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Objective To study the findings of 256‐slice CT angiography in atherosclerosis of the intracranial carotid arteary . Methods 402 cases of atherosclerotic lesions in the neck and brain were collected from 2013 to 2014 in our hospital ,in which there were 274 cases of male and 128 cases female .All patients underwent 256‐slice CT angiography of the head and neck .The incidence of atherosclerosis involvement in the intracranial carotid artery was analysed .The characteristics of carotid plaque on different sexes , age(≤60 years and >60 years) and location (right and left ) were observed .The length and property of plaque ,and stenostic rate of the artery were measured respectively .Results There were 164 cases (40 .80% ) of intracranial carotid artery athrosclerosis in 402 cases of atherosclerotic lesions of the neck and brain ,in which the incident rate of male was 38 .32% and female 46 .09% (χ2 =2 .182 ,P=0 .140) .The incident rate of >60 years group was more than that of ≤60 years group (χ2 0 .088) .And there were also no differences on lesion length (≤10 mm ,11-20 mm ,>20 mm) for different sexes or age groups (χ2 0 .091) . Calcified plaques were seen the most (79 .81% ) ,mixed plaques were seen more (15 .46% ) ,and lipid plaques (1 .58% ) and fibrous plaques (3 .15% ) were seen less .The mixed plaques were more seen on >60 years group than that on ≤60 years group for male pa‐tients (χ2 =12 .204 ,P=0 .001) ,but no other difference was found for plaque property in other different sexes or age groups .Mild stenosis of the corated artery was found in 54 .57% lesions ,moderate stenosis in 39 .75% and severe stenosis in 5 .68% ,and there were no any difference in different sexes or age groups (χ2 0 .151) .Conclusion CT angiography of the head and neck is of important significance for diagnosis of atherosclerosis in the intracranial carotid artery .
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Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.