PBL is a new teaching method.Applying it to the teching of distribution of bacterias can fully display students'subjective initiative and have good teaching effect ant at the same time build the new model of PBL in medical education.

Cellular prion protein (PrP(C)) is ubiquitously expressed in the cytomembrane of a considerable number of eukaryotic cells. Although several studies have investigated the functions of PrP(C) in cell proliferation, cell apoptosis, and tumorigenesis of mammals, the correlated functions of chicken PrP(C) (chPrP(C)) remain unknown. In this study, stable chPrP(C)-downregulated Marek's disease (MD) virus-transformed avian T cells (MSB1-SiRNA-3) were established by introducing short interfering RNA (SiRNA) targeting chicken prion protein genes. We found that downregulation of chPrP(C) inhibits proliferation, invasion, and migration, and induces G1 cell cycle phase arrest and apoptosis of MSB1-SiRNA-3 cells compared with Marek's disease virus-transformed avian T cells (MSB1) and negative control cells. To the best of our knowledge, the present study provides the first evidence supporting the positive correlation between the expression level of chPrP(C) and the proliferation, migration, and invasion ability of MSB1 cells, but appears to protect MSB1 cells from apoptosis, which suggests it functions in the formation and development of MD tumors. This evidence may contribute to future research into the specific molecular mechanisms of chPrP(C) in the formation and development of MD tumors.
Animals ; Apoptosis* ; Carcinogenesis ; Cell Cycle ; Cell Proliferation ; Chickens ; Down-Regulation* ; Eukaryotic Cells ; Mammals ; Marek Disease* ; RNA, Small Interfering ; T-Lymphocytes*

OBJECTIVE: To screen proteins interacting with ring finger protein 216(RNF216) through yeast two hybrid experiment, and further clarify the role of RNF216 in the pathogenesis of gonadotropin-releasing hormone deficiency. METHODS: A recombinant expression vector pGBKT7-RNF216 was constructed and transformed into yeast Y2HGold, which was hybridized with a human cDNA library in order to screen proteins interacting with RNF216. The interaction was verified in yeast Y2HGold. RESULTS: A recombinant expression vector pGBKT7-RNF216 was successfully constructed and expressed in yeast Y2HGold. Filamin B (FLNB) was identified by yeast two hybrid experiment, and their interaction was verified in yeast Y2HGold. CONCLUSION An interaction between FLNB and RNF216 was identified through yeast two hybrid experiment. RNF216 may affect the proliferation and migration of GnRH neurons by regulating FLNB or FLNB/FLNA heterodimers.
Gene Library ; Gonadotropin-Releasing Hormone/genetics* ; Humans ; Proteins ; Two-Hybrid System Techniques ; Ubiquitin-Protein Ligases/genetics*

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.