Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.

Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.

Multi-site enhancement of osteogenesis: peptide-functionalized GelMA hydrogels with three-dimensional cultures of human dental pulp stem cells.

Human dental pulp stem cells (hDPSCs) have demonstrated greater proliferation and osteogenic differentiation potential in certain studies compared to other types of mesenchymal stem cells, making them a promising option for treating craniomaxillofacial bone defects. However, due to low extracting concentration and long amplifying cycles, their access is limited and utilization rates are low. To solve these issues, the principle of bone-forming peptide-1 (BFP1) in situ chemotaxis was utilized for the osteogenic differentiation of hDPSCs to achieve simultaneous and synergistic osteogenesis at multiple sites. BFP1-functionalized gelatin methacryloyl hydrogel provided a 3D culture microenvironment for stem cells. The experimental results showed that the 3D composite hydrogel scaffold constructed in this study increased the cell spread area by four times compared with the conventional GelMA scaffold. Furthermore, the problems of high stem cell dosage and low rate of utilization were alleviated by orchestrating the programmed proliferation and osteogenic differentiation of hDPSCs. In vivo, high-quality repair of critical bone defects was achieved using hDPSCs extracted from a single tooth, and multiple 'bone island'-like structures were successfully observed that rapidly induced robust bone regeneration. In conclusion, this study suggests that this kind of convenient, low-cost, island-like osteogenesis strategy involving a low dose of hDPSCs has great potential for repairing craniomaxillofacial critical-sized bone defects.

Antibacterial performance of a porous Cu-bearing titanium alloy by laser additive manufacturing.

Porphyromonas gingivalis (P. gingivalis) is the most common species that causes peri-implantitis. It forms an irreversible dense biofilm and causes inflammation. A novel 3D-printed porous TC4-6Cu alloy was fabricated using selective laser melting (SLM) technology for the dental implant, which is anticipated to inhibit biofilm formation. We attempted to investigate the antibacterial ability and antibacterial mechanism of the 3D-printed porous TC4-6Cu alloy against P. gingivalis. This work used scanning electron microscopy (SEM) and laser confocal microscopy (CLSM) to detect the antimicrobial ability of the alloy against sessile P. gingivalis. The results indicated that the 3D-printed porous TC4-6Cu alloy could cause bacterial fragmentation and deformation. Plate antimicrobial counting experiments showed that the antibacterial rates of the alloy against adherent bacteria and planktonic bacteria after 24 h were 98.05% and 73.92%, respectively. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of Cu2+ were tested to appraise the antibacterial property of the alloy against planktonic P. gingivalis. The relationship between the antibacterial mechanism of the alloy with oxidative stress was evaluated through ROS fluorescence intensity and protein leakage concentration. The results revealed that the alloy significantly eliminated adherent bacteria and inhibited biofilm formation. Moreover, 3D-printed porous TC4-6Cu alloy demonstrated significant bactericidal ability by inducing the production of reactive oxygen species (ROS), which could result in protein leakage from the bacterial cell membrane. This research may open a new perspective on the development and biomedical applications for dental implantation.