Background: Autoantibodies to islet cell antigens are known predictors of type 1 diabetes and detected in latent autoimmune diabetes in adults.
Objectives: This study aimed to identify the metabolic and immunological disturbances in diabetic patients with positive and negative islet cell antibodies (ICAs)
Materials and methods: A total number of 235 known cases of diabetes mellitus type 1 (160) and type 2 diabetes (75) were admitted in the study. Serum ICA and immunoglobulins (IgA, IgM, IgG) as well lipid profile were measured.
Results: Positive ICAs was found in 40 out of 120 T1D (33.3%) and 28 out of 75 T2D (37.3%). All the patients were poorly controlled diabetes with the evidence of significant high

Back ground: Oxidative damage has been suggested to play a key role in accelerating inflammation and to be involved in the pathogenesis of rheumatoid arthritis (RA) and
osteoarthritis (OA). Many studies had shown that those patients have low antioxidants level and are at risk of increased oxidative stress.
Objective: This study was designed to examine the levels of serum Total Antioxidant Status (TAS). Malondialdehyde (MDA) as index of lipid peroxidation and C–Reactive Protein (CRP) as a
marker of oxidative stress in patients with RA and OA and compared them with healthy control.
Method: Serum TAS , MDA and CRP levels were measured in 16 RA and 24 OA patients and compare with those obtained from 25 healt

Despite the G protein-coupled receptors (GPCRs) being the largest family of signalling proteins at the surface of cells, their potential to be targeted in cancer therapy is still under-utilised. This review highlights the contribution of these receptors to the process of oncogenesis and points to some likely challenges that might be encountered in targeting them. GPCR-signalling pathways are often complex and can be tissue-specific. Cancer cells hijack these communication networks to their proliferative advantage. The role of selected GPCRs in the different hallmarks of cancer is examined to highlight the complexity of targeting these receptors for therapeutic benefit. Our

Protein arginine methyltransferases (PRMTs) play important roles in transcription, splicing, DNA damage repair, RNA biology, and cellular metabolism. Thus, PRMTs have been attractive targets for various diseases. In this study, we reported the design and synthesis of a potent pan-inhibitor for PRMTs that tethers a thioadenosine and various substituted guanidino groups through a propyl linker. Compound II757 exhibits a half-maximal inhibition concentration (IC50) value of 5 to 555 nM for eight tested PRMTs, with the highest inhibition for PRMT4 (IC50 = 5 nM). The kinetic study demonstrated that II757 competitively binds at the SAM binding site of PRMT1. Notably, II757 is selective for PRMTs over a panel of other methyltransferases, w

     is at an all-time high in the modern period, and the majority of the population uses the Internet for all types of communication. It is great to be able to improvise like this. As a result of this trend, hackers have become increasingly focused on attacking the system/network in numerous ways. When a hacker commits a digital crime, it is examined in a reactive manner, which aids in the identification of the perpetrators. However, in the modern period, it is not expected to wait for an attack to occur. The user anticipates being able to predict a cyberattack before it causes damage to the system. This can be accomplished with the assistance of the proactive forensic framework presented in this study. The proposed system combines

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

For the duration of the last few many years many improvement in computer technology, software program programming and application production had been followed with the aid of diverse engineering disciplines. Those trends are on the whole focusing on synthetic intelligence strategies. Therefore, a number of definitions are supplied, which recognition at the concept of artificial intelligence from exclusive viewpoints. This paper shows current applications of artificial intelligence (AI) that facilitate cost management in civil engineering tasks. An evaluation of the artificial intelligence in its precise partial branches is supplied. These branches or strategies contributed to the creation of a sizable group of fashions s

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and