Periodontitis is a dysbiotic biofilm-induced and host-mediated inflammatory disease of tooth supporting tissues that leads to progressive destruction of periodontal ligament and alveolar bone, thereby resulting in gingival recession, deep periodontal pockets, tooth mobility and exfoliation, and aesthetically and functionally compromised dentition. Due to the improved biopharmaceutical and pharmacokinetic properties and targeted and controlled drug release, nano-based drug delivery systems have emerged as a promising strategy for the treatment of periodontal defects, allowing for increased efficacy and safety in controlling local inflammation, establishing a regenerative microenvironment, and regaining bone and attachments. This review provides an overview of nano-based drug delivery systems and illustrates their practical applications, future prospects, and limitations in the field of periodontal tissue regeneration.

Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors.Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined.Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment.Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.

Background: The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and to evaluate the accuracy of this method based on free-hand tracing. Methods: Twenty-eight pairs of pre-treatment (T₁) and post-treatment (T₂) cephalograms were selected. Structural superimpositions of the anterior cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching algorithm. To quantitatively evaluate the differences between the two methods, the hand superimposed patterns were digitized. After automated and hand superimposition of T₂ cephalograms to T₁ cephalometric templates, landmark distances between paired automated and hand T₂ cephalometric landmarks were measured. Differences in hand superimposition among the operators were also calculated. Results: The T₂ landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between hand and automated superimposition (p > 0.05). Conclusions: Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research.

Background: Orthodontic tooth movement (OTM) in a region containing alveolar bone defects with insufficient height and width is hard to achieve. Bovine bone mineral (Bio-Oss) is available to restore the alveolar defect; however, whether the region augmented with a bovine bone mineral graft (BG) is feasible for OTM, and the mechanisms by which macrophages remodel the BG material, is uncertain under the mechanical force induced by OTM. Material and methods: Rats were divided into three groups: OTM (O), OTM + BG material (O + B), and Control (C). First molars were extracted to create bone defects in the O and O + B groups with bovine bone mineral grafting in the latter. Second molars received OTM towards the bone defects in both groups. After 28 days, maxillae were analyzed using microfocus-computed tomography (μCT) and scanning-electron-microscopy (SEM); and macrophages (M1/M2) were stained using immunofluorescence. THP-1 cell-induced macrophages were cultured under mechanical force (F), BG material (B), or both (F + B). Phagocytosis-related signaling molecules (cAMP/PKA/RAC1) were analyzed, and conditioned media was analyzed for MMP-9 and cytokines (IL-1β, IL-4). Results: Our study demonstrated that alveolar defects grafted with BG materials are feasible for OTM, with significantly increased OTM distance, bone volume, and trabecular thickness in this region. SEM observation revealed that the grafts served as a scaffold for cells to migrate and remodel the BG materials in the defect during OTM. Moreover, the population of M2 macrophages increased markedly both in vivo and in cell culture, with enhanced phagocytosis via the cAMP/PKA/RAC1 pathway in response to mechanical force in combination with BG particles. By contrast, M1 macrophage populations were decreased under the same circumstances. In addition, M2 macrophage polarization was also indicated by elevated IL-4 levels, reduced IL-1β levels, and less active MMP-9 in cell culture. Conclusion: This study explored the mechanisms of mechanical force-induced alveolar bone remodeling with bovine bone mineral grafts during OTM. The results might provide molecular insights into the related clinical problems of whether we can move teeth into the grafted materials; and how these materials become biologically remodeled and degraded under mechanical force.

The soft tissue morphology after orthognathic surgery has always been difficult to predict in preoperative planning. This narrative review aims to evaluate the current prediction methods and point out the direction for future research to improve predicting accuracy and speed. PubMed was the database for the review. The keywords for searching include: orthognathic surgery, prediction methods, simulation methods, artificial intelligence (AI), machine learning (ML), deep learning (DL), soft tissue, and surgical planning. Sixty‐seven articles were found and 60 relevant articles were evaluated. The review finds out that with the transition from two‐dimensional (2D) prediction to three‐dimensional (3D) prediction, AI algorithms, especially DL methods, are brought forward promisingly, such as deep neural network (DNN), PointNet, and Transformer. DL methods have the advantages of high accuracy, fast speed, and simplified use, therefore have broad development prospects. However, DL methods need to be further improved, mainly in data insufficiency, overfitting, and the lack of interpretability. To conclude, the review proposes methods that could improve the accuracy and availability of prediction by DL, such as learning from large and high‐quality databases, establishing separate databases for groups with different features, optimizing and simplifying the algorithm structure, utilizing integrated multimodal data, and implementing visualization techniques.

To explore the three-dimensional (3D) displacement patterns of maxillary molar distalization using clear aligners (CA) under four anchorage designs and provide clinical guidelines for optimal traction methods. A 3D finite element model was constructed based on CBCT and digital models from an adult patient requiring maxillary molar distalization. The model included cortical bone, cancellous bone, periodontal ligament, teeth, CA, and mini-screws. Four anchorage designs were simulated during sequential distal movement of bilateral maxillary second molars, first molars, and second premolars: (a) intramaxillary anchorage, (b) intermaxillary anchorage, (c) buccal mini-screw anchorage, and (d) palatal mini-screw anchorage. Displacement patterns of anterior and molar teeth were analyzed using ANSYS software and compared to the patient’s actual maxillary dentition movement. Most teeth’s actual displacement aligned with finite element predictions, except for central incisors, which showed mesial tipping instead of the simulated distal tipping. In the sequence-specific simulations: (1) Second molars only: All groups exhibited labial tipping and extrusion of anterior teeth (greater in Groups (a) and (b)), distal and buccal tipping with intrusion of second molars, and buccal tipping with extrusion of first molars (distal tipping in Groups (c) and (d), mesial in Groups (a) and (b)). (2) Second and first molars simultaneously: Anterior teeth showed reduced labial tipping, with palatal tipping and intrusion of right canines in Groups (c) and (d). All groups displayed distal tipping and intrusion of molars, with palatal tipping in second molars and buccal tipping in first molars. (3) Second premolars initiated: Anterior teeth in Groups (c) and (d) exhibited reduced labial tipping and extrusion, even with some palatal tipping and intrusion. Second molars in Groups (a) and (b) showed mesial, buccal tipping, and extrusion, while first molars in all groups and second molars in Groups (c) and (d) showed distal, palatal tipping, and intrusion. No significant differences in torque or vertical control were observed between buccal and palatal mini-screw anchorage. Compared to intramaxillary or intermaxillary anchorage, the combination of mini-screws anchorage has a better effect on the torque control of anterior teeth, which also helps to promote the distal movement of molars. While no significant differences were observed in the torque and vertical control of anterior teeth or molars between buccal and palatal mini-screws, the findings underscore the versatility of mini-screw anchorage in accommodating individual patient anatomies. Notably, the displacement patterns of bilateral maxillary teeth were not always symmetrical, highlighting the need for personalized treatment planning and close monitoring during orthodontic therapy.