Although three-dimensional (3D) bioprinting technology is rapidly developing, the design strategies for biocompatible 3D-printable bioinks remain a challenge. In this study, we developed a machine learning-based method to design 3D-printable bioink using a model system with naturally derived biomaterials. First, we demonstrated that atelocollagen (AC) has desirable physical properties for printing compared to native collagen (NC). AC gel exhibited weakly elastic and temperature-responsive reversible behavior forming a soft cream-like structure with low yield stress, whereas NC gel showed highly crosslinked and temperature-responsive irreversible behavior resulting in brittleness and high yield stress. Next, we discovered a universal relationship between the mechanical properties of ink and printability that is supported by machine learning: a high elastic modulus improves shape fidelity and extrusion is possible below the critical yield stress; this is supported by machine learn...

中文翻译：

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基于机器学习的3D可打印生物墨水设计策略：弹性模量和屈服应力决定了可打印性。

尽管三维（3D）生物打印技术正在迅速发展，但是生物相容性3D可打印生物墨水的设计策略仍然是一个挑战。在这项研究中，我们开发了一种基于机器学习的方法，该方法使用具有天然来源生物材料的模型系统设计3D可打印生物墨水。首先，我们证明与天然胶原蛋白（NC）相比，胶原蛋白（AC）具有理想的印刷物理性能。AC凝胶表现出较弱的弹性和温度响应性可逆行为，形成具有低屈服应力的软乳脂状结构，而NC凝胶显示出高度交联的和温度响应性不可逆行为，从而导致脆性和高屈服应力。接下来，我们发现了机器学习支持的油墨机械性能和可印刷性之间的通用关系：高弹性模量可提高形状保真度，并在临界屈服应力以下可能挤出；这由机器学习支持...