Effect of Grape Seed Proanthocyanidins on Fat Metabolism and Adipocytokines in Obese Rats.

This study aimed to investigate the effect of Grape Seed Proanthocyanidin (GSP) on fat metabolism and adipocytokines in obese rats. Fifty 5-week-old rats were randomly assigned to five groups (n = 10 per group) and given either a basal diet, a high-fat diet, or a high-fat diet supplemented with GSP (25, 50, and 100 mg/d) per group. The experiment lasted for five weeks, including a one-week adaptation period and a four-week treatment period. At the end of the experimental period, serum and adipose tissue samples were collected and analyzed. Additionally, we co-cultured 3T3-L1 preadipocytes with varying concentrations of GSP to explore its effect on adipocyte metabolism. The results demonstrated that GSP supplementation reduced weight, daily gain, and abdominal fat weight coefficient (p < 0.05). It also decreased levels of glucose, cholesterol (TC) (p < 0.05), triglycerides (TG) (p < 0.05), low-density lipoprotein (LDL), cyclooxygenase-2 (COX-2), and interleukin-6 (IL-6) in adipose tissue. Furthermore, GSP addition caused adipocyte crumpling in vitro and reduced the mRNA expression of COX-2, LEP, and TNF-α in adipocytes in vitro. These findings provide compelling evidence for exploring the role of GSP in the prevention and treatment of obesity and related diseases.

Study on landscape evaluation and optimization strategy of Central Park in Qingkou Town.

This article mainly discusses the evaluation and optimization of the green space utilization value of comprehensive parks used by people in dense urban areas based on the desire for green and healthy living in the postepidemic era. As a qualitative study of urban parks, this study builds an evaluation system based on the American landscape performance series and combines it with comprehensive indicators of China's urban parks, including environmental performance (such as park planning, infrastructure, trails, and vegetation), health performance (such as cultural education, park activities, and transportation accessibility) economic performance (such as tourist consumption and stimulating the development of surrounding construction) and three other aspects: conducting a site evaluation; evaluating observed behavior, interviews and questionnaires; and performing the analytic hierarchy process-coefficient of variation weight comprehensive evaluation analysis. Additionally, the park comprehensive index, land use index, traffic convenience, park vitality index and other dynamic changes are analyzed over time. The purpose is to explore the foundation of urban parks after the epidemic. The role of the urban park environment in sustainable ecological development is verified, and appropriate optimization and improvement actions are determined.

Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker.

BACKGROUND: The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. RESULTS: By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits. CONCLUSIONS: The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons.

Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree.

The influenza pandemic is a wide-ranging threat to people's health and property all over the world. Developing effective strategies for predicting the influenza outbreak which may prevent or at least get ready for a new influenza pandemic is now a top global public health priority. Owing to the complexity of influenza outbreaks that are usually involved with spatial and temporal characteristics of both biological and social systems, however, it is a challenging task to achieve the real-time monitoring of influenza outbreaks. In this study, by exploring the rich dynamical information of the city network during influenza outbreaks, we developed a computational method, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to identify the tipping point or critical stage prior to the influenza outbreak. With historical records of influenza outpatients between 2009 and 2018, the MST-DNM strategy has been validated by accurate predictions of the influenza outbreaks in three Japanese cities/regions, respectively, i.e., Tokyo, Osaka, and Hokkaido. These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak. The results show that our method is of considerable potential in the practice of public health surveillance.