Nd-induced honeycomb structure of intermetallic phase enhances the corrosion resistance of Mg alloys for bone implants.

Mg-5.6Zn-0.5Zr alloy (ZK60) tends to degrade too rapid for orthopedic application, in spite of its natural degradation, suitable strength and good biocompatibility. In this study, Nd was alloyed with ZK60 via laser melting method to enhance its corrosion resistance. The microstructure features, mechanical properties and corrosion behaviors of ZK60-xNd (x = 0, 1.8, 3.6, 5.4 wt.%) were investigated. Results showed that laser melted ZK60-xNd were composed of fine ɑ-Mg grains and intermetallic phases along grain boundaries. And the precipitated intermetallic phases experienced successive changes: divorced island-like MgZn phase → honeycomb-like T phase → coarsened and agglomerated W phase with Nd increasing. It was worth noting that ZK60-3.6Nd with honeycomb-like T phase exhibited an optimal corrosion behavior with a corrosion rate of 1.56 mm year-1. The improved corrosion behavior was ascribed to: (I) dense surface film caused by the formation of Nd2O3 hindered the invasion of immersion solution; (II) the three-dimensional honeycomb structure of intermetallic phases formed a tight barrier to restrain the propagation of corrosion. Moreover, ZK60-3.6Nd exhibited good biocompatibility. It was suggested that ZK60-3.6Nd was a preferable candidate for biodegradable bone implant.

Efficient Computer-Aided Design of Dental Inlay Restoration: A Deep Adversarial Framework.

Restoring the normal masticatory function of broken teeth is a challenging task primarily due to the defect location and size of a patient's teeth. In recent years, although some representative image-to-image transformation methods (e.g. Pix2Pix) can be potentially applicable to restore the missing crown surface, most of them fail to generate dental inlay surface with realistic crown details (e.g. occlusal groove) that are critical to the restoration of defective teeth with varying shapes. In this article, we design a computer-aided Deep Adversarial-driven dental Inlay reStoration (DAIS) framework to automatically reconstruct a realistic surface for a defective tooth. Specifically, DAIS consists of a Wasserstein generative adversarial network (WGAN) with a specially designed loss measurement, and a new local-global discriminator mechanism. The local discriminator focuses on missing regions to ensure the local consistency of a generated occlusal surface, while the global discriminator aims at defective teeth and adjacent teeth to assess if it is coherent as a whole. Experimental results demonstrate that DAIS is highly efficient to deal with a large area of missing teeth in arbitrary shapes and generate realistic occlusal surface completion. Moreover, the designed watertight inlay prostheses have enough anatomical morphology, thus providing higher clinical applicability compared with more state-of-the-art methods.

Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks.

The tooth defect is a frequently occurring disease within the field of dental clinic. However, the traditional manual restoration for the defective tooth needs an especially long treatment time, and dental computer aided design and manufacture (CAD/CAM) systems fail to restore the personalized anatomical features of natural teeth. Aiming to address the shortcomings of existed methods, this article proposes an intelligent network model for designing tooth crown surface based on conditional generative adversarial networks. Then, the data set for training the network model is constructed via generating depth maps of 3D tooth models scanned by the intraoral. Through adversarial training, the network model is able to generate tooth occlusal surface under the constraint of the space occlusal relationship, the perceptual loss, and occlusal groove filter loss. Finally, we carry out the assessment experiments for the quality of the occlusal surface and the occlusal relationship with the opposing tooth. The experimental results demonstrate that our method can automatically reconstruct the personalized anatomical features on occlusal surface and shorten the treatment time while restoring the full functionality of the defective tooth.

The extraction method of tooth preparation margin line based on S-Octree CNN.

