BACKGROUND: Hepatocyte growth factor (HGF) is a proangiogenic factor that exerts different effects over stem cell survival growth, apoptosis, and adhesion. Its impact on leukemogenesis has been established by many studies. AIM: This study aimed to determine the effect of plasma HGF activity on acute myeloid leukemia (AML) patients at presentation and after remission. PATIENTS AND METHODS: This was a cross-sectional prospective study of 30 newly-diagnosed, adult, and AML patients. All patients received the 7+3 treatment protocol. Patients’ clinical data were taken at presentation, and patients were followed up for 6 months to evaluate the clinical status. Plasma HGF levels were estimated by ELISA based methods in the pa

Extended calculations for sputtering yield through bombed Nickel – target by Xenon ions plasma are accomplished. The calculations include changing the input parameters: the energy of xenon ions plasma, the hit target angle of nickel target, thickness of the nickel target layer, and the slight change in the surface binding energy of Nickel. The program TRIM is used to accomplish these calculations. The results show that the sputtering yields directly dependent on these parameters. The change in angles of incidence plasma ions and energy leads to a significant change in the sputtering yields. On the other hand, the sputtering yields ore highly affected by changing target width and surface binding energy at fixed ion parameters.

Breastfeeding (BF) serves as a complete nutritional source for the first six months of infant’s life. Breast milk contains all essential nutrients that necessary for the physiological growth and development of infants. The aim of this study was to compare the physiological growth of infants including weight, height and head circumference who were exclusively breastfed for 6 months and those who were given bottle-fed or mixed fed and to find a percentage of exclusive breastfeeding among mothers who contributed in this study in Sulaimani city. This study was carried out in Sulaimani city/ Kurdistan region of Iraq and the cases were enrolled between the first of October 2018 and first of October 2019. The infants’ weight, height and hea

     This paper develops the work of Mary Florence et.al. on centralizer of semiprime semirings and presents reverse centralizer of semirings with several propositions and lemmas. Also introduces the notion of dependent element and free actions on semirings with some results of free action of centralizer and reverse centralizer on semiprime semirings and some another mappings.

Thermomechanical analysis (TMA) and differential scanning calorimetry (DSC) are used to investigate the effect of molding and annealing of polyester on the behavior of thermal expansion and crystallization since these factors play role in the reprocessing or recycling of the polymer. The dynamic mode of the TMA provides enhanced characterization information about the polyester since it separates the transitions into reversible and irreversible signals, and also reveals the progress of the amorphous regions as the polyester loses strength with the increasing temperature approaching melting. Slow cooling after annealing brings crystallization that may be attributed to molecular chain straightening due to orientation.

The physical, mechanical, electrical and thermal properties containing (Viscosity, curing, adhesion force, Tensile strength, Lap shear strength, Resistively, Electrical conductivity and flammability) of adhesive material that prepared from Nitrocellulose reinforced with graphite particles and aluminum streat. A comparison is made between the properties of adhesive material with varying percentage of graphite powder (0%, 25%, 30%, 35%, 40%) to find out the effect of reinforcement on the adhesive material. The ability of property an electrical was studied through the measurement of conductivity a function of temperature varying. The results of comparison have clearly shown that the increasing of conten

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod