Oleanolic acid: a novel cardioprotective agent that blunts hyperglycemia-induced contractile dysfunction

Diabetes constitutes a major health challenge. Since cardiovascular complications are common in diabetic patients this will further increase the overall burden of disease. Furthermore, stress-induced hyperglycemia in non-diabetic patients with acute myocardial infarction is associated with higher in-hospital mortality. Previous studies implicate oxidative stress, excessive flux through the hexosamine biosynthetic pathway (HBP) and a dysfunctional ubiquitin-proteasome system (UPS) as potential mediators of this process. Since oleanolic acid (OA; a clove extract) possesses antioxidant properties, we hypothesized that it attenuates acute and chronic hyperglycemia-mediated pathophysiologic molecular events (oxidative stress, apoptosis, HBP, UPS) and thereby improves contractile function in response to ischemia-reperfusion. We employed several experimental systems: 1) H9c2 cardiac myoblasts were exposed to 33 mM glucose for 48 hr vs. controls (5 mM glucose); and subsequently treated with two OA doses (20 and 50 µM) for 6 and 24 hr, respectively; 2) Isolated rat hearts were perfused ex vivo with Krebs-Henseleit buffer containing 33 mM glucose vs. controls (11 mM glucose) for 60 min, followed by 20 min global ischemia and 60 min reperfusion ± OA treatment; 3) In vivo coronary ligations were performed on streptozotocin treated rats ± OA administration during reperfusion; and 4) Effects of long-term OA treatment (2 weeks) on heart function was assessed in streptozotocin-treated rats. Our data demonstrate that OA treatment blunted high glucose-induced oxidative stress and apoptosis in heart cells. OA therapy also resulted in cardioprotection, i.e. for ex vivo and in vivo rat hearts exposed to ischemia-reperfusion under hyperglycemic conditions. In parallel, we found decreased oxidative stress, apoptosis, HBP flux and proteasomal activity following ischemia-reperfusion. Long-term OA treatment also improved heart function in streptozotocin-diabetic rats. These findings are promising since it may eventually result in novel therapeutic interventions to treat acute hyperglycemia (in non-diabetic patients) and diabetic patients with associated cardiovascular complications

To the background of German melancholy and enthusiasm concepts in V. A. Zhykovsky’s life and works

Статья посвящена актуальному вопросу современного литературоведения: взаимодействию В.А. Жуковского с немецким миром в финальный период жизнетворчества 1840-1850-х гг. Особое внимание отведено концептам меланхолии и энтузиазма в контексте систематических занятий поэта немецкой духовно-назидательной литературой

The effects of movement of attractors and pictorial content of rewards on users' behaviour in virtual environments:An empirical study in the framework of perceptual opportunities

Technological developments in Virtual Reality (VR) appear to outpace progress in design methodology of VR. The theory of Perceptual Opportunities (POs) has previously been proposed as a basis of such a design methodology (Blade and Padgett, 2002). This paper presents the first empirical study investigating the effect of representation of POs on users' behaviour in Virtual Environments (VEs). The current study has a methodological focus, using POs as a framework and desktop VR as a experimental environment. The application of an experimental paradigm is illustrated with two experiments. Evidence was found for an effect of movement type on choice of objects in a simple VE. Implications for VE design and the methodology of empirical research in the framework of POs are discussed.</p

The effects of movement of attractors and pictorial content of rewards on users' behaviour in virtual environments:An empirical study in the framework of perceptual opportunities

A major cause of spacecraft orbital variation comes from natural perturbations, which, in close proximity of a body, are dominated by its non-spherical nature. For small bodies, such as asteroids, these effects can be considerable, given their uneven (and uncertain) mass distribution. We propose to use solar sail technology to reduce or eliminate the net effects of the irregular gravity field on the orbit. The Perturbation Relief (PR) method will be introduced which obtains a minimum effort optimal control law via the GPOPS II software. Success will be shown by taking a Poincar´e section and measuring the distance between the point through which each successive orbit passes. This distance will reduce to zero as the perturbation is nullified, resulting in a periodic orbit. The drift in Right Ascension of the Ascending Node (&#937;) will also be verified. Minimisation of this will also indicate that the effects of the perturbation are minimised. Three test cases will be investigated at ellipsoidal representations of asteroids 3122 Florence, 101955 Bennu and a fictional Higher Eccentricity Body. The technique will later find application in a multiple asteroid rendezvous mission with a solar sail as the primary propulsion system

Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features

Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19

A Critical, Temporal Analysis of Saudi Arabia&rsquo;s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress