The pivotal role of autophagy in bone cells:bone-related cell activity and bone metabolism / 中国组织工程研究

BACKGROUND:The importance of autophagy for maintaining cellular homeostasis and stress response has long been recognized.As a way for cells to selectively clear their damaged organelles to achieve the recycling of cellular components,autophagy has a pivotal role in bone metabolism.OBJECTIVE:To review the role and possible mechanisms of autophagy in regulating bone-related cell activity and function among bone marrow mesenchymal stem cells,osteoblasts,osteocytes,and osteoclasts.METHODS:PubMed was searched for studies related to autophagy using the keywords of "autophagy;bone marrow mesenchymal stem cells;osteoblasts;osteocytes;osteoclasts."RESULTS AND CONCLUSION:We finally included 84 papers.Autophagy plays an important role in bone metabolism.Autophagy is involved in maintaining the balance between mineralization and absorption,and then maintaining bone homeostasis.An appropriate autophagy inducer may also benefit bone remodeling.Abnormal autophagy can lead to disorders of bone balance,leading to diseases such as osteoporosis.We may prevent or treat bone-related diseases by regulating the level of autophagy as its function in maintaining the balance of mineralization and resorption in bone homeostasis.

Research on convolutional neural network and its application on medical image / 生物医学工程学杂志

Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.