Repairing tooth defects with dental resin composites is currently the most commonly used method due to their tooth-colored esthetics and photocuring properties. However, the higher than desirable failure rate and moderate service life are the biggest challenges the composites currently face. Secondary caries is one of the most common reasons leading to repair failure. Therefore, many attempts have been carried out on the development of a new generation of antimicrobial and therapeutic dental polymer composite materials to inhibit dental caries and prolong the lifespan of restorations. These new antimicrobial materials can inhibit the formation of biofilms, reduce acid production from bacteria and the occurrence of secondary caries. These results are encouraging and open the doors to future clinical studies on the therapeutic value of antimicrobial dental resin-based restoratives. However, antimicrobial resins still face challenges such as biocompatibility, drug resistance and uncontrolled release of antimicrobial agents. In the future, we should focus on the development of more efficient, durable and smart antimicrobial dental resins. This article focuses on the most recent 5 years of research, reviews the current antimicrobial strategies of composite resins, and introduces representative antimicrobial agents and their antimicrobial mechanisms.

Backgroud: To analyze the morphological changes of the anterior alveolar bone after the retraction of incisors in premolar extraction cases and the relationship between incisor retraction and remodeling of the alveolar base represented by points A and B displacements. Methods: Pre- (T0) and post-treatment (T1) lateral cephalograms of 308 subjects in the maxilla and 154 subjects in the mandible who underwent the orthodontic treatment with extraction of 2 premolars in upper or lower arches were included. Alveolar bone width and height in both the maxillary and mandible incisor area were measured at T0 and T1 respectively. By superimposing the T0 and T1 cephalometric tracings, changes of points A and B, and the movement of the incisors were also measured. Then the correlation between incisor movement and the displacements of points A and B was analyzed. Results: The alveolar bone width (ABW) showed a significant decrease in both maxilla and mandible (P < 0.001) except the labial side of the mandible (P > 0.05). The alveolar bone height (ABH) showed a significant increase in the labial side of maxilla and a significant decrease in the lingual side of maxilla and mandible. A strong positive correlation was verified between incisor movement and position changes of points A and B in both horizontal and vertical directions. Conclusions: Anterior alveolar bone width and height generally decreased after orthodontic treatment. Incisor retraction led to significant position changes of points A and B. The decrease of anterior alveolar bone due to significant incisor retraction should be taken into account in treatment planning.

The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients with impacted canines and 49 patients without impacted canines. From all 74 subjects, we generated a dataset containing paired 2D panoramic X-rays and pseudo-3D images. This pseudo-3D image contained information about the location of the impacted canine in the buccal/lingual, mesial/distal, and apical/coronal positions. These data were utilized to train a deep-learning reconstruction algorithm, a generative AI. The location of the crown of the maxillary impacted canine was determined based on the output of the algorithm. The reconstruction was evaluated using the structure similarity index measure (SSIM) as a metric to indicate the quality of the reconstruction. The prediction of the impacted canine’s location was assessed in both the mesiodistal and buccolingual directions. The reconstruction algorithm predicts the position of the impacted canine in the buccal, middle, or lingual position with 41% accuracy, while the mesial and distal positions are predicted with 55% accuracy. The mean SSIM for the output is 0.71, with a range of 0.63 to 0.84. Our study represents the first application of AI reconstruction output for multidisciplinary care involving orthodontists, periodontists, and maxillofacial surgeons in diagnosing and treating maxillary impacted canines. Further development of deep-learning algorithms is necessary to enhance the robustness of dental reconstruction applications.

Aging severely impairs the beneficial effects of human dental pulp stem cells (hDPSCs) on cartilage regeneration. Lysine demethylase 3A (KDM3A) is involved in regulating mesenchymal stem cells (MSCs) senescence and bone aging. In this study, we investigated the role of KDM3A in hDPSCs aging and whether KDM3A could rejuvenate aged hDPSCs to enhance their chondrogenic differentiation capacity. The cellular aging of hDPSCs was evaluated by senescence-associated β-galactosidase (SA-β-gal) staining. Protein levels were determined using Western blot analysis. KDM3A was overexpressed in aged hDPSCs by lentivirus infection. Quantitative reverse-transcription polymerase chain reaction (RT-qPCR) were used to determine the mRNA levels of stemness markers. Toluidine blue staining was used to evaluate the effect of KDM3A overexpression on the chondrogenic differentiation of aged hDPSCs. hDPSCs at passage 12 or treated with etoposide exhibited augmented cellular senescence as evidenced by increased SA-β-gal activity. KDM3A was significantly increased during senescence of hDPSCs. Overexpression of KDM3A did not affect the stemness properties but significantly promoted the chondrogenic differentiation of aged hDPSCs. Our findings indicate that KDM3A plays an important role in the maintenance of the chondrogenic differentiation capacity of aged hDPSCs and suggest that therapies targeting KDM3A may be a novel strategy to rejuvenate aged hDPSCs.

Smile esthetics has become increasingly important for orthodontic patients, thus prediction of post-treatment smile is necessary for a perfect treatment plan. In this study, with a combination of three-dimensional craniofacial data from the cone beam computed tomography and color-encoded structured light system, a novel method for smile prediction was proposed based on facial expression transfer, in which dynamic facial expression was interpreted as a matrix of facial depth changes. Data extracted from the pre-treatment smile expression record were applied to the post-treatment static model to realize expression transfer. Therefore smile esthetics of the patient after treatment could be evaluated in pre-treatment planning procedure. The positive and negative mean values of error for prediction accuracy were 0.9 and − 1.1 mm respectively, with the standard deviation of ± 1.5 mm, which is clinically acceptable. Further studies would be conducted to reduce the prediction error from both the static and dynamic sides as well as to explore automatically combined prediction from the two sides.