The ligand 4-(2-aminmo-5-nitro-phenylazo)-1,5-dimethyl-2-phenyl-1,2-dihydro-pyrazol-3-one derived from 4-aminoantipyrine and 4-nitroaniline was synthesized. The synthesized ligand was characterized by 1HNMR, FT-IR, UV-Vis spectra and (C.H.N) analysis. Complexes of (YIII and LaIII ) with the ligand were prepared in aqueous ethanol with a 1:2 M:L ratio and at optimum pH. The prepared complexes were characterized by using flame atomic absorption, FT-IR, UV-Vis spectra,(C.H.N) analysis and conductivity measurement. The stoichiometry of complexes was studied by the mole ratio and job methods. A concentration range (1×10-4 - 3×10-4 M) obeyed Beer's law, the complex solutions show high values of molar absorption. On the basis of physicochemical

Twelve N-(6-sustirured benzothanol-2-y1) succinamic acids and 3-(6-substitted benzonathol-2-y1)-carbamoyl propionyl chloride were synthesized in good yields from reaction of benzonathol2-yl)

The ligand 4-(2-aminmo-5-nitro-phenylazo)-1,5-dimethyl-2-phenyl-1,2-dihydro-pyrazol-3-one derived from 4-aminoantipyrine and 4-nitroaniline was synthesized. The synthesized ligand was characterized by 1HNMR, FT-IR, UV-Vis spectra and (C.H.N) analysis. Complexes of (YIII and LaIII ) with the ligand were prepared in aqueous ethanol with a 1:2 M:L ratio and at optimum pH. The prepared complexes were characterized by using flame atomic absorption, FT-IR, UV-Vis spectra,(C.H.N) analysis and conductivity measurement. The stoichiometry of complexes was studied by the mole ratio and job methods. A concentration range (1×10-4 - 3×10-4 M) obeyed Beer's law, the complex solutions show high values of molar absorption. On the basis of physicochemical

Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o

Background: While two-thirds of breast cancers express hormone receptors for either estrogen (ER) and/or progesterone (PR) , genetically altered PI3K pathway was found in more than 70% of ER-positive breast cancers.An aberrant activity of cyclin-dependent kinase 1 (CDK1) in a wide variety of human cancers has selectively constituted an attractive pharmacological targets in MYC-dependent human breast cancer cells.

Aim of the study:  Role of p110-beta as well as and CDK 1  in the pathogenesis of subset of breast cancers and contribution in their carcinogenesis.

Type of the study: is a retrospective study

Methods: This retr

In this research, the preparation of bidentate Schiff base was carried out via the condensation reaction of both the salicylaldehyde with 1-phenyl-2,3-dimethyl-4-amino-5-oxo-pyrazole to form the ligand (L). The mentioned ligand was used to prepare complexes with transition metal ions Mn(II), Co(II), Ni(II), Cu(II) and Zn(II). The resulting complexes were separated and characterized by FTIR and UV-Vis spectroscopic technique. Elemental analysis for Carbon, Hydrogen and Nitrogen elements, electronic spectra of the ligand and complexes were obtained, and the magnetic susceptibility tests were also achieved to measure the dipole moments. The molar conductivities were also measured and determination of chlorine content in the complexes and

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att