PURPOSE: . The objective of this study was to investigate how internal structures influence the overall and marginal accuracy of full arch preparations fabricated through additive manufacturing in different printing systems. MATERIALS AND METHODS: . A full-arch preparation digital model was set up with three internal designs, including solid, hollow, and grid. These were printed using three different resin printers with nine models in each group. After scanning, each data was imported into the 3D data processing software together with the master cast, aligned and trimmed, and then put into the 3D data analysis software again to compare the overall and marginal deviation whose results are expressed using root mean square values and color maps. To evaluate the trueness of the resin model, the test data and reference data were compared, and the precision was evaluated by comparing the test data sets. Color maps were observed for qualitative analysis. Data were statistically analyzed by one-way analysis of variance and Bonferroni method was used for post hoc comparison (α = .05). RESULTS: . The influence of different internal structures on the accuracy of 3D printed resin models varied significantly (P < .05). Solid and grid models showed better accuracy, while the hollow model exhibited poor accuracy. The color maps show that the resin models have a tendency to shrink inwards. CONCLUSION . The internal structure design influences the accuracy of the 3D printing model, and the effect varies in different printing systems. Irrespective of the kind of printing system, the printing accuracy of hollow model was observed to be worse than those of solid and grid models. [J Adv Prosthodont 2023;15:145-54]

Objective:To compare and evaluate the difference in maxillary dentition position using an anatomical facebow and jaw movement analyzer.Methods:From March to May 2023, 15 medical interns from Yantai Stomatological Hospital were recruited, including 9 males and 6 females, aged 20-25 years. Digital models and plaster models of maxillary dentition were obtained from the 15 medical interns. The anatomical facebow group (AFB) and jaw movement analyzer group (JMA) were used to transfer the position of the maxillary dentition to the virtual articulator. The virtual occlusal articulator module of exocad denture design software was used to measure the inclination angle of the occlusal plane of the two groups, the distance between the mesio-incisal angle of the left maxillary central incisor and the lateral center point of the lateral condylar sphere of the virtual occlusal articulator, the distance between the mesial buccal cusp of the maxillary first molar and the lateral center point of the lateral condyle sphere of the virtual articulator. The same marks (mesial incisor point of left maxillary central incisor and mesial buccal cusp point of both maxillary first molars) were measured in two groups of maxillary dentition, and the root-mean-square error between 3 points was calculated.Results:The occlusal plane inclination angle in AFB group (9.11°±3.85°) was significantly larger than that in JMA group (4.94°±2.69°) ( t=10.45, P<0.001). There were significant differences between AFB and JMA groups. The distances from the mesial cusp of the left first molar to the lateral center of the left condylar, from the mesial cusp of the left maxillary central incisor to the lateral center of the left condylar[(91.75±3.05), (129.09±4.60) mm]were significantly smaller than those in the JMA group[(95.68±5.45), (132.41±5.64) mm]( t=-4.48, P=0.001; t=-4.21, P=0.001). In both groups of models, the distance of the mesial cusp of the left maxillary central incisor was (8.81±2.56) mm, and the distance between mesial buccal cusp of maxillary left first molar was (7.56±2.49) mm, the distance between mesial buccal cusp of maxillary right first molar was (7.13±2.77) mm; the root mean square error was (7.93± 2.94) mm. Compared with 0, the difference was statistically significant ( t=10.45, P<0.001). Conclusions:There were differences between the two methods (anatomical facebow and the jaw movement analyzer) for transferring the maxillary dentition position to the three-dimensional space position of the virtual articulator.

Objective:To identify tooth number abnormalities on pediatric panoramic radiographs based on deep learning.Methods:Eight hundred panoramic radiographs of children aged 4 to 11 years meeting the inclusion and exclusion criteria were selected and randomly assigned by writing programs in Python (version 3.9) to the training set (480 images), verification set (160 images) and internal test set (160 images), taken in Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology between November 2012 to August 2020. And all panoramic radiographs of children aged 4 to 11 years taken in the First Outpatient Department of Peking University School and Hospital of Stomatology from June 2022 to December 2022 were collected as the external test set (907 images). All of the 1 707 images were obtained by operators to determine the outline and to label the tooth position of each deciduous tooth, permanent tooth, permanent tooth germ and additional tooth. The deep learning model with ResNet-50 as the backbone network was trained on the training set, validated on the verification set, tested on the internal test set and external test set. The images of test sets were divided into two categories according to whether there was abnormality of tooth number, to calculate sensitivity, specificity, positive predictive value and negative predictive value, and then divided into four types of extra teeth and missing permanent teeth both existed, extra teeth existed only, missing permanent teeth existed only, and normal teeth number, to calculate Kappa values. Results:The sensitivity, specificity, positive predictive value and negative predictive value were 98.0%, 98.3%, 99.0% and 96.7% in the internal test set, and 97.1%, 98.4%, 91.9% and 99.5% in the external test set respectively, according to whether there was abnormality of tooth number. While images were divided into four types, the Kappa value obtained in the internal test set was 0.886, and that in the external test set was 0.912. Conclusions:In this study, a deep learning-based model for identifying abnormal tooth number of children was developed, which could identify the position of additional teeth and output the position of missing permanent teeth on the basis of identifying normal deciduous and permanent teeth and permanent tooth germs on panoramic radiographs, so as to assist in diagnosing tooth number abnormalities.

Panic disorder (PD) is an acute paroxysmal anxiety disorder with poorly understood pathophysiology. The dorsal periaqueductal gray (dPAG) is involved in the genesis of PD. However, the downstream neurofunctional changes of the dPAG during panic attacks have yet to be evaluated in vivo. In this study, optogenetic stimulation to the dPAG was performed to induce panic-like behaviors, and in vivo positron emission tomography (PET) imaging with F-flurodeoxyglucose (F-FDG) was conducted to evaluate neurofunctional changes before and after the optogenetic stimulation. Compared with the baseline, post-optogenetic stimulation PET imaging demonstrated that the glucose metabolism significantly increased (P < 0.001) in dPAG, the cuneiform nucleus, the cerebellar lobule, the cingulate cortex, the alveus of the hippocampus, the primary visual cortex, the septohypothalamic nucleus, and the retrosplenial granular cortex but significantly decreased (P < 0.001) in the basal ganglia, the frontal cortex, the forceps minor corpus callosum, the primary somatosensory cortex, the primary motor cortex, the secondary visual cortex, and the dorsal lateral geniculate nucleus. Taken together, these data indicated that in vivo PET imaging can successfully detect downstream neurofunctional changes involved in the panic attacks after optogenetic stimulation to the dPAG.