This study aimed to synthesize a novel amide prodrug of metformin with aspirin by amide bond. The structure was characterized by (FTIR, 1H-NMR, 13C-NMR and CHNO) ,Purification of the prepared compound was using column chromatography. Using of 40 rabbit having the same weight and devided into 4 groups (4x10) the first group (G1): (the control healthy group) was given drink water and didn’t give any material, second group(G2):( the control infected group) was given hydrogen peroxide concentration % 0.5 until infecting diabetes mellitus, third group(G3): was given hydrogen peroxide concentration as in (G2) and ( 250 mg/kg) of aspirin and (348.8 mg/kg) of metformin, fourth group(G4): was given hydrogen peroxide concentration as in (G2) and (596.3 mg/kg) of the proposed prodrug. After 3 hours, blood was taken from all groups, the serum was separated, and prepared to study it with biochemical and enzymatic study. Statistical data analysis revealed significant decrease in the levels of (lactate dehydrogenase, creatinkinase) , and significant decrease in the concentration of (Glucose) , and significant increase in the concentration of (total protein, albumin, globulin) affected by the prepared Compound [A] as compared with the control infected group. Statistical analysis revealed significant decrease in the levels of (Lactate dehydrogenase) and significant increase in the levels of (creatinkinase) , and significant increase in the concentration of (Glucose, albumin, total protein and globulin) affected by prepared compound [A] as compared with the control healthy group.
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
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Objectives: To study the effect of providing tertiary (specialized) health care for type 2 diabetic patients to meet the WHO and ADA standards and glycemic targets.
Method: Six months, Jan. – Jun. 2010, cohort study was conducted on 600 adult diabetics who registered in the National Diabetes Center (NDC) / Al-Mustansiriya University, Baghdad – Iraq. They were followed for 3- 6 months; each time patients were examined physically and their blood pressure, height, weight and BMI were measured. Fasting blood samples were taken from all patients to test the FPG, HbA1c, T.Chol, TG, HDL and LDL.
Results: Patients’ age was 52.85±15.56 year and the male/female ratio was 1.01, the median duration of disease was 7 years and their BMI w
Artificial pancreas is simulated to handle Type I diabetic patients under intensive care by automatically controlling the insulin infusion rate. A Backstepping technique is used to apply the effect of PID controller to blood glucose level since there is no direct relation between insulin infusion (the manipulated variable) and glucose level in Bergman’s system model subjected to an oral glucose tolerance test by applying a meal translated into a disturbance. Backstepping technique is usually recommended to stabilize and control the states of Bergman's class of nonlinear systems. The results showed a very satisfactory behavior of glucose deviation to a sudden rise represented by the meal that increase the blood glucose
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