Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder caused by impaired insulin secretion from pancreatic β-cells and insulin resistance in target tissues. Genome-wide association studies have identified over 50 genetic variants linked to T2DM, including polymorphisms associated with the disease. This study investigates the impact of the FADS1 (rs174547) polymorphism in T2DM patients compared to healthy controls and examines serum levels of omega-3 and omega-6 fatty acids, as well as D5D and D6D enzyme levels and activity. This case–control study included 120 participants: 60 newly diagnosed T2DM patients and 60 apparently healthy controls matched for age, sex, and other sociodemographic factors. Polyunsaturated fatt

Eight different Dichloro(bis{2-[1-(4-R-phenyl)-1H-1,2,3-triazol-4-yl-κN3]pyridine-κN})iron(II) compounds, 2–9, have been synthesised and characterised, where group R=CH3 (L2), OCH3 (L3), COOH (L4), F (L5), Cl (L6), CN (L7), H (L8) and CF3 (L9). The single crystal X-ray structure was determined for the L3 which was complemented with Density Functional Theory calculations for all complexes. The structure exhibits a distorted octahedral geometry, with the two triazole ligands coordinated to the iron centre positioned in the equatorial plane and the two chloro atoms in the axial positions. The values of the FeII/III redox couple, observed at ca. −0.3 V versus Fc/ Fc+ for complexes 2–9, varied over a very small potential range of 0.05 V.

      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin