论文标题
通过深度学习对成人骨骼干细胞对激光器加工的形态的反应进行建模
Modeling adult skeletal stem cell response to laser-machined topographies through deep learning
论文作者
论文摘要
成年人类骨髓间基质干细胞对通过飞秒激光加工产生的表面地形的反应可以通过深度神经网络预测。该网络能够预测细胞对统计学上显着水平的响应，包括具有P <0.001的定位预测，因此可以用作模型来确定细胞比对所需的最小线分离，这对组织结构的发展和组织工程有影响。深层神经网络作为模型的应用减少了对细胞行为增强对地形线索的理解所需的实验细胞培养量，并批判性地提供了对新表面结构对组织制造和细胞信号传导的影响的快速预测。
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.
