Reyestopp2868

Background and Aim There are few pieces of evidence on the association between nut consumption and the risk of cardiovascular disease (CVD) in the Eastern Mediterranean Region. This study investigated the relationship of nut consumption with the risk of CVD and all-cause mortality in the Iranian population. Methods and Results This population-based prospective cohort study was carried out in 6,504 randomly selected participants aged ≥35 years in central Iran (2001-2013) in the framework of the Isfahan Cohort Study. Dietary data were collected by a validated 48-item food frequency questionnaire. Subjects or their next of kin were interviewed biannually, looking for the possible occurrence of cardiovascular events and all-cause mortality. During the median follow-up of 135 months and 52,704.3 person-years, we found a total of 751 CVD events. In unadjusted model, participants in the highest quartile of nut intake had a lower CVD risk hazard ratio (HR) [95% confidence interval (CI)] 0.57(0.47-0.70); P for trend less then 0.001, CVD mortality [HR (95% CI) 0.54 (0.33-0.72); P for trend less then 0.001], and all-cause mortality [HR (95% CI) 0.24 (0.14-0.42); P for trend less then 0.001]. In the fully adjusted model, the association was diluted, and no significant relationship was found between nut intake and CVD events and all-cause mortality, except for CVD mortality in the highest quartile vs. the lowest one [HR (95% CI) 0.55 (0.30-0.98)]. Conclusion Nut intake had an inverse association with the risk of CVD mortality. It is suggested to perform studies to examine the association of individual types of nuts and different preparation methods on CVD risk and mortality.Background and Aims Beans are rich in purines, which are important substances that lead to elevated serum urate, especially exogenous purines. Few studies were conducted to assess the relationship between beans intake and serum urate or hyperuricemia, especially in rural people. The purpose of this study was to validate the association by gender in the rural Chinese population. Methods A total of 38,855 participants aged 18-79 years old were enrolled from the Henan Rural Cohort Study (Registration number ChiCTR-OOC-15006699). Tamoxifen research buy Dietary data were collected using a validated food frequency questionnaire (FFQ). Linear regression models and logistic regression models were used to examine the associations between beans intake and serum urate levels or hyperuricemia. Restricted cubic spline regression was performed to display the dose-response relationship. Results In multivariate-adjusted linear regression, an inverse correlation was found between beans intake and serum urate level (the highest quartile Q4 vs. the bottom quartile Q1) in both men (P = 0.008) and women (P less then 0.001). Per 10-g increment in beans intake was associated with 0.30 μmol/L decreased concentration of serum urate in men and 0.71 μmol/L in women. The multivariate-adjusted odds ratios (ORs) of hyperuricemia were 0.83 (0.71, 0.97) in men and 0.73 (0.63, 0.84) in women (Q4 vs. Q1). Per 10-g increment in beans intake created a 1% decreased risk of hyperuricemia in men and 3% in women. The cubic spline suggested a risk reduction for hyperuricemia with increasing intake of beans. Conclusion A higher beans intake was associated with a lower serum urate level and a reduced risk of hyperuricemia in both sexes, and the association was more pronounced in women.Chronic exposure to a high-fat diet (HFD) may predispose individuals to neuropathologies and behavioral deficits. The objective of this study was to determine the temporal effects of a HFD on weight gain, behavioral deficits, and dopamine changes in young mice. One-month old C57BL/6J male and female mice were fed either a control diet (containing 10% calories from fat) or a HFD (containing 45% of calories from fat) for 5 months. Physiological measures such as food consumption, body weight, blood glucose, and behaviors such as motor activity, sensorimotor integration, and anxiety-like behaviors were evaluated monthly. Dopamine (DA), dopamine receptor D2 (DRD2), and dopamine transporter (DT) protein expression levels were measured in the midbrain after 5 months of dietary exposure. Results showed that body weight was significantly greater in the HFD-exposed group compared to the control-group at the end of the 4th month, while food consumption was similar in both groups. For behavioral effects, the HFD group exa similar time point as increased body weight, at about 3-4 months. Overall, this study provides a critical understanding on how HFD-induced changes in weight gain and behavioral deficits in this strain occur over time. The behavioral changes support the idea that changes also occurred in neurochemical pathways such as dopamine dysregulation.Yokukansankachimpihange is a Japanese herbal medicine reported to benefit anxiety and sleep disorders, and it has recently been introduced to treat behavioral and psychological symptoms of dementia. There are no multicenter studies of its effectiveness regarding dementia in Japan, and this study's main objective was to clarify the effects of Yokukansankachimpihange on behavioral and psychological symptoms of dementia in a sample of patients from multiple healthcare centers. Nine facilities affiliated with Osaka Association of Psychiatric Clinics participated in November 2013 through April 2015 and provided 32 Alzheimer's disease patients to whom Yokukansankachimpihange was orally administered for 8 weeks. During the study, the patients continued their regular medication regimens. Behavioral and psychological symptoms of dementia (Behavioral Pathology in Alzheimer's Disease Rating Scale [Behave-AD]), core symptoms [Mini-Mental State Examination (MMSE)], activities of daily living [Nishimura Activity of Daily Lers, Yokukansankachimpihange's efficacy might indirectly relieve these caregivers' burden of care.Background Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.