Objective To discuss the role of heparan sulfate (HS) in bone formation and bone remodeling and summarize the research progress in the osteogenic mechanism of HS. Methods The domestic and abroad related literature about HS acting on osteoblast cell line in vitro, HS and HS composite scaffold materials acting on the ani-mal bone defect models, and the effect of HS proteoglycans on bone development were summarized and analyzed. Results Many growth factors involved in fracture healing especially heparin-binding growth factors, such as fibroblast growth factors, bone morphogenetic protein, and transforming growth factor β, are connected noncovalently with long HS chains. HS proteoglycans protect these proteins from protease degradation and are directly involved in the regulation of growth factors signaling and bone cell function. HS can promote the differentiation of stem cells into osteoblasts and enhance the differentiation of osteoblasts. In bone matrix, HS plays a significant role in promoting the formation, maintaining the stability, and accelerating the mineralization. Conclusion The osteogenesis of HS is pronounced. HS is likely to become the clinical treatment measures of fracture nonunion or delayed union, and is expected to provide more choices for bone tissue engineering with identification of its long-term safety.
【摘要】目的 探索與實踐臨床藥學(七年制)專業的建設,以培養具有醫藥學專門知識的高級臨床藥師。方法 在山東大學首辦七年制臨床藥學專業,通過專業課程建設、畢業實習環節、加強專業教學和實習管理、增進交流等,拓寬辦學思路。結果 成功開辦了臨床藥學(七年制)專業,但專業有以下不足:①社會了解度不夠,學生專業思想不穩固；②課程設置有缺陷；③臨床課教學和實習質量有待提高。結論 七年制臨床藥學專業在山東大學的成功開辦,在國內是有較強的示范性和試驗性意義。但專業在社會了解度、課程設置、臨床課教學和實習質量上有待提高。
O6-carboxymethyl guanine(O6-CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant Mycobacterium smegmatis porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machine learning technology has been widely used in the analysis of nanopore sequencing data. But the machine learning always need a large number of data labels that have brought extra work burden to researchers, which greatly affects its practicability. Accordingly, this paper proposes a nano-Unsupervised-Deep-Learning method (nano-UDL) based on an unsupervised clustering algorithm to identify methylation events in nanopore data automatically. Specially, nano-UDL first uses the deep AutoEncoder to extract features from the nanopore dataset and then applies the MeanShift clustering algorithm to classify data. Besides, nano-UDL can extract the optimal features for clustering by joint optimizing the clustering loss and reconstruction loss. Experimental results demonstrate that nano-UDL has relatively accurate recognition accuracy on the O6-CMG dataset and can accurately identify all sequence segments containing O6-CMG. In order to further verify the robustness of nano-UDL, hyperparameter sensitivity verification and ablation experiments were carried out in this paper. Using machine learning to analyze nanopore data can effectively reduce the additional cost of manual data analysis, which is significant for many biological studies, including genome sequencing.