Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

Periodontal tissue regeneration is the ultimate goal of the treatment for periodontitis-affected teeth. The success of regenerative modalities relies heavily on the utilization of appropriate biomaterials with specific properties. Poly (lactic-co-glycolic acid) (PLGA), a synthetic aliphatic polyester, has been actively investigated for periodontal therapy due to its favorable mechanical properties, tunable degradation rates, and high biocompatibility. Despite the attractive characteristics, certain constraints associated with PLGA, in terms of its hydrophobicity and limited bioactivity, have led to the introduction of modification strategies that aimed to improve the biological performance of the polymer. Here, we summarize the features of the polymer and update views on progress of its applications as barrier membranes, bone grafts, and drug delivery carriers, which indicate that PLGA can be a good candidate material in the field of periodontal regenerative medicine.

The repair of infected bone defects is still challenging in the fields of orthopedics, oral implantology and maxillofacial surgery. In these cases, the self-healing capacity of bone tissue can be significantly compromised by the large size of bone defects and the potential/active bacterial activity. Infected bone defects are conventionally treated by a systemic/local administration of antibiotics to control infection and a subsequent implantation of bone grafts, such as autografts and allografts. However, these treatment options are time-consuming and usually yield less optimal efficacy. To approach these problems, novel biomaterials with both antibacterial and osteoinductive properties have been developed. The antibacterial property can be conferred by antibiotics and other novel antibacterial biomaterials, such as silver nanoparticles. Bone morphogenetic proteins are used to functionalize the biomaterials with a potent osteoinductive property. By manipulating the carrying modes and release kinetics, these biomaterials are optimized to maximize their antibacterial and osteoinductive functions with minimized cytotoxicity. The findings, in the past decade, have shown a very promising application potential of the novel biomaterials with the dual functions in treating infected bone defects. In this review, we will summarize the current knowledge of novel biomaterials with both antibacterial and osteoinductive properties.

Advanced glycation end products (AGEs) can stimulate osteoblast apoptosis and have a critical role in the pathophysiology of diabetic osteoporosis. Mitochondrial abnormalities are closely related to osteoblast dysfunction. However, it remains unclear whether mitochondrial abnormalities are involved in AGE-induced osteoblastic cell apoptosis. Silibinin, a major flavonolignan compound of silimarin, has strong antioxidant and mitochondria-protective properties. In the present study, we explored the possible mitochondrial mechanisms underlying AGE-induced apoptosis of osteoblastic cells and the effect of silibinin on osteoblastic cell apoptosis. We demonstrated that mitochondrial abnormalities largely contributed to AGE-induced apoptosis of osteoblastic cells, as evidenced by enhanced mitochondrial oxidative stress, conspicuous reduction in mitochondrial membrane potential and adenosine triphosphate production, abnormal mitochondrial morphology, and altered mitochondrial dynamics. These AGE-induced mitochondrial abnormalities were mainly mediated by the receptor of AGEs (RAGE). In addition, we found that silibinin directly downregulated the expression of RAGE and modulated RAGE-mediated mitochondrial pathways, thereby preventing AGE-induced apoptosis of osteoblastic cells. This study not only provides a new insight into the mitochondrial mechanisms underlying AGE-induced osteoblastic cell apoptosis, but also lays a foundation for the clinical use of silibinin for the prevention or treatment of diabetic osteoporosis.