After the functions and requirements of medical information officers and the distribution of enterprises were described, the problems in domestic education of medical informatics ( such as insufficient class hours for medical course and unclear orientation of subjects) were analyzed according to the status quo in training of medical informatics students, and some suggestions were put forward for the orientation of medical informatics education in China.

Aim To investigate the effect and mecha-nism of valproic acid on neuroglobin expression, and the neuroprotective role of valproic acid against H2 O2-induced neurotoxicity. Methods Western blot, RT-PCR and luciferase assay were used to detect the pro-tein levels, mRNA levels and promoter activity of mouse and human neuroglobin induced by valproic acid. Luciferase assay was used to investigate the role of transcription factor CREB in the up-regulation of neuroglobin by valproic acid. MTT assay was used to evaluate the effect of valproic acid against H2 O2-in-duced neurotoxicity. Results VPA treatment marked-ly increased the protein levels, mRNA levels and pro-moter activity of Ngb in mouse N2 a cells and human SKNSH cells. CREB specific inhibitor KG501 or CREB dominant negative mutant KCREB attenuated VPA-induced Ngb promoter activity. VPA could pro-tect N2a cells from H2 O2-induced neurotoxicity. Con-clusion CREB mediates VPA-induced Ngb up-regula-tion, which may contribute to the neuroprotective effects of VPA in oxidative stress in neurons.

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.