Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.
Deep learning
modeling technique
stem cell behavior
topographical cues
Journal
Tissue & cell
ISSN: 1532-3072
Titre abrégé: Tissue Cell
Pays: Scotland
ID NLM: 0214745
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
17
06
2020
revised:
11
09
2020
accepted:
11
09
2020
pubmed:
26
9
2020
medline:
4
8
2021
entrez:
25
9
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32977273
pii: S0040-8166(20)30376-1
doi: 10.1016/j.tice.2020.101442
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101442Subventions
Organisme : Medical Research Council
ID : MR/R015651/1
Pays : United Kingdom
Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.