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Tissue engineering offers immense potential for addressing the unmet needs in repairing tissue damage and organ failure. Vascularization, the development of intricate blood vessel networks, is crucial for the survival and functions of engineered tissues. Nevertheless, the persistent challenge of ensuring an ample nutrient supply within implanted tissues remains, primarily due to the inadequate formation of blood vessels. This issue underscores the vital role of the human vascular system in sustaining cellular functions, facilitating nutrient exchange, and removing metabolic waste products. In response to this challenge, new approaches have been explored. Microfluidic devices, emulating natural blood vessels, serve as valuable tools for investigating angiogenesis and allowing the formation of microvascular networks. In parallel, bioprinting technologies enable precise placement of cells and biomaterials, culminating in vascular structures that closely resemble the native vessels. To this end, the synergy of microfluidics and bioprinting has further opened up exciting possibilities in vascularization, encompassing innovations such as microfluidic bioprinting. These advancements hold great promise in regenerative medicine, facilitating the creation of functional tissues for applications ranging from transplantation to disease modeling and drug testing. This review explores the potentially transformative impact of microfluidic and bioprinting technologies on vascularization strategies within the scope of tissue engineering. 

The development of an efficient and powerful machine learning (ML) model for materials property prediction (MPP) remains an important challenge in materials science. While various techniques have been proposed to extract physicochemical features in MPP, graph neural networks (GNN) have also shown very strong capability in capturing effective features for high-performance MPP. Nevertheless, current GNN models do not effectively differentiate the contributions from different atoms. In this paper we develop a novel graph neural network model called GATGNN for predicting properties of inorganic materials. GATGNN is characterized by its composition of augmented graph-attention layers (AGAT) and a global attention layer. The application of AGAT layers and global attention layers respectively learn the local relationship among neighboring atoms and overall contribution of the atoms to the material's property; together making our framework achieve considerably better prediction performance on various tested properties. Through extensive experiments, we show that our method is able to outperform existing state-of-the-art GNN models while it can also provide a measurable insight into the correlation between the atoms and their material property. Our code can found on – https://github.com/superlouis/GATGNN.