The 3-aminoacetophenone and 4-aminoantipyrine were used as ‎precursors to prepare new six ligands. The three new ligands (L1,L2 ‎and L3) were synthesis by reacting one mole of 3-aminoacetophenone ‎with one mole of (Acetyl chloride), (benzoyl chloride), (4-‎methoxybenzoyl chloride) and ammonium thiocyanat in acetone as a ‎solvent, they are:-‎ L1 (AAA) =[N-(3-acetylphenylcarbamothioyl)acetamide]‎ L2 (BAA) =[N-(3-acetylphenylcarbamothioyl)benzamide]‎ L3 (MAA) =[N-(3-acetylphenylcarbamothioyl)-4-methoxy benzamide]‎ Also three new derivatives of 4-aminoantipyrine were synthesis by ‎reacting one mole of 4-aminoantipyrine with one mole of (Acetyl ‎chloride), (benzoyl chloride), (4-methoxybenzoyl chloride) and ‎ammonium thio

Coupling reaction of 2-amino benzoic acid with 8-hydroxy quinoline gave bidentate azo ligand. The prepared ligand has been identified by Microelemental Analysis,1HNMR,FT-IR and UV-Vis spectroscopic techniques. Treatment of the prepared ligand with the following metal ions (ZnII,CdII and HgII) in aqueous ethanol with a 1:2 M:L ratio and at optimum pH, yielded a series of neutral complexes of the general formula [M(L)2]. The prepared complexes have been characterized by using flame atomic absorption, (C.H.N) Analysis, FT-IR and UV-Vis spectroscopic methods as well as conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods, Beer's law obeyed over a concentration range

Rheumatoid arthritis is a chronic inflammatory autoimmune disease its etiology is unknown. The classical autoimmune diseases, have adaptive immune genetic associations with autoantibodies and major histocompatibility complex (MHC) class II such as rheumatoid arthritis (RA), diabetes mellitus type two (DM II). Serum of99 males suffering from RA without DMII as group (G1), 45 males suffering from RA with DM II as group (G2) and 40 healthy males as group (G3) were enrolled in this study to estimation of alkaline phosphates (ALP), C-reactive protein (CRP) and Pentraxin-3(PTX). Results showed a highly significant increase in PTX3 levels in G1 and G2 compared to G3 and a significant decrease in G1comparing to G2. Results also revealed a significa

Background: Patient satisfaction is of increasing importance and widely recognized as an important indicator of quality of the medical care. There was no homogeneous definition of patient satisfaction, since satisfaction concerns different aspects of care or settings, as well as care given by various professions.

Objective: The objective of this study is to assess the patients’ level of satisfaction with diabetes care and to identify the underlying factors influencing it.

Methods: This cross-sectional study had been conducted in the Specialized Center for Diabetes and Endocrinology in Baghdad Al- Rusafa 2018. Where150 type two diabetic patients attending their follow-up

In this paper ,six new mixed metal ligand complexes are reported with Cephalexin (Ceph.H)as a primary ligand and Dimethylglyoxime (DMG) as secondary ligand with metal Chloride [MCl2 .nH2O. M=Mn(II),Co(II),Cu(II),Ni(II) and Zn(II),n=0-6] ,CrCl3.6H2O.The complexes are of (1:1:1)(Metal:Ligand: Ligand) Stoichiometry.The structures of these complexes are confirmed by using FT-IR and UV- electronic spectroscopies, magnetic moments, melting points, molar conductivity measurements and the metal % analysis revealed that the complexes analyze indicates a four coordinated as (A)=[M(HDMG) (Ceph)] .M=[Ni(II)and Zn(II).Six coordinated as (B) = K2[M(DMG)(CePh)(H2O)]. M= Mn (II),Co(II) and Cu(II) and (C)=[Cr(DMG)(Ceph)]Cl2. Interestingly, the in-vitro anti

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep