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Purpose -To develop a computer-assisted prefabricated implant design and manufacturing system to improve the esthetic outcome in chin surgery. Design/methodology/approach -Design methods for medical rapid prototyping (RP) of custom-fabricated chin augmentation implant are presented in this paper. After a careful preoperative planning based on cephalometric tracing for esthetic assessment, helical computed tomography data were used to create a three-dimensional model of the deficient mandible. Based on these data, the inner surface of the prosthesis was designed to fit the bone surface exactly. The outer geometry was generated from a dried human mandible to create anatomically correct shape prosthesis. The inner and outer surfaces were then connected, and a solid model resulted. A RP system was used for production of the physical models. The surgical planning was performed using the implants and skull models. The resulting SLA implant is used for the production of a mold, which is used to cast the titanium part. Three patients with a congenital small chin or a small and asymmetric mandible underwent reconstruction with individual prefabricated implant. Mean follow-up period was 1.5 years. Findings -This approach showed significant results in chin augmentation. Compared with traditional methods, the intra-operative fit was excellent. The operating time was reduced. Postoperatively, the patients experienced the restoration of a natural chin contour, so the esthetic outcome was pleasing. Over the mean follow-up period of 1.5 years, there were no complications and no implant had to be removed. Long-term excellent esthetic outcomes by using this new technique have recently been reported.Research limitations/implications -The methods described above suffer from certain limitations. The registration of the mandible template to create the augmentation image requires high skills of the designer. In addition, the use of RP model in preoperative preparation is expensive. Practical implications -This method not only demonstrates the significant progress in the reconstruction of chin defects using CAD/CAM RP and RT, compared with the conventional methods of chin augmentation surgery, but also provides natural geometrical prosthesis contour design and accurate fabrication and precise fitting of the prosthesis. The advantages of using this technique are that the physical model of the implant is fitted on the skull model so that the surgeon can plan and rehearse the surgery in advance and a less invasive surgical procedure and less time-consuming reconstructive and an adequate esthetic can result. Originality/value -This clinical case demonstrated the potential value of CAD/CAM and RP-based custom fitted and anatomically correct shape prosthesis fabrication and presurgical planning in craniofacial surgery.

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Background Osteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA. Methods and materials The GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines. Results The Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts. Conclusions The 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.