Background: Coronary artery disease remains the main cause of death despite several preventive programs. Epicardial adipose tissue is a visceral fat depot of the heart located along the large coronary arteries and on the surface of ventricles and apex. Intima media thickness is commonly recognized as the initial stage in the development of atherosclerosis. The development of ultrasound machines, advances in echocardiographic devices and high resolution transducers facilitate comprehensive analysis of epicardial fat thickness (EFT) and carotid –intima media thickness (C-IMT).
Aim: To investigate the relationship of echocardiographic epicardial fat thickness (EFT) and carotid –intima media thickness (C-IMT) with the severity of coronary artery disease.
Methods: A cross sectional multicenter diagnostic accuracy study carried out at Ibn Al-Nafis Cardiovascular Hospital, Ibn Al- Bitar Cardiology Center and Ghazi Al-Hariri Hospital –Iraqi Center for Cardiology during the period between October 2016 and May 2017 assessing. History regarding demographic data and risk factors was taken. Two dimensional transthoracic echocardiographic measurements were done. EFT was measured from parasternal long axis view perpendicular to the right ventricular free wall. C-IMT was measured according to that recommended by the American society of echocardiography (ASE).
Results: The current study demonstrated that the EFT has a direct and significant correlation with the number of involved vessels (P value ˂ 0.001, r= 0.770). The validity of EFT to discriminate between multiple vessels disease and no vessel involvement is excellent (AUC= 0.984, cut point > 8mm, sensitivity=92.9%, specificity= 93.3%). There is inverse and significant correlation between ejection fraction (EF) and three vessels disease (P value ˂ 0.001, r= -0.507). Also there is a direct and significant correlation between C-IMT and severity of CAD. ROC analysis for validity of C-IMT to discriminate between multiple vessels disease and no vessel involvement is excellent (AUC= 0.961, cut point > 1.04mm, sensitivity=92.9%. specificity=86.7%). C-IMT is fair to discriminate between single vessel disease and no vessel involvement (AUC=0.738, cut point ˃ 0.84mm, sensitivity 100%), so if correlated with age, C-IMT above 0.84 can be used as a cut off point for the prediction of CAD. Finally, EFT and C-IMT are more likely to reflect severity of CAD than their ratio.
Conclusions: C-IMT and EFT are simple and reproducible parameters that can be used as a screening tool for the presence and severity of CAD especially before symptoms appearance in high risk patients
Computer literacy is an urgent necessity for university students, given the rapid development in the means of communication in which we live in this era, and the flow of abundant information. Mainly on the computer in all administrative and academic transactions, where first of all the registration for the semester is done through the computer. Computer culture has many characteristics and advantages that distinguish it from other sciences, including the concept of computer culture that cannot be defined absolutely, and it is difficult to define its levels, because the specifications of the computer-educated individual differ from one individual to another, and from time to time also, you find it a luxury in a country What, and you
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... Show MoreBackground: Cell adhesion molecules are protein entities that are located on the cell surface. The vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1) expression is related to type 2 diabetes mellitus (T2DM) with dyslipidemia. Objectives: To determine the levels of VCAM-1 and ICAM-1 in T2DM patients with dyslipidemia and to explore the relationship between VCAM-1 and ICAM-1 and the development of dyslipidemia in T2DM patients. Patients and methods: The study included 150 individuals with an age range of (35-55) years. Patients with diabetes for more than 5 years were excluded. Fifty healthy individuals constituted Group 1 (G1), fifty patients with T2DM constituted Group 2 (G2), and fifty T2DM p
... Show MoreConcrete filled steel tube (CFST) columns are being popular in civil engineering due to their superior structural characteristics. This paper investigates enhancement in axial behavior of CFST columns by adding steel fibers to plain concrete that infill steel tubes. Four specimens were prepared: two square columns (100*100 mm) and two circular columns (100 mm in diameter). All columns were 60 cm in length. Plain concrete mix and concrete reinforced with steel fibers were used to infill steel tube columns. Ultimate axial load capacity, ductility and failure mode are discussed in this study. The results showed that the ultimate axial load capacity of CFST columns reinforced with steel fibers increased by 28% and 20 % for circular and square c
... Show MoreThe present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m
... Show MoreDetection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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