Correlation between nutritional status and oral health quality of life, self-efficacy of older inpatients and the influencing factors.

OBJECTIVE: This study explores the relationship between nutritional status and oral health quality of life, the self-efficacy of older inpatients and the correlative factors. METHODS: In this study, the convenience sampling method was used to select 307 older inpatients in the southern section of the Renji Hospital affiliated to Shanghai Jiao Tong University School of Medicine from October to December 2020 as the main research participants. A mini nutritional assessment questionnaire was used to assess nutritional status, and the Chinese version of a geriatric oral health assessment index questionnaire was used to determine the oral health quality of life. Self-efficacy was assessed by a general self-efficacy scale questionnaire. Descriptive statistics were used to analyse data using the SPSS 22.0 software. Pearson correlation and multiple linear regression analysis were applied to explore the correlation between variables and factors concerned with nutritional status, respectively. RESULTS: The results of this study showed that the self-efficacy and oral health quality of life of older inpatients were at a moderate level. Among the patients, 263 had one or more tooth defects, and only 128 had oral restorations or wore dentures. The risk of malnutrition in hospitalised older patients was 37.1%, and the incidence of malnutrition was 13.4%. The risk factors of nutritional status of older patients were age, oral-related quality of life, prealbumin index, self-efficacy, chronic disease, monthly income and tooth defect (P < 0.05). CONCLUSION: The incidence of malnutrition and malnutrition risk in hospitalised older patients is relatively high. The main associated factors include age, tooth defect, oral health quality of life, self-efficacy, chronic disease status and monthly income. Therefore, older inpatients, especially those with prosthodontic problems, should carry out nutritional assessments, intervention and graded management as soon as possible to improve their self-efficacy, improve their nutrition and health status and reduce the incidence of a poor prognosis.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Association between spousal diabetes status and diabetic retinopathy in Chinese patients with type 2 diabetes.

PURPOSE: To evaluate the association between spousal diabetes status and the prevalence of diabetic retinopathy in Chinese patients with type 2 diabetes. METHODS: A cross-sectional community-based study was performed in 1510 patients with type 2 diabetes in Shanghai, China. Non-mydriatic digital fundus photography was used to detect diabetic retinopathy. Spousal diabetes status was assessed using a standardised interview questionnaire. RESULTS: The prevalence of diabetic retinopathy was significantly lower in patients who had diabetic spouses, compared with those who did not (20.2% vs 29.1%, p ⩽ 0.01). The fully adjusted odds ratio for diabetic retinopathy in those had diabetic spouses was decreased by 36% (odds ratio = 0.64, 95% confidence interval = 0.42-1.00, p = 0.048). The negative correlation between spousal diabetes status and diabetic retinopathy was presented in patients with the duration of diabetes ⩾ 10 years, those with HbA1c ⩾ 7% and those not using lipid-lowering drugs (odds ratio = 0.31, 95% confidence interval = 0.13-0.74, p = 0.0082; odds ratio = 0.50, 95% confidence interval = 0.27-0.94, p = 0.031; odds ratio = 0.58, 95% confidence interval = 0.37-0.92, p = 0.021, respectively). CONCLUSION: We demonstrated that spousal diabetes was associated with a lower diabetic retinopathy prevalence in Chinese patients with type 2 diabetes.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).