Background :Atherosclerosis is the most
frequent underlying cause of ischemic heart
disease and a major cause of death all over the
world. This study was carried out to analyze and
compare the angiographic findings in patients
with diabetes mellitus versus non diabetics with
coronary heart disease , and to correlate these
findings with some risk factors for coronary
heart disease.
Methods: A total of 100 patients were studied,
50 with diabetes mellitus, and 50 non diabetics.
This study was carried out at Al-Sadr teaching
hospital in Basrah, Southern Iraq during the
period April 2009- September 2009. All patients
were known to have coronary heart disease. Risk
factors for coronary heart disease were studied
and coronary angiography was performed to
define coronary lesions for all patients, and were
classified into those who had single , two , three
or four vessels disease, according to the number
of critical coronary vessels involed. Angiography
was considered as normal when the test did not
identify any obstruction of any major epicardial
artery.
Results: Diabetic patients had more multi-vessels
involvement coronary heart disease than non
diabetics. Right coronary artery, left main stem
artery, left circumflex artery involvement and left
ventricular systolic dysfunction were seen more
in diabetics than non diabetic patients.
Conclusions: This study showed that diabetic
patients had more critical multi-vessels
involvement than non diabetic. Diabetic patients
had also worse coronary angiographic findings
independent on the presence or absence of other
risk factors
Some metal ions (Mn+2, Co+2, Ni+2, Cu+2, Zn+2, Cd+2 and Hg+2) complexes of quinaldic acid (QuinH) and α-picoline (α-Pic) have been synthesized and characterized on the basis of their , FTIR, (U.V-Vis) spectroscopy, conductivity measurements, magnetic susceptibility and atomic absorption. From the results obtained the following general formula has suggested for the prepared complexes [M(Quin)2( α-Pic)2].XH2O where M+2 = (Mn, Co, Ni, Cu, Zn, Cd and Hg), X = 2, X = zero for (Co+2 and Hg+2) complexes, (Quin-) = quinaldate ion, (α-Pic) = α-picoline. The results showed that the deprotonated ligand (QuinH) by using (KOH) coordinated to metal ions as bidentate ligand through the oxygen atom of the carboxylate group (-COO-) and the nitrogen ato
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The mixed-spin ferrimagnetic Ising system consists of two-dimensional sublattices A and B with spin values and respectively .By used the mean-field approximation MFA of Ising model to find magnetism( ).In order to determined the best stabile magnetism , Gibbs free energy employ a variational method based on the Bogoliubov inequality .The ground-state (Phase diagram) structure of our system can easily be determined at , we find six phases with different spins values depend on the effect of a single-ion anisotropies .these lead to determined the second , first orders transition ,and the tricritical points as well as the compensation phenomenon .
Complexes of some metal ions ( Mn(I? ) , Co(??) , Ni(??) ,Cu (??) , Zn(I?) , Cd (??) , and Hg(??) ) with 8-hydroxyquinoline (Oxine) and 2- Picoline (2-pic ) have been synthesized and characterized on the basis of their FT-IR. and Uv-visible spectroscopy ,atomic absorption molar conductivity measurements and magnetic susceptibility ,from the results obtained the following general formula has been given for prepared complexes [M (oxine)2 (2-pic)2]where M = M(??) = Mn , Co , Ni , Cu , Zn , Cd , Hg(oxine)- = ionic ligand 8-hydroxyquinolin (oxinato)(2- pic) = 2- picoline
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New (pentulose-?-lactone-2,3-enedibenzoate barbituric acid) (L) have been synthesized by reaction of (5-C-dimethyl malonyl-pentulose-?-lactone-2,3-enedibenzoate) with urea in alkaline media (sodium methoxide). (Ca+2, Co+2, Ni+2, Cu+2, Zn+2, Cd+2 and Hg+2) complexes of (pentulose-?-lactone-2,3-enedibenzoate barbituric acid) (L) have been prepared and characterized by (1H and 13CNMR), FTIR, (U.V-Vis) spectroscopy, Atomic absorption spectrophotometer (A.A.S), Molar conductivity measurements and Magnetic moment measurements, and the following general formula has been given for the prepared complexes [MLCl2(H2O)].XH2O, where M = (Ca+2, Co+2, Ni+2, Cu+2, Zn+2, Cd+2, Hg+2), X = five molecules with (Cd+2) complex, L = (pentulose-?-lactone-2,3
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The performance of a diesel engine was tested with diesel oil contaminated with glycol at the engineering workshop/Department of Agricultural Machines and Equipment / College of the Agricultural Engineering Sciences at the University of Baghdad. To investigate the impact of different concentrations of glycol on the performance of a diesel engine, an experimental water-cooled four-stroke motor was utilized, with oil containing 0, 100, and 200 parts per million (ppm). Specific fuel consumption, thermal efficiency, friction power, and exhaust gas temperature were examined as performance indicators. To compare the significance of the treatments, the study employed a full randomization design (CRD), with three replicates for each treatment at th
... Show MoreThe problem of slow learning in primary schools’ pupils is not a local or private one. It is also not related to a certain society other than others or has any relation to a particular culture, it is rather an international problem of global nature. It is one of the well-recognized issues in education field. Additionally, it is regarded as one of the old difficulties to which ancient people gave attention. It is discovered through the process of observing human behaviour and attempting to explain and predict it.
Through the work of the two researchers via frequent visits to primary schools that include special classes for slow learning pupils, in addition to the fact that one of the researcher has a child with slow learning issue, t
A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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