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Modeling adult skeletal stem cell response to laser-machined topographies through deep learning

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.
Deep Learning, Modelling techniques, Stem cells, Topographic effects
0040-8166
MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

MacKay, Benita Scout, Praeger, Matthew, Grant-Jacob, James, Kanczler, Janos, Eason, Robert, Oreffo, Richard and Mills, Benjamin (2020) Modeling adult skeletal stem cell response to laser-machined topographies through deep learning. Tissue and Cell, 67, [101442]. (doi:10.1016/j.tice.2020.101442).

Record type: Article

Abstract

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

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Submitted date: 17 June 2020
Accepted/In Press date: 11 September 2020
e-pub ahead of print date: 15 September 2020
Published date: 23 September 2020
Keywords: Deep Learning, Modelling techniques, Stem cells, Topographic effects

Identifiers

Local EPrints ID: 443950
URI: http://eprints.soton.ac.uk/id/eprint/443950
ISSN: 0040-8166
PURE UUID: 514bc39a-6286-470b-bb4f-49b9874a5818
ORCID for Benita Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Janos Kanczler: ORCID iD orcid.org/0000-0001-7249-0414
ORCID for Robert Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Richard Oreffo: ORCID iD orcid.org/0000-0001-5995-6726
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

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Date deposited: 17 Sep 2020 16:41
Last modified: 18 Feb 2021 17:37

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