Psoralen inhibits RAW264.7 differentiation into osteoclasts and bone resorption by regulating CD4+T cell differentiation / 中国中药杂志

This paper aimed to investigate whether psoralen inhibits the differentiation and bone resorption by regulating CD4+T cell differentiation in RANKL-induced osteoclastogenesis in RAW264.7 cells, and elucidate its mechanism for osteoporosis. CD4+T cells were isolated from spleen cells of Balb/c mice by immunomagnetic separation method. The cells were divided into blank control group and psoralen group. The cells were cultured in 24-well plates and cultured for 3 days, and then they were collected for co-culture experiments after 4 days. Co-culture experiments were divided into RAW264.7 cell group, psoralen+RAW264.7 cell group, without psoralen treatment of CD4+T cells+RAW264.7 cell group, psoralen treatment of CD4+T cells+RAW264.7 cell group. After 5 days of co-culture, TRAP staining was used to detect the number of osteoclasts, and after 8 days of co-culture, bone resorption was evaluated by toluidine blue staining. The expressions of RORγt, Foxp3, IL-17, TNF-α, TGF-β and IL-10 in CD4+T cells and osteoclast differentiation-related genes MMP-9, TRAP and Cat-K were detected by Real-time polymerase chain reaction (RT-PCR); ELISA kit was used to detect IL-17, TNF-α, TGF-β and IL-10 and other cytokines levels. Our data confirmed that the psoralen significantly promoted the expression of Foxp3, TGF-β and IL-10 in CD4+T, and inhibited the expression of RORγt, IL-17 and TNF-α in CD4+T, the CD4+T cells without treatment by psoralen can significantly promote RANKL-induced differentiation of RAW264.7 to osteoclasts, and psoralen treatment of CD4+T can significantly inhibit RANKL-induced RAW264.7 osteoclast differentiation and bone resorption. Taken together, psoralen inhibits the differentiation and bone resorption of RAW264.7 into osteoclasts by promoting the development of CD4+ CD25+ Treg/Th17 balance in CD4+T cells to CD4+CD25+T.

Automatic detection and diagnosis of lung nodules on CT images based on LDA and SVM / 南方医科大学学报

Based on suspected pulmonary nodule segmentation images obtained previously and with a large-sample training, automatic detection and diagnosis of the pulmonary nodules on CT images was realized by extracting the multi-dimensional features of the pulmonary nodule images and the application of LDA and SVM statistical classifiers. Experimental results showed that this detection and diagnosis method produced better classification results, and is practical for application in CAD systems.