Role of GSα-dependent signaling in bone homeostasis, condylar remodeling and enamel mineralization

The Dentin Matrix Protein (DMP1) is a critical regulator of bone and dentin mineralization and this protein is highly expressed in osteocytes and odontoblasts. Gs alpha (Gsα) protein, the main intracellular signal of a broad class of G-protein coupled receptors (GPCRs), is highly expressed in bone cells, including osteocytes. We and others have demonstrated that mice lacking the Gsα expression, predominantly in osteocytes (DMP1-GsαKO mice), develop severe osteopenia driven by a marked reduction in osteoblast activity associated with a significant increase in SOST/sclerostin expression. In this study, we have examined the role of Gsα in the jaws and teeth of DMP1-GsαKO mice to investigate if the absence of Gsα expression in osteocytes and odontoblasts altered teeth and jaws morphology. Our previous studies showed that DMP1-GsαKO leads to a significant decrease in both trabecular and cortical bone content in the skeleton, as assessed by μCT and histomorphometric analysis. Here we characterize the dental and craniofacial phenotype of DMP1-GsαKO mice. Results showed that DMP1-GsαKO had decreased total mandibular bone mineral density (BMD), total mandibular mineral content (BMC), condylar BMD and total tooth mineralization as assessed by DEXA using a Lunar PIXImus II densitometer. Furthermore, μCT analysis revealed that condylar bone volume and tooth mineralization is reduced in DMP1-GsαKO mice compared to control littermate. μCT also showed that the overall skull size and specifically the zygomatic bone is larger in the control group. Next, we examined H&E histological sections of the jaws of DMP1-GsαKO and control mice, which confirmed the osteopenic phenotype. Tartrate-resistant acid phosphatase (TRAP) staining showed that the number of TRAP-positive osteoclasts was increased in the DMP1-GsαKO mice compared to controls, suggesting increased bone resorption. In conclusion, our studies identified Gsa signaling in osteocytes and odontoblasts as important in maintaining normal bone and tooth homeostasis

Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists