To observe the influence of the placental apoptosis on the expression of Bax,Bcl-2, Fas, FasLand TNF-α during the second trimester of pregnancy, mice of experimental group were intraperitoneal injected with 100 purified Toxoplasma gondii tachyzoites added in 0.2mL of PBS, while those of the control group were injected with 0.2 mL of sterile PBS (0.01 mol/L, pH 7.4) in the 8-th day of pregnancy. During the 12, 14, 16 and 18-th days of pregnancy, 5 mice both in experimental and control group were randomly killed and the expression levels of the apoptosis-related proteins Bax, Bcl-2, Fas, FasL and TNF-α in the placental tissues were determined by means of immunohistochemical methods. It was showed that the apoptosis-related protein expressed both in villus and decidua of the placenta, most of which were expressed in syneytiotrophoblast (ST). The positive cells with expression of Bax, Fas, FasL and TNF-α increased along with the increase of the pregnant days in both the experimental group and the control group, and the positive cells with expression of Bcl-2 decreased along with the increase of the pregnant days. It was also demonstrated that the positive cells with expression of Bax, Fas, FasL and TNF-α of the experimental group showed a higher percentage of expression than that of the control group on the same pregnant days, but the positive cells with Bcl-2 expression of the experimental group were fewer than that of the control group. It is concluded that the expression of apoptosis-related protein Bax, Bcl-2, Fas, FasL and TNF-α in the placenta were altered when the pregnant mice were infected with Toxoplasma gondii during the second trimester, which may induce the apoptosis through the endogenic and ectogenic pathway.

With the arrival of the era of big data, increasing attention has been drawn to the application of artificial intelligence (AI) in the medical field. AI has many advantages, such as objectivity, accuracy, minimal invasiveness, time savings and high efficiency. Therefore, the combination of AI with dental diagnosis and treatment can help dentists improve work efficiency and save medical resources, offering potential significant benefits for dental application. At present, AI has been gradually integrated into prosthodontics, oral and maxillofacial surgery, orthodontics, endodontics and periodontics. The AI system can realize automatic tooth preparation, automatic tooth arrangement and implantology. Deep learning can be used to assist in diagnosing maxillary sinus inflammation, predicting the complications of tooth extraction and improving the accuracy of osteotomy. The AI system can also provide significant clues for the diagnosis, treatment and prognosis of oral and maxillofacial tumors. The breakthrough brought by AI in cephalometric and the assessment of facial attractiveness of patients has promoted the development of intelligent and personalized orthodontic treatment. Deep learning and analysis of medical images also promote the accuracy of root canal therapy as well as the diagnosis and treatment of periodontal diseases. AI technology has realized the leap from digitalization to automation and intelligence in oral diagnosis and treatment, and its application potential in the oral field should not be underestimated. Based on the concepts of AI, this paper will focus on the application of artificial intelligence in various oral clinical fields and briefly introduce its advantages, problems and future.