Regenerative therapy for tissues by mesenchymal stem cell (MSCs) transplantation has received much attention. The cluster of differentiation (CD)146 marker, a surface-antigen of stem cells, is crucial for angiogenic and osseous differentiation abilities. Bone regeneration is accelerated by the transplantation of CD146-positive deciduous dental pulp-derived mesenchymal stem cells contained in stem cells from human exfoliated deciduous teeth (SHED) into a living donor. However, the role of CD146 in SHED remains unclear. This study aimed to compare the effects of CD146 on cell proliferative and substrate metabolic abilities in a population of SHED. SHED was isolated from deciduous teeth, and flow cytometry was used to analyze the expression of MSCs markers. Cell sorting was performed to recover the CD146-positive cell population (CD146+) and CD146-negative cell population (CD146-). CD146 + SHED without cell sorting and CD146-SHED were examined and compared among three groups. To investigate the effect of CD146 on cell proliferation ability, an analysis of cell proliferation ability was performed using BrdU assay and MTS assay. The bone differentiation ability was evaluated using an alkaline phosphatase (ALP) stain after inducing bone differentiation, and the quality of ALP protein expressed was examined. We also performed Alizarin red staining and evaluated the calcified deposits. The gene expression of ALP, bone morphogenetic protein-2 (BMP-2), and osteocalcin (OCN) was analyzed using a real-time polymerase chain reaction. There was no significant difference in cell proliferation among the three groups. The expression of ALP stain, Alizarin red stain, ALP, BMP-2, and OCN was the highest in the CD146+ group. CD146 + SHED had higher osteogenic differentiation potential compared with SHED and CD146-SHED. CD146 contained in SHED may be a valuable population of cells for bone regeneration therapy.

Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.

There is no clinical evidence of the usage of 2-methacryloyloxyethyl phosphorylcholine (MPC) polymers in dental practice. We performed in vitro studies to determine whether the application of an MPC coating to stainless steel orthodontic wires confers low-friction and antimicrobial properties to these wires. The friction test on MPC-coated wires was performed using a precision universal/tensile tester. MPC polymer was coated on a 50 × 50 mm stainless steel plate, and samples were assessed using an antimicrobial activity test. To verify the effect of MPC polymer-treated wires on experimental tooth movement models in vitro, examinations were performed on typodonts to determine the improvement in tooth movement efficiency. The polymer treatment wire groups demonstrated significantly enhanced tooth movement compared with the untreated wire groups, at both 50 g and 100 g traction forces. The results indicated that MPC coating inhibited the attachment of oral bacteria, such as Streptococcus mutans, on a stainless steel plate. Additionally, the coating seemed to improve the efficiency of tooth movement by reducing the occurrence of friction. The application of an MPC coating onto stainless steel wires, which are used as orthodontic materials, may reduce static friction and bacterial adherence to the oral cavity and improve tooth movement.

Malocclusion and morphological abnormalities of the jawbone often affect the stomatognathic function and long-term postoperative stability in patients with jaw deformities. There are few reports on the effect of maximum tongue pressure (MTP) in these patients. We investigated the relationship between the MTP and jawbone morphology and the effect of the MTP on surgery in 42 patients with jaw deformity who underwent surgical orthodontic treatment at Hiroshima University Hospital. The MTP was measured using a tongue pressure measurement device; the average value was considered as the MTP. Based on the MTP measured before surgery, patients were classified into the high- or the low-MTP group. The clinical findings and results of the cephalometric analysis were compared. Posterior movement of the mandible in the high-MTP group was significantly lower than that in the low-MTP group. The ANB angle, overjet, and overbite in the high-MTP group were significantly smaller than those in the low-MTP group. On the other hand, there was no difference between the two groups in the measured values, indicating a labial inclination of the anterior teeth (U1 to SN, U1 to FH, IMPA, and FMIA). MTP has been suggested to affect mandibular prognathism in patients with jaw deformities.

Background/Objectives: Cephalometric analysis has a pivotal role in the quantification of the craniofacial skeletal complex, facilitating the diagnosis and management of dental malocclusions and underlying skeletal discrepancies. This study aimed to develop a deep learning system that predicts a cephalometric skeletal parameter directly from lateral profile photographs, potentially serving as a preliminary resource to motivate patients towards orthodontic treatment. Methods: ANB angle values and corresponding lateral profile photographs were obtained from the medical records of 1600 subjects (1039 female and 561 male, age range 3 years 8 months to 69 years 1 month). The lateral profile photographs were randomly divided into a training dataset (1250 images) and a test dataset (350 images). Seven regression convolutional neural network (CNN) models were trained on the lateral profile photographs and measured ANB angles. The performance of the models was assessed using the coefficient of determination (R²) and mean absolute error (MAE). Results: The R² values of the seven CNN models ranged from 0.69 to 0.73, and the MAE values ranged from 1.46 to 1.53. Among the seven models, InceptionResNetV2 showed the highest success rate for predictions of ANB angle within 1° of range and the highest performance in skeletal class prediction, with macro-averaged accuracy, precision, recall, and F1 scores of 73.1%, 78.5%, 71.1%, and 73.0%, respectively. Conclusions: The proposed deep CNN models demonstrated the ability to predict a cephalometric skeletal parameter directly from lateral profile photographs, with 71% of predictions being within 2° of accuracy. This level of accuracy suggests potential clinical utility, particularly as a non-invasive preliminary screening tool. The system’s ability to provide reasonably accurate predictions without radiation exposure could be especially beneficial for initial patient assessments and may enhance efficiency in orthodontic workflows.