A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network

Background: Arterial stiffness is related with atherosclerosis and cardiovascular disease events. Patients with atherosclerotic disease show to have larger diameters, reduced arterial compliance and lower flow velocities. Aim of study : To compare between patients of two age groups with concomitant diseases diabetes and hypertension in regard to intima media thickness and blood flow characteristics in order to estimate the blood perfusion to the brain via the common and internal carotid arteries. Subject and Methods : 40 patients with (diabetic and hypertension) diseases were enrolled , they were classified according to age. Color Doppler and B mode ultrasound was used to determine lumen Diameter (D), Intima – media thickness (IMT)

To study the genetic effect of gestational diabetes mellitus by study IRS1gene expression in female with Gestational diabetes mellitus. It is characterized high level of blood glucose, especially during first trimester then increased during the 2^nd and 3^rd trimester of the pregnancy period. The blood samples taken from one hundred twenty healthy women and female with gestational diabetes mellitus in 3^rd trimester period of pregnancy, level of fasting blood glucose (FBG) also HbA1c% measured to diagnose GDM, in addition to lipid profile (cholesterol, triglyceride, HDL, LDL, and VLDL), molecular study consist of RNA extraction and qRT- PCR for IRS1gene expression determination. The fasting blood glucose mg/

Gestational Diabetes Mellitus is known as carbohydrate intolerance first detected during pregnancy. Pregnancy is periods of intense hormonal changes. The aim of the present study was to investigate a possible relation between the changes in serum hormones such as Luteinizing hormone (LH) , follicle stimulating hormone(FSH), Progesterone, and Prolactin with gestational diabetes mellitus. Thirty patients with gestational diabetes mellitus aged (22 -40) year attending the national center for treatment and research of diabetes/ AL-Mustansiriya University in Baghdad and 29 controls aged (20-39) year were participated. Hormonal tests including, FSH, LH, Progesterone, and Prolactin were detected by using Enzyme Linked Fluorescent Assay (ELFA) k

     Type 2 diabetes mellitus (T2DM) is a potentially fatal metabolic disorder worldwide, in this COVID-19 era. Long-term allopathic treatment has a variety of side effects, prompting the search for alternative therapies. Oleuropein, the primary bioactive ingredient of Olive Leaf Extract (OLE), has shown noteworthy actions to control T2DM. The present study provides a dynamic study of % improvement in GLUT4 concentration with different doses of metformin (150mg-500mg) in combination with 500mg using a dynamic in silico model developed in Cell Designer 4.4.2, a system biology tool. The results indicated that 300mg of metformin and 500mg of oleuropein is the optimum combination to treat diabetes, ensuring a 2% improvement in G

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