Abnormalities in the Lipid and lipoprotein levels are common in the general population and are considered as very important risk- factors for cardiovascular disease .In this context the effect of cholesterol, which is one of the most clinically relevant lipids is very important. Aim of the present study was to determine the levels of GLP-1 and GPCR in non- diabetic dyslipidaemic patients and compare the results with the control group, which may be used as a novel biomarker to predict heart disease in these patients. The study was also aimed to find the relationship between GLP-1 and GPCR with lipid profile and glucagon in the patient group. The study involved 90 non-diabetic dyslipidaemia patients, with 90 healthy controls. The subjects were matched by age (35-50 years) and body mass index (BMI) (28 kg/m2). Blood samples were collected from healthy controls and dyslipidaemic patients after 12-14 hours of fasting. The study was conducted between January 2015– September 2015 in the Ibn- Al Naphes hospital in Baghdad province / Iraq .Diabetic patients were exclusion from this study. BMI were determined for all student groups.FBG, Lipid Profile ,glucagon, GLP-1, GPCR was determined in the control and patient groups. The results are expressed as mean ± SEM. Student ̓s t-test was used to compare the significance of the variation between dyslipidaemia and control groups. Results showed nonsignificant elevations in BMI, FBS, and HbA1c levels in the patient group compared with the control group. There was a significant elevation in TC, TG, LDL-c, and VLDL-c levels in the patient group compared with the control group, while a significant decrease was noticed in HDL-c level in the patient group compared with the control group .There was also a significant elevation in glucagon, while a highly significant elevation in GLP-1 and GPCR levels in the patient group when compared with the control group. A significant correlation was observed between GLP-1 with TC, TG, HDL-c, and GPCR in the patient group. There was also a significant correlation between GPCR with TC, TG and HDL-c in the patient group. From this study, it is concluded that a significant increase in GLP-1and GPCR levels, in addition to their correlation with TC, TG, HDL-c and glucagon in the patient group, compared to the control group indicate that these parameters could be used as a novel biomarker to predict heart disease in these patients in future.
The aim of this study was to investigate the effect of operating variables on, the percentage of removed sludge (PSR) obtained during re-refining of 15W-40 Al-Durra spent lubricant by solvent extraction-flocculation treatment method. Binary solvents were used such as, Heavy Naphtha (H.N.): MEK (N:MEK), H.N. : n-Butanol (N:n-But), and H.N. : Iso-Butanol (N:Iso:But). The studied variables were mixing speed (300-900, rpm), mixing time (15-60, min), and operating temperature (2540, oC). This study showed that the studied operating variables have effects where, increasing the mixing time up to 45 min for H.N.: MEK, H.N.: n-Butanol and 30 min for H.N.: Iso-Butanol increased the PSR, after that percentage was decreased; increasing t
... Show MoreGe-Au infrared photoconductive detection was prepared from germanium single crystal which were doped with different gold concentration using thermal evaporation. The spectral resonsivity (Rλ), spectral detectivity (D*) were determined as function of wavelength, also the resistance, conductivity in dark and with illumination to infrared radiation, the gain and relative photo response have been measured with different gold concentration. Remarkable improvements in the photoresponse gain were observed for the highest resistance specimen at the expense of spectral detectivity values.
This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different
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In this paper, the maximum likelihood estimates for parameter ( ) of two parameter's Weibull are studied, as well as white estimators and (Bain & Antle) estimators, also Bayes estimator for scale parameter ( ), the simulation procedures are used to find the estimators and comparing between them using MSE. Also the application is done on the data for 20 patients suffering from a headache disease.
In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as the Bayes method. The comparison was made using the mean error squares (MSE), where the best estimator is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).
Samples of Bi1.6Pb0.4Sr2Ca2Cu3O10+δ superconductor were prepared by solid-state reaction method to study the effects of gold nanoparticles addition to the superconducting system, Nano-Au was introduced by small weight percentages (0.25, 0.50, 0.75, 1.0, and 1.25 weight %). Phase identification and microstructural
characterization of the samples were investigated using XRD and SEM. Moreover, DC electrical resistivity as a function of the temperature, critical current density Jc, AC magnetic susceptibility, and DC magnetization measurements were carried to evaluate the relative performance of samples. x-ray diffraction analysis showed that both (Bi,Pb)-2223 and Bi-2212 phases coexist in the samples having an orthorhombic crystal struct
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In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). T
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