A speech bulb prosthesis for a soft palate defect with a polyetherketoneketone (PEKK) framework fabricated by multiple digital techniques: A clinical report.

This clinical report described a digital workflow for the design, manufacture, and clinical delivery of a polyetherketoneketone (PEKK) removable dental prosthesis with a speech bulb. The process combined intraoral scanning, digital milling for the PEKK framework, and 3D printing for the definitive cast.

Application of digital impression and model in removable partial dentures for Kennedy classâ andâ ¡dentition defects.

OBJECTIVES: This study aimed to evaluate the application of digital impression and resin model technology in removable partial dentures (RPD) for Kennedy classⅠandⅡdentition defects. METHODS: Patients with Kennedy classⅠorⅡdental defect were selected and grouped in accordance with the following denture production processes: digital impression/resin model/cast cobalt-chromium alloy framework group (group A), digital impression/resin model/laser printed titanium framework group (group B), alginate impression/plaster model/cast cobalt-chromium alloy framework group (group C), and alginate impression/plaster model/laser printed titanium framework group (group D), with 40 cases in each group. The final RPD was examined in place in the mouth, and the evaluation indicators included the retention force of clamp ring, the tightness of connector and base, and the accuracy of occlusion. The evaluation scores of each index were used for analysis on the Kruskal-Wallis rank-sum test. RESULTS: No statistically significant difference in the score of each index was found among the four groups in RPD. CONCLUSIONS: The cast cobalt-chromium alloy and laser-printed titanium framework RPD using digital impression and resin model can meet the clinical restoration requirements of patients with Kennedy classⅠandⅡdentition defects.

The effect of insulin and kruppel like factor 10 on osteoblasts in the dental implant osseointegration in diabetes mellitus patients.

Diabetes mellitus, metabolic disease, is characterized by chronic hyperglycemia. Patients with diabetes mellitus are susceptible to infection and therefore have a higher prevalence and progression rate of periodontal disease. We aimed to study the effect of insulin and kruppel like factor 10 (KLF10) on osteoblasts proliferation and differentiation, and expression of bone metabolism-related molecules and related signaling pathway molecules of AKT serine/threonine kinase 1 (AKT) and nuclear factor kappa B subunit 1 (NF-κB) through in vitro experiments, which can provide theoretical basis for the dental implant osseointegration in diabetic patients. The osteoblasts (hFOB 1.19 cells) were subdivided into KLF10 gene over expression group, KLF10 gene knockdown group, and KLF10 gene knockdown + insulin treatment group. CCK-8 and ELISA were, respectively, used for analysis of cell proliferation and differentiation. In vitro experiments were applied to detect the mRNA and protein expression of bone metabolism-related molecules, respectively. GSE178351 dataset and GSE156993 dataset were utilized to explore the expression of KLF10 in periodontitis. In osteoblasts, insulin treatment increased the expression of KLF10. Insulin and KLF10 could reduce the proliferation and differentiation of osteoblasts. Knockdown of KLF10 could increase the expression of bone metabolism-related molecules and activate AKT and NF-κB pathways, whereas insulin reversed this effect. KLF10 was up-regulated in both patients with periodontitis and type 2 diabetes mellitus with periodontitis. It is assumed that knockdown of KLF10 in insulin resistance may promote osteoblasts differentiation and dental implant osseointegration in diabetic patients.

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs.

Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.