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Three-dimensional (3D) printing provides a new approach of fabricating implantable products because it permits a flexible manner to extrude complex and customized shapes of the tissue scaffolds. Compared with other printable biomaterials, the polyurethane elastomer has several merits, including excellent mechanical properties and good biocompatibility. However, some intrinsic behavior, especially its high melting point and slow rate of degradation, hampered its application in 3D printed tissue engineering. Herein, we developed a 3D printable amino acid modified biodegradable waterborne polyurethane (WBPU) using a water-based green chemistry process. The flexibility of this material endows better compliance with tissue during implantation and prevents high modulus transplants from scratching surrounding tissues. The histocompatibility experiments show that the WBPU induces no apparent acute rejection or inflammation in vivo. We successfully fabricated a highly flexible WBPU scaffold by deposition 3D printing technology at a low temperature (50°C ~ 70 °C), and the printed products could support the adhesion and proliferation of chondrocytes and fibroblasts. The printed blocks possessed controllable degradability due to the different amounts of hydrophilic chain extender and did not cause accumulation of acidic products. In addition, we demonstrated that our WBPU is highly applicable for implantable tissue engineering because there is no cytotoxicity during its degradation. Taken together, we envision that this printable WBPU can be used as an alternative biomaterial for tissue engineering with low temperature printing, biodegradability, and compatibility.

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.

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Porous titanium alloys have been prepared by gelcasting in this study. The elastic solid green body was first polymerized and then vacuum sintered to porous titanium alloys with low contamination by controlling sintering conditions. The microstructure and the total porosity of the vacuum sintered porous Ti-Co and Ti-Mo alloys were analyzed by using scanning electron microscopy and x-ray diffraction. Moreover, compression and bending tests were conducted to investigate their mechanical properties. The results show that open and closed three-dimensional pore morphologies and total porosity ranging from 38.34% to 58.32% can be achieved. In contrast to porous Ti by gelcasting, the compression and bending strengths of porous titanium alloys were significantly increased by adding Mo and Co with Young's modulus ranging between 7-25 GPa, which is close to that of human cortical bone, therefore being suited for potential application in load-bearing implants.