Diabetes mellitus type 2 (T2DM) is a metabolic disorder that influences above 450 million individuals around the world. Type 2 diabetes is a lack of insulin due to pancreatic β-cell malfunction and insulin resistance. This study aimed to detect insulin resistance using homeostasis model assessment (HOMA IR) and determined the correlation with glutathione-s-transferase (GST) activity in T2DM and neuropathy patients as a predictor of oxidative stress, which occurs when the oxidation-antioxidant equilibrium is disrupted. Reactive oxygen species causes vascular injury and a series of inflammation. In the present study, the results show there is no significant difference in diabetic patients (DM) and neuropathy patients (NU) versus healthy p

     In this research, new Schiff base is derived from chitosan O-nitrobenzyldehyde and its complexes were synthesized. All compounds were characterized by FT-IR, UV-Visible, TGA, DTA, TG and molar conductivity with melting point. The results showed that Schiff base was coordinated via  nitrogen atom azomethine with the center metal ions Co+2,Ni+2 and Cu+2 behaving monodentate ligand and forming complexes with molecular formula [M(L)Cl2H2O] The tetrahedral geometrical was suggested for all prepared complexes based on the characterization data for all techniques.   +2,Cu+2, Ni+2M = Co   

Some metal ions (Mn
+2
, Fe
+2
,Co
+2
,Ni
+2
,Cu
+2
, Cd
+2
and Hg
+2
) complexes of N-acetyl
Tryptophan( AcetrpH) and (2, 2′-bipyridine) (2, 2′-Bipy)have been synthesized and then
characterized on the basis of their FT-IR, UV-Vis spectroscopy, magneticsuscptibity
conductivity measurements and atomic absorption;from the results obtained and the propsed
molecular structure for these complexes as octahedral geometry,the following general formula
has been given for the prepared complexes.
[M
+n
(Acetrp)2(2, 2′-Bipy)].
Where M= Mn
+2
, Fe
+2
,Co
+2
,Ni
+2
,Cu
+2
, Cd
+2
,Hg
+2
(Acetrp)
-=Ligand ion(N-acetyl

Natural Bauxite (BXT) mineral clay was modified with a cationic surfactant (hexadecy ltrimethy lammonium bromide (BXT-HDTMA)) and characterized with different techniques: FTIR spectroscopy, X-ray powder diffraction (XRD) and scanning electron microscopy (SEM). The modified and natural bauxite (BXT) were used as adsorbents for the adsorption of 4- Chlorophenol (4-CP) from aqueous solutions. The adsorption study was carried out at different conditions and parameters: contact time, pH value, adsorbent dosage and ionic strength. The adsorption kinetic (described by a pseudo-first order and a pseudo-second order), equilibrium experimental data (analyzed by Langmuir, Freundlich and Temkin isotherm models) and thermodynamic parameters (change in s

Numerical study is adapted to combine between piezoelectric fan as a turbulent air flow generator and perforated finned heat sinks. A single piezoelectric fan with different tip amplitudes placed eccentrically at the duct entrance. The problem of solid and perforated finned heat sinks is solved and analyzed numerically by using Ansys 17.2 fluent, and solving three dimensional energy and Navier–Stokes equations that set with RNG based k−ε scalable wall function turbulent model. Finite volume algorithm is used to solve both phases of solid and fluid. Calculations are done for three values of piezoelectric fan amplitudes 25 mm, 30 mm, and 40 mm, respectively. Results of this numerical study are compared with previous b

Background: During the COVID-19 outbreak, the number of patients who have developed acute coronary syndromes (ACS) has soared rapidly, cardiovascular disease and mortality are influenced by the elevated inflammatory biomarkers. The aim of this study is to compare inflammatory markers between patients with ACS who hadn’t previously had COVID-19 and those who’d be infected within the preceding three months; as well as, evaluating the effect of statins on inflammatory biomarkers.

Methods: This is a comparative cross-sectional study of 42 patients who presented with ACS and had previously had COVID-19 and 48 patient who had never had CO

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F