The Growth Differentiation Factor -15 (GDF-15) is a member of the transforming growth factor β superfamily. İt represents an example of the stress response cytokines. It's mostly found in cardiac myocytes, adipocytes, macrophages, endothelial cells, and vascular endothelial cells, whether they're generated normally or not. GDF-15 levels have increased and are associated with cardiovascular risk. Aim of the study: To investigate the correlation between angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs) with the level of plasma GDF-15 in a group of hypertensive patients. Materials and methods: A case-control study involved 90 individuals, 60 hypertensive patients (36 on ACE inhibitors and 24 on ARBs)

BACKGROUND: Carcinoma of urinary bladder is one of the most common malignancies worldwide and constitutes a major health problem. Multiple risk factors are associated with this tumor and its prognosis will depend on different clinicopathological parameters. Over expression of P53 protein and mutant Rb gene is associated with more aggressive clinical and histopathological features of the tumor such as advanced stage and higher grade. AIM: The immunohistochemical expression of Rb gene and P53 gene will be assessed through their protein products in transitional cell carcinoma (TCC) of the urinary bladder and then will be correlated with other well-known risk factors and prognostic parameters of bladder TCC, such as grading, tumor size, smoking

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone