A low calcium medium developed for epidermal keratinocytes were prepared according to the MCDB 153 modified formula and used in human epidermal keratinocyte culture compared with DMEM culture system. The observation by contrast microscopy and electron microscopy showed that in the low calcium medium keratinocytes grew as a monolayer of high proliferation and had many characteristics of basal cells, with a more rounded shape and large intercellular spaces. Increasing the calcium ion concentration in the medium or changing the other culture conditions the cells in these cultures could be induced stratification and terminal differentiation. The results suggest that the growth, proliferation and differentiation of cultured human epidermal keratinocytes can be controlled and regulated someway.

Citrulline which is a nonprotein amino acid,synthesizes the peptide bond,but it is not incorporated into proteins. It's used to be known as no point, but there is a difficult result that citrulline is related to many diseases in recent research.Citrulline is a biomaker in gastrointestinal injury,a supplement of arginine in arginine deficiency patients,and the evidence of sepsis.Citrulline,as an antigen among the process of its creation ,is one of the nosogenesis of rheumatoid arthritis,and checking the antibodies of citrulline is effective in diagnosing the rheumatoid arthritis early. Citrulline is also meaningful in diagnosing and treatment of some unfrequent diseases. Citrulline is widely used.

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled com-pound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unla-beled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and Bind-ingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 recep-tor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.