Objective To investigate the clinic manifestations of intestinal involvement of Beh(c)et's disease (BD).Methods Medical data of patients with intestinal involvement of BD admitted to Peking Union Medical College Hospital (PUMCH) from January 1994 to August 2013 were retrospectively reviewed.Clinic manifestations,laboratory tests and endoscopic characteristics were analyzed,and the influence of sex on the clinic manifestations was investigated.Numerical data and categorical data comparisons were analyzed using t-test,x2 test respectively.Results Sixty-four patients with intestinal involvement accounted for 10.5 percent (64/611) of total hospitalized BD patients during the same period.Among these 64 patients,31 were male and 33 were female.The median age at the onset of intestinal involvement was 34 years old.Gastrointestinal symptoms were presented after other systemic symptoms of BD in 91％ patients (58/64).Abdominal pain (88％,56/64) was the most common clinical manifestations of intestinal involvement,and severe complications such as gastrointestinal bleeding (31％,20/64),intestinal obstruction (22％,14/64),intestinal perforation (11％,7/64) and intestinal fistula (16％,10/64) could also occur in some patients.Lesions usually located at ileocecum portion of the gastrointestinal tract.The common endoscopic manifestation was deep ulcers.Female sex was associated with higher C reactive protein level and lower serum albumin level [(52±46) vs (27±36) mg/L,t=2.287,P=0.026; Serum albumin:(34±6) vs (37±5) mg/L,t=2.237,P=0.029].After treated with glucocorticoid,im-munosuppressant and TNF-α blockers,62 patients achieved clinical remission while 21 cases were operated.Conclusion Most of intestinal involvement of BD patients are young adults,and the gastrointestinal symp-toms usually present after the presence of other systemic symptoms.Abdominal pain is the most common manifestations,while severe complications such as gastrointestinal bleeding and intestinal obstruction can also occur.The most common endoscopic findings are deep ulcers,which often located in the ileocecal region.Female patients are more likely to have severe clinical course.Glucocorticoid,immunosup-pressant and TNF-α antagonist therapy are effective,but some patients still need surgical intervention.

Objective:To evaluate the efficacy of artificial intelligence assisted pulmonary nodule diagnosis system in detection pulmonary nodule and predicting the malignant probability of pulmonary nodule.Methods:A retrospectively analyze the clinical data of 199 patients with lung nodules in the Thoracic Surgery Department of Lanzhou University Second Hospital from May 2016 to July 2020. The preoperative chest CT was imported into the artificial intelligence system to record the detected lung nodules, to measure nodal diameter and density classification and malignant probability prediction value of each nodule. The detection rate of pulmonary nodules by artificial intelligence system was calculated, and the sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of artificial intelligence system in the differential diagnosis of benign and malignant pulmonary nodules were calculated and compared with manual film reading. and the sensitivity and specificity in the differential diagnosis of benign and malignant pulmonary nodules under the condition of different size and density of pulmonary nodules.Results:A total of 204 pulmonary nodules were pathologically diagnosed by surgical resection, and the detection rate of pulmonary nodules by artificial intelligence system was 100%. The artificial intelligence system can distinguish benign and malignant pulmonary nodules with a sensitivity of 95.83%(95% CI: 0.8967-0.9883), specificity 25.00%(95% CI: 0.1717-0.3425), and a positive likelihood ratio of 1.27(95% CI: 1.14-1.44), negative likelihood ratio 0.17(95% CI: 0.06-0.46), Manual reading for the differentiation of benign and malignant pulmonary nodules has a sensitivity of 87.36%(95% CI: 0.7850-0.9352), specificity 72.17%(95% CI: 0.6214-0.8079), and a positive likelihood ratio of 3.14(95% CI: 2.26-4.37), the negative likelihood ratio is 0.18(95% CI: 0.10-0.31). 5mm≤diameter of pulmonary nodule<10 mm, sensitivity 100%(95% CI: 0.6637-1.0000), specificity 50.00%(95% CI: 0.01258-0.98740), 10 mm≤diameter of pulmonary nodule <20 mm, sensitivity 94.29%(95% CI: 0.8084-0.9930), specificity 29.83%(95% CI: 0.1843-0.4340), 20 mm≤ diameter of pulmonary nodule ≤30 mm, sensitivity 96.15%(95% CI: 0.8679-0.9953), specificity 18.37%(95% CI: 0.0876-0.9953), sensitivity of subsolid lung nodules: 100%(95% CI: 0.9051-1.0000), specificity 20.00%(95% CI: 0.0051-0.7164), solid lung nodule sensitivity 93.22%(95% CI: 0.8354-0.9812), specificity 25.24%(95% CI: 0.1720-0.3476). Conclusion:The artificial intelligence assistant diagnosis system of pulmonary nodules has a strong performance in the detection of pulmonary nodules, but it can not meet the clinical requirements in the differentiation of benign and malignant pulmonary nodules. At present, the artificial intelligence system can be used as an auxiliary tool for doctors to detect pulmonary nodules and assist in the diagnosis of benign and malignant pulmonary nodules.

Objective    To explore the mechanism of DDX46 regulation of esophageal squamous cell carcinoma. Methods    Picture signals of fluorescence in gene array were scanned and differential expression of gene in two groups (a DDX46-shRNA-LV group and a control-LV group) were compared by GCOSvL.4 software. These differential expressed genes were analyzed by bioinformatics methods finally, and validated by quantitative real time polymerase chain reaction (qRT-PCR) analysis. Results    According to the screening criteria of fold change ≥2 and P<0.05, 1 006 genes were differentially expressed after DDX46 knockdown, including 362 up-regulated and 644 down-regulated genes. Bioinformatics analysis and gene co-expression network building identified that these differentially expressed genes were mainly involved in cell cycle, proliferation, apoptosis, adhesion, energy metabolism, immune response, etc. Phosphatidylinositol 3-kinase (PI3K) was the key molecule in the network. The results of RT-qPCR were completely consistent with the results of gene microarra. Conclusion    Bioinformatics can effectively exploit the microarray data of esophageal squamous cell carcinoma after DDX46 knockdown, which provides a valuable clue for further exploration of DDX46 tumorigenesis mechanism and helps to find potential drug therapy.