering

Abstract

The viscoelastic deformation of porcine aortic endothelial cells grown under static culture conditions was measured using the micropipette technique. Experiments were conducted both for control cells (mechanically or trypsin detached from the substrate) and for cells in which cytoskeletal elements were disrupted by cytochalasin B or colchicine. The time course of the aspirated length into the pipette was measured after applying a stepwise increase in aspiration pressure. To analyze the data, a standard linear viscoelastic half-space model of the endothelial cell was used. The aspirated length was expressed as an exponential function of time. The actin microfilaments were found to be the major cytoskeletal component determining the viscoelastic response of endothelial cells grown in static culture.

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Metadata

Authors
Sato, M., Theret, D. P., Wheeler, L. T., Ohshima, N., & Nerem, R. M.
Venue
Journal of Biomechanical Engineering
Published
1990-08-01
Volume
112
Issue
3
Pages
263-268
ISSN
  • 0148-0731 (ISSN-L)
  • 1528-8951
OpenAlex
W2055199484
Cited by
373
References
0
FWCI
0.67
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Application of the Micropipette Technique to the Measurement of Cultured Porcine Aortic Endothelial Cell Viscoelastic Properties. https://app.cochat.ai/papers/10.1115/1.2891183
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Sato, M., Theret, D. P., Wheeler, L. T., Ohshima, N., & Nerem, R. M. (1990). Application of the Micropipette Technique to the Measurement of Cultured Porcine Aortic Endothelial Cell Viscoelastic Properties. Journal of Biomechanical Engineering. https://doi.org/10.1115/1.2891183

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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.