Tissue & Cell ( IF 2.6 ) Pub Date : 2020-09-15 , DOI: 10.1016/j.tice.2020.101442 Benita S Mackay 1 , Matthew Praeger 1 , James A Grant-Jacob 1 , Janos Kanczler 2 , Robert W Eason 1 , Richard O C Oreffo 2 , Ben Mills 1
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.
中文翻译:
通过深度学习模拟成人骨骼干细胞对激光加工地形的反应
成人骨髓基质干细胞对飞秒激光加工产生的表面形貌的反应可以通过深度神经网络进行预测。该网络能够将细胞反应预测到统计显着水平,包括概率P < 0.001 的定位预测,因此可以用作模型来确定细胞对齐所需的最小线间距,对组织结构发育和组织工程。深度神经网络作为模型的应用减少了对细胞行为的地形线索的增强理解所需的实验细胞培养量,并且关键的是,提供了对新型表面结构对组织制造和细胞的影响的快速预测。信令。