The clinical diagnosis and treatment of kidney transplantation involve a large amount of high-dimensional, nonlinear and multimodal complex data. Traditional statistical methods make it difficult to fully identify the underlying patterns. However, artificial intelligence (AI) technology has stronger capabilities in data integration and analysis, and is particularly adept at handling complex data. This article reviews the application of AI in the field of kidney transplantation, covering the role of deep learning models in the quantitative assessment of donor kidney quality in the pre-transplantation stage, the value of virtual biopsy systems and the optimization strategies for donor-recipient matching, as well as the dynamic prediction of graft survival rate by the iBox system in the post-transplantation stage, the advantages of AI models in non-invasive monitoring of rejection and the application results of individualized dose prediction of immunosuppressive drugs. Although the clinical application of medical AI is still limited by insufficient model interpretability, questionable generalization ability, and ethical and legal risks, technologies such as federated learning and digital twins are expected to solve the problems of data privacy and simulation prediction, and further promote the precise development of kidney transplantation.

Objective: To evaluate the bond performance of primers, universal adhesives and self-adhesive resin cements containing methacryloyloxydecyl dihydrogen phosphate (MDP) to zirconia. Methods: Two hundred and ten yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) plates were prepared and divided into seven groups according to each of six MDP-containing products and one MDP-free resin cement. Two primers, two universal adhesives and two self-adhesive resin cements were applied on the specimens after these specimens recived alumina air abrasion. The group of MDP-free resin cement was set as control. Each group contained 30 specimens. Y-TZP plates were bonded to resin composite to build resin bonding specimens, and half of these plates were stored in distilled water at 37 ℃ for 24 hours, the other half were stored for 12 months. Shear bond strength (SBS) test and fracture modes analysis were performed. Results: The SBS values of all the experimental groups were above 9.51 MPa, which increased 24-hour values compared to the (5.04±0.50) MPa of the control group. Group using one of the self-adhesive resin cement yielded the highest SBS, (11.06±0.84) MPa. SBS of all groups decreased significantly compared to 24-hour SBS after aging (P<0.05), however, the SBS values of MDP-containing products groups can still be maintained above 7.44 MPa. Two self-adhesive cement groups and one of the universal adhesive group maintained higher SBS values, and the values were statistical different from those from the two primer groups (P<0.05) . Conclusions Different MDP-containing products have different effects on the bond durability of zirconia.

PURPOSE: The aim was to study the masking ability of high-translucency monolithic zirconia and provide guidance in selecting resin luting cements in order to mask discolored substrates. MATERIALS AND METHODS: 160 high-translucency zirconia specimens were divided into 32 groups depending on their thickness and shades. Using five shades of try-in paste, the specimens were luted onto the sub strates (Co-Cr, precious-metal, opaque porcelain-sintered Co-Cr, opaque porcelain-sintered precious-metal, and 5M3-shade zirconia). All CIELAB color parameters were measured and statistically analyzed. RESULTS: Zirconia shade and thickness and try-in paste shade affected CIELAB color parameters (P=.000) in different substrates groups, and there were interactions among these factors (P=.000). All five try-in paste shades can be chosen to achieve ΔE values of zirconia with 1.2 – 1.5 mm for masking dark-tooth-like 5M3-shade and zirconia with 1.5 mm for masking precious-metal groups < 2.6. Only suitable try-in paste shades were used, can ΔE values that less than 2.6 be achieved when applied translucent monolithic zirconia with 0.7–1.0 mm for masking dark-tooth-like 5M3-shade and zirconia with 0.7 – 1.2 mm for masking precious-metal groups. CONCLUSION: Choosing suitable resin cement shades is necessary for high-translucency monolithic zirconia to achieve ideal masking ability (ΔE < 2.6) on the dark-tooth.
Masks ; Resin Cements ; Spectrophotometry

PURPOSE: Shear bond strength (SBS) test is the most commonly used method for evaluating resin bond strength of zirconia, but SBS results vary among different studies even when evaluating the same bonding strategy. The purpose of this study was to promote standardization of the SBS test in evaluating zirconia ceramic bonding and to investigate factors that may affect the SBS value of a zirconia/resin cement/composite resin bonding specimen.MATERIALS AND METHODS: The zirconia/resin cement/composite resin bonding specimens were used to simulate loading with a shear force by the three-dimensional finite element (3D FE) modeling, in which stress distribution under uniform/non-uniform load, and different resin cement thickness and different elastic modulus of resin composite were analyzed. In vitro SBS test was also performed to validate the results of 3D FE analysis.RESULTS: The loading flat width was an important affecting factor. 3D FE analysis also showed that differences in resin cement layer thickness and resin composite would lead to the variations of stress accumulation area. The SBS test result showed that the load for preparing a SBS specimen is negatively correlated with the resin cement thickness and positively correlated with SBS values.CONCLUSION: When preparing a SBS specimen for evaluating bond performance, the load flat width, the load applied during cementation, and the different composite resins used affect the SBS results and therefore should be standardized.
Cementation ; Ceramics ; Composite Resins ; Elastic Modulus ; In Vitro Techniques ; Methods ; Resin Cements