Objective To construct recombinant lentiviral vectors of porcine bone morphogenetic protein 2 (BMP-2) gene and to detect BMP-2 gene activity and bone marrow mesenchymal stem cells (BMSCs) osteogenetic differentiation so as to lay a foundation of the further study of osteochondral tissue engineering. Methods BMSCs were isolated from bone marrow of 2-month-old Bama miniature porcines (weighing, 15 kg), and the 2nd generation of BMSCs were harvested for experiments. The porcine BMP-2 gene lentiviral vector was constructed by recombinant DNA technology and was used to transfect BMSCs at multiplicity of infection (MOI) of 10, 25, 50, 100, and 200, then the optimal value of MOI was determined by fluorescent microscope and inverted phase contrast microscope. BMSCs transfected by BMP-2 recombinant lentiviral vectors served as experimental group (BMP-2 vector group); BMSCs transfected by empty vector (empty vector group), and non-transfected BMSCs (non-transfection group) were used as control groups. RT-PCR, immunohistochemistry staining, and Western blot were performed to detect the expressions of BMP-2 mRNA and protein. Then the BMSCs osteogenesis was detected by alkaline phosphatase (ALP) staining, ALP activities, and Alizarin red staining. Results The recombinant lentiviral vectors of porcine BMP-2 gene was successfully constructed and identified by RT-PCR and gene sequencing, and BMSCs were successfully transfected by BMP-2 recombinant lentiviral vectors. Green fluorescent protein could be seen in the transfected BMSCs, especially at MOI of 100 with best expression. The immunohistochemistry staining and Western blot showed that BMSCs transfected by BMP-2 recombinant lentiviral vectors could express BMP-2 protein continuously and stably at a high level. After cultivation of 2 weeks, the expression of ALP and the form of calcium nodules were observed. Conclusion The porcine BMP- 2 gene lentiviral vector is successfully constructed and transfected into the BMSCs, which can express BMP-2 gene and protein continuously and stably at a high level and induce BMSCs differentiation into osteoblasts.
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.