Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Objective:To analyze the effect of glucocorticoid on the biological function of lens epithelial cells (LECs) by bioinformatics and predict related microRNA (miRNA).Methods:GSE3040 database was downloaded and the human LECs line (HLE-B3) cells in the experimental group were treated with 1 μmol/L dexamethasone, and HLE-B3 cells in the control group were treated with 1 μmol/L dimethyl sulfoxide(DMSO).GEO2R was used to analyze the differentially expressed genes between the two groups.Metascape website was employed to analyze the functional enrichment of differentially expressed genes, and EdU cell proliferation assay was performed to detect the difference in cell proliferation between the two groups.STRING website and cytoscape software were used to construct protein-protein interaction network.Hub genes were calculated by cytohubba app, and quantitative real-time PCR was performed to detect the expression levels of hub genes between the two groups.MirCode website was used to predict the related miRNAs.Results:A total of 341 differentially expressed genes were detected between the experimental group and the control group, among which there were 300 up-regulated genes and 41 down-regulated genes. SLC12A1, MED13L, ALDH5A1, SLC15A3 and WWC1 were the top five down-regulated genes and SCNN1A, ANKRD36, FKBP5, PYY and ADH1B were the top five up-regulated genes.The top 20 terms of functional enrichment were listed, and the negative regulation of HLE-B3 cells proliferation showed the most enrichment.Cell proliferation rate in the experimental group was (8.09±0.20)%, which was significantly lower than (39.63±0.80)% in the control group ( t=38.43, P<0.01).The top ten hub genes were SST, CXCL8, GRM1, GNRH1, CXCL5, PPBP, CX3CR1, PYY, EDNRA and GRK5, and quantitative real time PCR confirmed that the expression levels of SST, CXCL8, GRM1, PYY, EDNRA and GRK5 mRNA were statistically different (all at P<0.05).The top six miRNAs which might be associated with hub genes were miR-15abc, miR-214, miR-23abc, miR-129-5p, miR-132 and miR-24. Conclusions:The 1 μmol/L glucocorticoid can negatively regulate the proliferation of HLE-B3 cells. SST, CXCL8, GRM1, PYY, EDNRA and GRK5 may be hub genes and miR-15abc, miR-214, miR-23abc, miR-129-5p, miR-132, miR-24 are most likely to relate to them.