Objective To analyze the clinical, imaging characteristics and NOTCH3 mutations of cerebral autosomal dominant arteriopathy with the subcortical infarcts and leukoencephalopathy (CADASIL) in Henan, China.Methods CADASIL patients diagnosed by gene or biopsy in People′s Hospital of Zhengzhou University between 2012-2016 were recruited.Clinical and imaging features of these patients were analyzed retrospectively.The distribution of NOTCH3 gene mutations hotspots was described in Henan region at the same time.Results There were 37 patients from 19 families who were diagnosed as CADASIL by genetic testing or biopsy, 27 of whom had symptoms of CADASIL.Two families were confirmed by pathological examination and 17 by genetic testing.Of these 17 families, 13 mutations were found.Mutations in exon 11 were found in eight families, in exon 4 were detected in four families, and in exon 13 were found in two families.Mutation in exons 3, 8 and 20 was detected in one family respectively.Most patients presented with stroke and several presented with cognitive decline.Twelve patients had been attacked by risk factors.Magnetic resonance imaging (MRI) was performed on 22 patients.White-matter lesions were distributed in brain stem, basal ganglia, subcortical, temporal pole, external capsule.There were 19 patients with white-matter lesions in temporal pole and seven in capsula externa, showed as a high signal in T2WI.Conclusions CADASIL patients can be associated with risk factors.T2WI hyperintensities in the anterior temporal lobe were more common than that in the capsular external.Exon 11 and exon 4 were the hotspots for the NOTCH3 mutation in Henan patients.

Objective To investigate the clinical manifestations,imaging features,molecular genetic characteristics and possible pathogenic mechanisms of hereditary cerebral small vessel disease (CSVD) caused by heterozygous mutation of HtrA serine protease-1 (HTRA1) gene.Methods The clinical data of a Chinese Han family with CSVD carrying a heterozygous mutation of HTRA 1 gene,which came from the Department of Neurology,Henan Provincial People's Hospital in March 2018,were analyzed retrospectively.The clinical and radiographic features were summarized.Several high-throughput whole exon high-throughput sequencing was used to capture the mutation sites and the Sanger sequencing was used to validate the results.The family diagram was drawn and the 3D model construction and mutation function prediction were performed using silico tools.The relevant literature was reviewed and the pathogenesis was explored.Results The pedigree map showed that the family had an autosomal dominant inheritance pattern.Three generations of the family were investigated,and three family members in the same generation suffered from the disease.The first symptom of the proband was diplopia at the age of 39,accompanied by recurrent stroke,cognitive impairment and mood disorders,without alopecia.Head magnetic resonance imaging revealed bilateral diffuse,symmetric lesions,multiple lacunar infarcts,perivascular space,and microbleeds.The elder sister of the proband developed symptoms of left limb weakness at the age of 46,whose other clinical and imaging features were similar to those of the proband.The proband's mother died at the age of 59 due to repeated strokes.Whole exon sequencing indicated heterozygous missense mutation at c.821G＞A locus of HTRA1 gene in the proband and her 4th elder sibling,which was a new pathogenic mutation after consulting several mutation sites of databases.Function prediction suggested pathogenicity.Conclusions The heterozygous mutation of c.821G＞A in HTRA1 gene may lead to autosomal dominant CVSD.This genetic type should be given clinical attention.

Objective To analyze the clinical presentation , the mutation of the pathogenic genes and imaging features in a Chinese Han early-onset Alzheimer's disease pedigree.Methods A pedigree of Alzheimer's disease was collected.The DNA sequence of presenilin 1 (PSEN1), presenilin 2, micro-tubule associated protein tau ,β-amyloid precursor protein gene was analyzed , the clinical presentation , results of accessory examination , neuropsychological evaluation of the proband were investigated and the point mutations of some members of the family , 50 sporadic Alzheimer's disease patients , 50 normal controls were verified.Results The proband of the family appeared as language impairment , memory loss, personality change, repeated language, visuospatial impairment, mental and behavior disorder.The gene detection showed p.L226R mutation in the condon 226 in the exon 7 of PSEN1 gene of the proband and five other family members (Ⅲ1 ,Ⅲ2 ,Ⅲ4 ,Ⅲ6 ,Ⅲ7 ).The mother of the proband had the suspicious symptoms , and the sister and the brother of the proband had the similiar symptoms with the proband , all of whom died.Fifty sporadic Alzheimer'disease patients and 50 unrelated normal subjects did not have the mutation .The computed tomographic angiography showed that the brain blood vessels were normal and 18 F-fludeoxyglucose positron emission tomography (18F-FDG-PET) showed brain atrophy and hypometabolism in frontotemporal regions, parietal regions, hippocampal areas, however, the MRI, MRA and 18F-FDG-PET of the two mutation carriers (Ⅲ6 ,Ⅲ7 ) were all normal.Conclusion We reported a novel mutation in an early-onset Alzheimer's disease family presented as language impairment in the early stage of the disease , the p.L226R mutation of PSEN1, which may be a pathogenic mutation to cause the family's dementia.

OBJECTIVES: This study evaluated the association between obesity and glaucoma in middle-aged and older people. A population-based retrospective cohort study was conducted using data from the China Health and Retirement Longitudinal Study. METHODS: Glaucoma was assessed via self-reports. Multivariate logistic regression analysis and a Cox proportional hazards model were used to assess the relationship between obesity and glaucoma risk. RESULTS: Older males living in urban areas who were single, smokers, and non-drinkers were found to have a significantly higher incidence of glaucoma (all p<0.05). Diabetes, hypertension, and kidney disease were also associated with higher glaucoma risk, while dyslipidemia was associated with lower risk (all p<0.05). After the model was adjusted for demographic, socioeconomic, and health-related variables, obesity was significantly associated with a 10.2% decrease in glaucoma risk according to the Cox proportional hazards model (hazard ratio, 0.90; 95% confidence interval [CI], 0.83 to 0.97) and an 11.8% risk reduction in the multivariate logistic regression analysis (odds ratio, 0.88; 95% CI, 0.80 to 0.97). A further subgroup analysis showed that obesity was associated with a reduced risk of glaucoma in people living in rural areas, in smokers, and in those with kidney disease (all p<0.05). Obesity also reduced glaucoma risk in people with diabetes, hypertension, or dyslipidemia more than in healthy controls (all p<0.05). CONCLUSIONS This cohort study suggests that obesity was associated with a reduced risk of glaucoma, especially in rural residents, smokers, and people with kidney disease. Obesity exerted a stronger protective effect in people with diabetes, hypertension, or dyslipidemia than in healthy people.

In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.