Objective: To investigate the expression of co-stimulatory molecule CD86 and inducible costimulator(ICOS) in the intestinal mucosa of Crohn disease (CD) and to exlpore its pathologic significance. Methods: Expression of co-stimulator CD86 and ICOS was examined by immunohistoehemistry on paraffin embedded tissue from patients with CD (30 cases) and normal controls (20 cases). The subsets of lamina propria mononuclear cells (LPMC) were also analysed via immunostaining for CD4, CD8 and CD20. Results: Increased amount of CD86 or ICOS positive LPMC was observed in the lesional area of CD when compared with the essentially normal area of CD and normal controls (q = 9. 23 ,P ＜0. 01 and q =5. 46 ,P＜0. 01). In addition, the expression of CD86 or ICOS was higher in intestinal epithelium of CD than that in normal controls (H = 24. 93, P＜0. 01 and H = 4. 66, P＜0. 01 ) , whereas no significant difference was seen between the diseased and the essentially normal area of CD. The amount of CD4 or CD8 positive lymphocytes in lamina propria, epithelium and small vascular walls was also significantly increased in CD than that in normal controls (P＜0. 05 or P＜0. 01). Conclusion: Increased amount of CD86 or ICOS positive LPMC and enterocytes in CD suggests that co-stimulatory molecules may play a role in the pathogenesis of CD. The enterocytes may act as non-specific antigen presenting cells in the process of cellular immunity activation in CD.

Objective To evaluate antimicrobial effect and mechanism of meropenem in the model of PA infection by C.elegans.Methods To evaluate drug effects of PA infection with caenorhabditis elegans by different concentrations of culture medium, determinate the lethal rate of C.elegans.Western blot detected mitogen activated protein kinase ( Mitogen-activated protein kinase MAPK ) activity change, and PCR detected antimicrobial peptide genes expression in C.elegans after PA infection,the effect of meropenem on MAPK activity change and antimicrobial peptide genes expression.Results Compared with the control group (OP-50), the death rate of C.elegans in PA infection group changed significantly (P<0.01). Meropenem showed protective effect after C.elegans infection ( P <0.01 ) .Detection of MAPK kinase activity showed that PA infection caused PMK-1 kinase activation, further study showed that antibiotics meropenem did not affect the activation of PMK-1 kinase (no significant difference).C.elegans antimicrobial peptide gene Lys-1, clec-85, F55G11.7, K08D8.5 activity increased in PA infection (P<0.01).Meropenem promoted the expression of the antimicrobial peptide gene increased (P<0.01),with synergistic effects.Conclusion Our results show that a C.elegans pathogenicity model can be applied screening drug susceptible to pathogens infection quickly and easily.

Objective To study the effect of fish skin collagen peptide on skin collagen metabolism.Methods Twenty-two rots were divided into treatment and control groups.In rats of the treatment group,fish skin collagen peptide at a dose that is 20 times of the recommended dose for human was added to the food for 40 days. Skin HYP was isolated by hydrolysis with hydrochloric acid. The concentration of skin HYP was determined by visible spectrophotometer and converted it to collagen concentration and compared between the 2 groups.Results The skin collagen concentration was significantly higher in the treatment group than that in the control group (69.79%±4.36%;64.36%±4.36%,P＜0.05).At 40 days,the average body weight of rats in the treatment group was (32.1±3.16)g;while it was(36.5± 2.19)g in the control group (P＜0.05).Conclusion Fish skin collagen peptide may help to increase collagen concentration of skin and control body weight.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients' physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.
Bone Density ; Deep Learning ; Diabetes Mellitus/epidemiology* ; Female ; Fractures, Bone/etiology* ; Humans ; Osteoporosis/complications* ; Risk Factors