Objective　To evaluate the expression of Fas/FasL antigen on peripheral blood T lymphocytes and its possible role in Behcet disease. Methods　The expression of Fas and FasL antigen on peripheral blood T lymphocytes was determined by flow cytometry and three-colour double marked immunofluorescence methods in 26 patients with Behcet disease and 43 healthy individuals. Results　The difference was significant between Behcet disease (25.70%±7.32%) and controls (14.02%±6.30%) concerning the Fas expression on CD4+ T lymphocytes(P＜0.01) . But no difference was found concerning the expression of FasL antigen between Behcet disease and controls (P＞0.05).The expression of Fas antigen on CD8+ T lymphocytes from Behcet disease (9.47%±6.97%)was significantly higher than that in control group(3.47%±2.75%), but no difference was found concerning FasL antigen expression on CD8+ T lymphocytes between Behcet disease and controls(P＞0.05).Conclusion　These results indicate the imbalance of the expression of Fas and FasL on T lymphocytes in Behcet disease is responsible for the perpetuation and recurrence of Behcet disease for the activated lymphocytes would not be eliminated through apoptosis mediated by Fas/FasL system.

OBJECTIVE To explore the effect of smearing catheter surface with chloramphenicol for preventing catheter -associated urinary tract infections. METHODS Totally 100 cases of preoperative patients needed for indwelling urethral catheters were randomly grouped, 50 of 100 cases as test group, and the others 50 cases as the control group. Catheters after smearing surface with chloramphenicol were inserted using aseptic technique in the test group, urinary catheters without using chloramphenicol were inserted in the control group according to routine aseptic technique protocol, and then regularly taken out urinary specimen from two groups respectively for microbial culture. RESULTS The observation showed bacterial growth positive rate was 30%, and 66.7%, respectively in the control group, but positive rate was 6.7%, and 30%, respectively in the test group after the seventh day and the tenth day. There was a statistic significant difference (P

The M2 ion channel protein is an important target against influenza A virus. In this study, H5N1 influenza A virus M2 ion channel (H5M2) gene was cloned into pcDNA4 vector. The HEK293 stable cell line expressing H5M2 was successfully established. The expression of H5M2 ion channel protein was induced only by tetracycline and confirmed by imuunofluorescence and Western blot. The ion channel activity of H5M2 was confirmed by whole cell patch-clamp recording. Fifty micromol per liter amantadine blocked the H5M2 channel conductance completely in HEK293 cells. This stable cell line may provide a model for screening inhibitors of M2 ion channel.
Base Sequence ; Cell Line ; Humans ; Influenza A Virus, H5N1 Subtype ; metabolism ; Ion Channels ; antagonists & inhibitors ; Kidney ; cytology ; Molecular Sequence Data ; Patch-Clamp Techniques ; Viral Matrix Proteins ; biosynthesis ; genetics

Objective:To develop a prototype artificial intelligence image recognition system for detecting dental caries, especially those without cavities, in children.Methods:Seven hundred and twelve intraoral photos, which were taken by dental professionals using a digital camera from October 2013 to June 2020 in the Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, were collected from the children who received dental treatment under general anesthesia. The well-documented post-treatment electronic dental record of each child was identified as label standard to determine whether the teeth were carious and the type of caries types such as caries that had become cavities (caries with cavities), pit and fissure caries that had not become cavities (pit and fissure caries) and proximal caries which the marginal ridge enamel had not been destroyed (proximal caries). The various teeth and caries types were labeled by pediatric dentists using VoTT software (Windows 2.1.0, Microsoft, U S A). There were five labeled groups: pit and fissure caries, approximal caries, non-carious approximal surfaces, caries with cavities and teeth without caries (including intact fillings). Each group was randomly divided into training dataset, validation dataset and test dataset at a ratio of 6.4∶1.6∶2.0 by using random number table. After using the labeled training dataset for deep learning training, a deep learning-based artificial intelligence (AI) image recognition system for detecting dental caries was established, with the caries probability greater than 50.0% as the criterion for determining caries. Sensitivity and accuracy were used as indicators of recognition specificity.Results:Seven hundred and twelve single-jaw intraoral photographs were segmented and annotated into 953 pit and fissure caries, 1 002 approximal caries, 3 008 caries with cavities, 3 189 teeth without caries and 862 non-carious approximal surfaces, totaly 9 014 labels. The sensitivities and specificities of the test set were 96.0% and 97.0% for caries with cavities, 95.8% and 99.0% for pit and fissure caries and 88.1% and 97.1% for approximal caries.Conclusions:The current AI system developed based on deep learning of the intra-oral photos in the present study showed the ability to detect dental caries. Furthermore, the AI system could accurately verify different types of dental caries such as caries with cavities, pit and fissure caries and proximal caries.