The research includes a clinical study of Preptin with other parameters. The normal value of preptin in hypothyroidism (2638.4±280.0) in female while (2960.4±256.6) in male, in hyperthyroidism (589.0±90.1) in male, while in female (993.2±103.9), diabetes (2465.6±282.4) in female, in male (2085.5±282.8), in diabetes & hypothyroidism (3314.3±177.3) in male,(3179.4±265.7) in female, but control group in female (427.8±60.4), in male (384.7±62.4) at age (20-45) years they were divided into five groups: group one (G1) consisted of 30 hypothyroidism. The two group (G2) consisted of 30 patients with hyperthyroidism. And three group (G3) consisted of 30 healthy group, four group (G4) consisted of 30 patient with diabetes, and five group (G

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.

During recent years, there has been an increasing interest in the investigation of the cytokines roles in pathogenesis of cancer, thus the study aimed at evaluating the level of tumor necrosis factor-alpha(TNF-?) in sera of Iraqi multiple myeloma (MM) patients. Beta 2-microglobulion (?2-m) was assessed to determine if there was any association between this cytokine and the level of ?2- m, as the latter is related to the stage of the disease. In addition, the age and gender were also taken into consideration. Furthermore, we investigated the relationship between IgG and TNF-? in sera of patients. 49 Iraqi patients (27 males and 22 females).The patients were also divided into two groups: the first group included (17) patients who were

Abstract Background: The human epidermal growth factor receptor 2(HER2) proto-oncogene is overexpressed or amplified in approximately 15%-25% of invasive breast cancers. Approximately 35% of HER2-amplified breast cancers have coamplification of the topoisomerase II-alpha (TOP2A) gene encoding an enzyme that is a major target of anthracyclines. Hence, the determination of genetic alteration (amplification or deletion) of both genes is considered as an important predictive factor that determines the response of breast cancer patients to treatment. The aims of this study are to determinate TOP2A status gene amplification in a set of Iraqi patients with breast cancer that have had an equivocal (2+) and positive HER2/neu by immunohistochemistry

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i

  In this paper, the restricted least squares method is employed to estimate the parameters of the Cobb-Douglas production function and then analyze and interprete the results obtained.         A practical application is performed on the state company for leather industries in Iraq for the period (1990-2010).         The statistical program SPSS is used to perform the required calculations.

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