Background: Diabetes mellitus (DM) accompanied with an increase in the death rate and represents a significant public health challenge. It is the cause of other disorders and infection in many body organs. Hence, it is important to study the possible changes in the immunological components in the serum of diabetic patients which are not well understood. In this work, serum C3, C4, IgA, IgG, and IgM were estimated in the patients with insulin dependent diabetes mellitus (IDDM) and compared with healthy persons. Patients and Methods: Twenty-one insulin dependent diabetic patients in addition to twenty-four healthy persons as control group were participated in this study. Serum C3, C4, IgA, IgG, and IgM were measured by using immunodiffusio

     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca

Background: Type 2 diabetes mellitus (T2DM) is considered a global disease as it affects over 150 million people worldwide, a number that is supposed to be doubled by 2025. High glucose levels, in vitro, appear to raise the extent of LDL oxidation, and glycated LDL is more prone to oxidative modification.Objective: To investigate the relationship between serum level of vitamin E and lipid profile in patients with type II DM.Methods: This study involved 28 patients suffering from type II DM diagnosed 1-4 years ago and with age ranged from 17 -60 years old, with different residence around Basra ; In addition to 56 apparently healthy persons matched in age and sex to the patients as a control group. The medical histories were taken and Gene

Boltzmann mach ine neural network bas been used to recognize the Arabic speech.  Fast Fourier transl(>lmation algorithm has been used t() extract speciral 'features from an a caustic signal .

The  spectral  feature size is reduced by series of operations in

order to make it salable as input for a neural network which is used as a recogni zer by Boltzmann Machine Neural  network which has been used as a recognizer for phonemes . A training set consist of a number of Arabic phoneme repesentations, is used to train lhe neuntl network.

The neural network recognized Arabic. After Boltzmann Machine Neura l    network   training  the  system   with 

Background: Cardiovascular disease (CVD) is an important complication of type 2 diabetes mellitus (T2DM). Oxidative stress plays a major role in the development of CVD. Saliva has a diagnostic properties aiding in the detection of systemic diseases. This study aimed to assess the association between salivary oxidative stress markers and the risk of vascular disease (VD) in T2DM patients. Materials and Methods: One hundred T2DM patients and fifty apparently healthy males were enrolled in this study. Saliva sample was collected for assessment of oxidative stress markers including: lipid peroxidation plasma thiobarbituric acid-reactive substances (TBARS), uric acid (UA) and total antioxidant capacity (TAC) levels. Arterial stiffness index (ASI

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

The aim of the study was comparative between oxidative stress in diabetes mellitus using the measurement of some biophysical and biochemical parameters on two groups of diabetic patients, were conducted in the Al-Yarmuk Teaching Hospital,30 patients insulin dependent diabetes mellitus (IDDM) or type 1 ,their ages ranged between (15-45) and30 patients non- insulin dependent diabetes mellitus (NIDDM) or type 2,their ages ranged between (42-65).This study has been compared with 30 healthy subjects. The present study was demonstrated to evaluate the alteration in oxidative stress as measured by plasma and red blood cells Malondialdehyde (MDA) andchanges in antioxidant mechanism as measured by plasma and red blood cells Glutathione (GSH)

Background: Diabetes mellitus is a chronic disease with an increasing prevalence worldwide and characterized by an increase in oxidative stress and inflammation. The most important factor that is responsible for oxidative stress and production of reactive oxygen species (ROS) is hyperglycemia. The major targets of ROS are proteins. The most common and widely used biomarker of severe oxidative protein damage is protein carbonyl content.

The study was designed to assess the serum level of protein carbonyl as a marker of protein oxidation in patients with type 2 diabetes mellitus and to evaluate the effect of age, body weight, waist circumference, diabetic control and disease duration on the level

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al