The tooth preparation margin line has a significant impact on the marginal fitness for dental restoration. Among the previous methods, the extraction of margin line mainly relies on manual interaction, which is complicated and inefficient. Therefore, we propose a method to extract the margin line with the convolutional neural network based on sparse octree (S-Octree) structure. First, the dental preparations are rotated to augment the dataset. Second, the preparation models are treated as the sparse point cloud with labels through the spatial partition method of the S-Octree. Then, based on the feature line, the dental preparation point cloud is automatically divided into two regions by the convolutional neural network (CNN). Third, in order to obtain the margin line, we adopt some methods such as the dense condition random field (dense CRF), point cloud reconstruction, and back projection to the original dental preparation model. Finally, based on the measurement indicators of accuracy, sensitivity, and specificity, the average accuracy of the label predicted by the network model can reach 97.43%. The experimental results show that our method can automatically accomplish the extraction of the tooth preparation margin line.

Complete denture tooth arrangement technology driven by a reconfigurable rule.

The conventional technique for the fabrication of complete dentures is complex, with a long fabrication process and difficult-to-control restoration quality. In recent years, digital complete denture design has become a research focus. Digital complete denture tooth arrangement is a challenging issue that is difficult to efficiently implement under the constraints of complex tooth arrangement rules and the patient's individualized functional aesthetics. The present study proposes a complete denture automatic tooth arrangement method driven by a reconfigurable rule; it uses four typical operators, including a position operator, a scaling operator, a posture operator, and a contact operator, to establish the constraint mapping association between the teeth and the constraint set of the individual patient. By using the process reorganization of different constraint operators, this method can flexibly implement different clinical tooth arrangement rules. When combined with a virtual occlusion algorithm based on progressive iterative Laplacian deformation, the proposed method can achieve automatic and individual tooth arrangement. Finally, the experimental results verify that the proposed method is flexible and efficient.

A combined strategy to enhance the properties of Zn by laser rapid solidification and laser alloying.

The orthopedic application of Zn is limited owing to the poor strength and low plasticity. In this study, a novel strategy by combining rapid solidification obtained by selective laser melting (SLM) and alloying with Mg was proposed to improve the mechanical properties of Zn. The microstructures, mechanical properties, as well as in vitro cytocompatibility of SLM processed Zn-xMg (x = 0-4 wt%) were studied systematically. Results shown that SLM processed Zn-xMg alloys consisted of fine equiaxed α-Zn grains with homogeneously precipitated Mg2Zn11 along grain boundaries. More importantly, the grains size of α-Zn was decreased from 104.4 ±â€¯30.4 µm to 4.9 ±â€¯1.4 µm with Mg increasing. And Mg mainly dissolved in α-Zn developing into supersaturated solid solution due to rapid solidification effect. As a consequence, the ultimate tensile strength and elongation were enhanced by 361% and 423%, respectively, with Mg containing up to 3 wt%. Meanwhile, alloying with Mg enhanced the corrosion resistance of Zn, with the degradation rate decreasing from 0.18 ±â€¯0.03 mm year-1 to 0.10 ±â€¯0.04 mm year-1. Furthermore, SLM processed Zn-xMg exhibited good biocompatibility. This research suggested that SLM processed Zn-3Mg alloy was a potential biomaterial for orthopedic applications.

Mechanical reinforcement of bioceramics scaffolds via fracture energy dissipation induced by sliding action of MoS2 nanoplatelets.

The inherent brittleness of bioceramics restricts their applications in load bearing implant, although they possess good biocompatibility and bioactivity. In this study, molybdenum disulfide nanoplatelets (MSNPs) were used to reinforce bioceramics (Mg2SiO4/CaSiO3) scaffolds fabricated by selective laser sintering (SLS). The fracture mode of scaffolds was transformed from transgranular to mixed trans- and intergranular. It could be explained that MSNPs could slide easily due to their weak interlayer van der Waals interactions and provide elastic deformation due to their high elastic modulus. Such sliding action and elastic deformation synergistically induced crack bridging, crack deflection, pull-out and break of MSNPs. Those effects effectively increased the fracture energy dissipation and strain capacity as well as changed the fracture mode, contributing to high fracture toughness and compression strength. Additionally, the scaffolds with MSNPs not only formed a bioactive apatite layer in simulated body fluid, but also supported cell adhesion and proliferation.