Dietary supplementation of Huangshan Maofeng green tea preventing hypertension of older C57BL/6 mice induced by desoxycorticosterone acetate and salt

Evaluation and Analysis of Elderly Mental Health Based on Artificial Intelligence

Objective

The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly.

Method

In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05).

Huangshan Maofeng Green Tea Extracts Prevent Obesity-Associated Metabolic Disorders by Maintaining Homeostasis of Gut Microbiota and Hepatic Lipid Classes in Leptin Receptor Knockout Rats

Huangshan Maofeng green tea (HMGT) is one of the most well-known green teas consumed for a thousand years in China. Research has demonstrated that consumption of green tea effectively improves metabolic disorders. However, the underlying mechanisms of obesity prevention are still not well understood. This study investigated the preventive effect and mechanism of long-term intervention of Huangshan Maofeng green tea water extract (HTE) on obesity-associated metabolic disorders in leptin receptor knockout (Lepr^−/−) rats by using gut microbiota and hepatic lipidomics data. The Lepr^−/− rats were administered with 700 mg/kg HTE for 24 weeks. Our results showed that HTE supplementation remarkably reduced excessive fat accumulation, as well as ameliorated hyperlipidemia and hepatic steatosis in Lepr^−/− rats. In addition, HTE increased gut microbiota diversity and restored the relative abundance of the microbiota responsible for producing short chain fatty acids, including Ruminococcaceae, Faecalibaculum, Veillonellaceae, etc. Hepatic lipidomics analysis found that HTE significantly recovered glycerolipid and glycerophospholipid classes in the liver of Lepr^−/− rats. Furthermore, nineteen lipid species, mainly from phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and triglycerides (TGs), were significantly restored increases, while nine lipid species from TGs and diglycerides (DGs) were remarkably recovered decreases by HTE in the liver of Lepr^−/− rats. Our results indicated that prevention of obesity complication by HTE may be possible through maintaining homeostasis of gut microbiota and certain hepatic lipid classes.