Objective To evaluate the health-related quality of life in convalescents of severe acute respiratory syndrome.Methods Health-related quality of life evaluated by SF-36 scale was performed on 68 SARS patients discharged from hospital in the first and sixth month during 2003-05～2003-12 in Department of Respiratory Medicine of Beijing Friendship Hospital.Results In the first month,SARS patients had a significant impairment in both physical and psychological functioning.Physical condition improved significantly six months later,but there was no obvious improvement in mental health.Conclusion Psychological treatment should be included.The SF-36 scale is a valid,sensitive and reliable tool for assessing the quality of life for SARS patients.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

Statistics show that 76.74% (4 688) of 6 109 patients with chronic wounds are those over 50 years of age; the proportion of patients with underlying diseases in all age groups above 50 years ranges from 78.25% to 100.00%; among the underlying diseases of chronic wound patients, the top four diseases are diabetes mellitus , cardiovascular and cerebrovascular diseases, hypertension, and respiratory diseases. The above underlying diseases and ages of patients are the susceptibility factors of corona virus disease 2019 released by National Health Commission of China. It is an unavoidable fact that patients with chronic wounds have to go to the hospital for treatment prescribed by the physician. At the same time, we found that there were not a few patients who go far afield because of various reasons when go to the hospital for treatment. During the period of epidemic prevention and control, this kind of "go far afield" style of seeking medical treatment may increase the exposure risk during transportation. Accordingly, we convened 36 wound care clinics in different regions in Shanghai to implement the "Five Measures" to encourage patients with chronic wounds to seek medical treatment proximately. The principle of this operation is that when seeking medical treatment, trying our best to reduce as much as possible the transportation distance for patients with chronic wounds to minimize the exposure risk during the epidemic period and eventually support the epidemic prevention and control campaign.