Effect of green tea on inflammation and oxidative stress in cisplatin-induced experimental liver function

Introduction: Cisplatin is one of the most potent chemotherapeutic antitumor drugs. Also, oxidative stress has been established to be involved in cisplatin-induced toxicity. Therefore, the present study was undertaken to examine the antioxidant and anti-inflammation potential of green tea hydroalcoholic extract (GTE) against the liver function of cisplatin in male rats.Methods: Adult male Wistar rats (180–250 g) were divided into 4 groups (n = 5) treated as follows: (1) control group (saline solution, 1 ml kg−1 body weight, i.p.), cisplatin group (7 mg kg−1 body weight, i.p.). Animals of Groups III received only green tea extract (30 mg/kg/day, by gavage). Group IV was given green tea extract+ cisplatin once daily for 24 hours. Liver function was evidenced in the cisplatin group by the increased serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). The mechanism of cisplatin induced liver function was considered as being decreased the total antioxidant power (TAP). Systemic inflammation was assessed by tumor necrosis factor-alpha (TNF-α) levels.Results: A decrease in TAP level in cisplatin group was observed compared with control group. GTE administration decreased TNF-α and increased TAP compared to cisplatin group, but showed no significant differences between groups.Conclusion: The results suggested that green tea could ameliorate cisplatin liver function in rats through reduction of oxidative toxic stress and inflammation

Mental Health Status, Life Satisfaction, and Mood State of Elite Athletes During the COVID-19 Pandemic:a follow-up study in the phases of home confinement, reopening, and semi-lockdown condition

Scientific reports notified that the pandemic caused by the Coronavirus disease 2019 (COVID-19) has raised an unprecedented mental health emergency worldwide. Abrupt changes in daily routine, environmental constraints, adopted home confinement measures, and uncertainty about a date for returning to usual activities can potentially affect mental health and sports activities in athletes. Hence, we designed a cross-sectional study with a within-subjects design to investigate the impact of the pandemic on mental health, mood states, and life satisfaction of elite athletes. During the three phases of home confinement (April 14–24, n = 525), reopening (May 9–19, n = 464), and current semi-lockdown (July 20–31, n = 428), elite athletes voluntarily responded to an online survey. The self-report questionnaire was prepared to collect demographic and epidemiological variables of interest and the COVID-19-related information. All participants also completed the Profile of Mood State (POMS), General Health Questionnaire-28 (GHQ-28), and Satisfaction with Life Scale (SWLS). The main result is that the training rate, mental health, life satisfaction, and positive mood have decreased during the home confinement period as compared with the reopening and semi-lockdown phases. However, the need for psychosocial services has increased during the pandemic period. The present study provides the first preliminary evidence that home confinement conditions during the COVID-19 pandemic might have negatively influenced elite athlete’s mood state, mental health, and life satisfaction, as well as training rates. Monitoring the psychological parameters of elite athletes and developing strategies to improve their mental health during the COVID-19 pandemic should be on the agenda. Next studies, therefore, seem reasonable to focus on active interventions for athletes during the ongoing COVID-19 pandemic

Ehsan Mirzakhalili High-Order Solution of Viscoelastic Fluids Using the Discontinuous Galerkin Method

In this paper, the high-order solution of a viscoelastic fluid is investigated using the discontinuous Galerkin (DG) method. The Oldroyd-B model is used to describe the viscoelastic behavior of the fluid flow. The high-order accuracy of the applied DG method is verified for a Newtonian benchmark problem with an exact solution. Next, the same algorithm is utilized to solve the viscoelastic flow by separating the stress tensor into the stress due to the Newtonian solvent and the stress due to the solved viscoelastic polymers. The high-order accuracy of the solution for viscoelastic flow is demonstrated by solving the planar Poiseuille flow. Then, the planar contraction problem is simulated as a benchmark for the viscoelastic flow. The obtained results are in good agreement with the results in the literature for both creeping and inertial flow when high-order polynomials were used even on coarse meshes

A higher-order accurate unstructured finite volume Newton-Krylov algorithm for inviscid compressible flows

A fast implicit (Newton-Krylov) finite volume algorithm is developed for higher-order unstructured (cell-centered) steady-state computation of inviscid compressible flows (Euler equations). The matrix-free General Minimal Residual (GMRES) algorithm is used for solving the linear system arising from implicit discretization of the governing equations, avoiding expensive and complicated explicit computation of the higher-order Jacobian matrix. An Incomplete Lower-Upper factorization technique is employed as the preconditioning strategy and a first-order Jacobian as a preconditioning matrix. The solution process is divided into two phases: start-up and Newton iterations. In the start-up phase an approximate solution of the fluid flow is computed which includes most of the physical characteristics of the steady-state flow. A defect correction procedure is proposed for the start-up phase consisting of multiple implicit pre-iterations. At the end of the start-up phase (when the linearization of the flow field is accurate enough for steady-state solution) the solution is switched to the Newton phase, taking an infinite time step and recovering a semi-quadratic convergence rate (for most of the cases). A proper limiter implementation for higher-order discretization is discussed and a new formula for limiting the higher-order terms of the reconstruction polynomial is introduced. The issue of mesh refinement in accuracy measurement for unstructured meshes is revisited. A straightforward methodology is applied for accuracy assessment of the higher-order unstructured approach based on total pressure loss, drag measurement, and direct solution error calculation. The accuracy, fast convergence and robustness of the proposed higher-order unstructured Newton-Krylov solver for different speed regimes are demonstrated via several test cases for the 2nd, 3rd and 4th-order discretization. Solutions of different orders of accuracy are compared in detail through several investigations. The possibility of reducing the computational cost required for a given level of accuracy using high-order discretization is demonstrated.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

Application of Machine Learning in Computational Fluid Dynamics-based Design and Optimisation of Turboexpanders Used in Natural Gas Pressure Reduction Stations

A recently proposed resolution in the market of natural gas (NG) supply in urban areas considers the installation of energy-saving machinery such as turbo expanders in pressure reduction stations (PRSs) of NG distribution networks. The use of turboexpanders in these networks has successfully shown pronounced benefits over the traditional Joule-Thompson (J-T) valves, by effectively recovering the waste energy of the gas during the expansion process. On the negative side, however, turboexpanders are often exposed to off-design operations, ie mainly due to inefficient design causing an improper response to instantaneous variations of upstream pressure in a given NG distribution cycle, which may eventually compromise their advantages, if running uncontrolled. Towards addressing this very complexity, the present work is intended to introduce and examine a cost-effective, yet reliable, numerical framework that integrates machine learning (ML) with computational fluid dynamics (CFD) to improve re-design and optimisation of existing NG turboexpanders in PRS facilities, with the ultimate goal of upgrading traditional procedures frequently used for maintaining such machinery. Considering the high granularity of the proposed framework, it is anticipated that it could be conveniently extended as a robust supplemental tool for related industrial maintenance procedures dealing with NG turbomachinery and energy systems.<br/