The objective of this study is to estimate the effect of the hydro-ethanolic catechin extract toward blood glucose, lipid profile and liver functions in alloxan diabetic mice. 50 healthy mice (25-30 g) were divided into five groups of ten animals for each. Group A received normal saline as normal control group. To induce diabetes, alloxan (150 mg/kg), intraperitoneal (i.p.) single dose was injected to groups B, C, D and E. Group B represents diabetic control group. Groups C, D and E received ethanolic catechin extract (30 mg/kg and 40 mg/kg) for different periods of 1, 2 and 3 weeks as treated groups. Blood glucose, serum lipids [Total Cholesterol (TC), Triglycerides (TGs) and High Density Lipoproteins (HDL)], asparagine transaminase (AST),

Sixty albino female mice were used in this experiment to study the possible effect of the crude alcoholic extract of the celery leaves on their fertility. These animals were randomly and equally divided into three experimental groups (20 females/group). The first and second groups were orally given a daily dose of 500 and 1000 mg/kg body weight, respectively, of the crude alcoholic extract, while the third group (control) was similarly treated, at the same time, with 0.1 ml/gm body weight of physiological saline for comparison. The treatment, however, lasted for 25 consecutive days. On day 26, after treatment was stopped, the life body weight of all animals was recorded before sacrificing the animals. Thereafter blood

The activity of the aqueous extract of Olea europaea was tested at concentrations of 8, 15 or 20 mg/kg of body weight on lipid profile in twenty female local rabbits. These animals were randomly divided into four groups (five animals in each group). Three groups were dosed orally with the concentrations mentioned above, while the last was administered with distilled water and considered as a control group. These animals were orally dosed by aqueous extract using a micropipette for 30 days. The results showed that there was a significant (P<0.05) decrease in cholesterol, triglycerides, low density lipoprotein (LDL-cholesterol), very low density lipoprotein (VLDL-cholesterol) concentrations and atherosclerosis index means for the three trea

Cataract is an opacity in the normally transparent focusing lens of the eye which leads to blindness. The aim of current study is to investigate the possible protective and therapeutic effects of aqueous extract of Foeniculum vulgare seed eye drops (0.5%) against selenite induced cataract in rabbits. Aqueous extract of Foeniculum vulgare seed prepared then formulated as eye drops. Sodium selenite used to induce cataract in rabbits' right eye by single intravitreal injection. Thirty-six rabbits included in the study and divided into three equal groups (12 rabbits in each group): healthy control group, Cataract-Induced group and group treated by Foeniculum vulgare seed extract eye drops. Parameters include the score of lens opacity which was

The Target costing is an important modern techniques strategic managerial accounting.which is been shown active adoption to changes in contemporary business environments Inaddition,they had been adopted by the units as aresult of the growth in the strategic approach in the man agement..the goal of using target costing is to build and strengthen competitive abilities of economic units thvough introducing appropriate ways to decrease cost and improving quality of product The hypothesis reflects interest in achieving the goal of the research,ie.research question,The hypothesis use target costing to assist economic units to decrease cost and in the manner that leads to competitive advantages while surviving and flourishing in current busines

     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot.  Simulation results, whi

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med