3D printing technology has great advantages in small batch and personalized customization, so it has attracted much attention in the biomedical field. The consumables available for 3D printing include polymer, metal, ceramic and derived materials. Biomedical ceramics, with high melting point and poor toughness, are the most difficult materials to be used in 3D printing. The progress of 3D printing ceramic preparation process using ceramic powder, ceramic slurry, ceramic wire, ceramic film and other different raw materials as consumables are reviewed, and the surface roughness, size, density and other parameters of ceramics prepared by SLS, 3DP, DIW, IJP, SL, DLP, FDM, LOM and other different processes are compared. The study also summarizes the clinical application status of 3D printed bioceramics in the field of hard tissue repair such as bone tissue engineering scaffolds and dental prostheses. The SL ceramic additive manufacturing technology based on the principle of UV polymerization has better manufacturing precision, forming quality and the ability to prepare large-size parts, and can also endow bioceramics with better biological properties, mechanical properties, antibacterial, tumor treatment and other functions by doping trace nutrients and surface functional modification. Compared with the traditional subtractive manufacturing process, the bioceramics prepared by 3D printing not only have good mechanical properties, but also often have better biocompatibility and osteoconductivity.
Bone and Bones ; Ceramics ; Printing, Three-Dimensional ; Tissue Engineering ; Tissue Scaffolds

The minimum life is one of the most important research topics in synthetic biology. Minimizing a genome while at the same time maintaining an optimal growth of the cells is one of the important research objectives in metabolic engineering. Here we propose a genome minimization method based on genome scale metabolic network analysis. The metabolic network is minimized by first deleting the zero flux reactions from flux variability analysis, and then by repeatedly calculating the optimal growth rates after combinatorial deletion of the non-essential genes in the reduced network. We applied this method to the classic E. coli metabolic network model ---iAF1260 and successfully reduced the number of genes in the model from 1 260 to 312 while maintaining the optimal growth rate unaffected. We also analyzed the metabolic pathways in the network with the minimized number of genes. The results provide some guidance for the design of wet experiments to obtain an E. coli minimal genome.
Escherichia coli ; genetics ; metabolism ; Genes, Bacterial ; Genome, Bacterial ; genetics ; Metabolic Engineering ; Metabolic Networks and Pathways