After baking the flour, azodicarbonamide, an approved food additive, can be converted into carcinogenic semicarbazide hydrochloride (SEM) and biurea in flour products. Thus, determine SEM in commercial bread products is become mandatory and need to be performed. Therefore, two accurate, precision, simple and economics colorimetric methods have been developed for the visual detection and quantitative determination of SEM in commercial flour products. The 1st method is based on the formation of a blue-coloured product with λmax at 690 nm as a result of a reaction between the SEM and potassium ferrocyanide in an acidic medium (pH 6.0). In the 2nd method, a brownish-green colored product is formed due to the reaction between the SEM and phosph

Background: Anemia is a common finding in patients with renal failure disease and it is, primarily, due to reduced production of iron and erythropoietin. Therefore, the aim of current study was to evaluate the relationship between serum level of hepcidin 25, on the one hand, and levels of serum iron, total iron-binding capacity, serum erythropoietin and transferrin saturation% in Iraqi male patients undrgoning haemodialysis.. Methods: Serum from75 males in the end stage of hemodialysis, as group one, and 25 Iraqi healthy malecontrols, as group two, were recruited in this study. Results: Data from current study showed significant increase in hepcidin 25 and significant decrease in serum iron, total iron-binding capacity, transferrin saturati

To assess the contribution of Doppler broadening and examine the
Compton profile, the Compton energy absorption cross sections are
measured and calculated using formulas based on a relativistic
impulse approximation. The Compton energy-absorption cross
sections are evaluated for different elements (Fe, Zn, Ag, Au and Hg)
and for a photon energy range (1 - 100 keV). With using these crosssections,
the Compton component of the mass–energy absorption
coefficient was derived, where the electron momentum prior to the
scattering event caused a Doppler broadening of the Compton line.
Also, the momentum resolution function was evaluated in terms of
incident and scattered photon energy and scattering angle. The res

تشخيص عوامل النجاح الحرجة لتفعيل استخدام الحاسوب الشخصي

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

It has become the subject of the environment and its problems during the last three decades of the important topics and dangerous that gained the attention of researchers in this regard as especially with the worsening repercussions severe, and turned out to be hot issues impose themselves urgently everywhere in the world, not concerned with the environment and specialists out, but also on all people wherever they are, regardless of their standard of living, and the circumstances of their lives, and their level of education and culture because the relationship of man with environment relationship since antiquity as relied upon to provide what they need him to stay, and through the stages of the development of civilization has made some a

Pseudomonas aeruginosa is a common and major opportunistic human pathogen, its causes many and dangersinfectious diseases due to death in some timesex: cystic fibrosis , wounds inflammation , burns inflammation , urinary tract infection , other many infections otitis external , Endocarditis , nosocomial infection and also causes other blood infections (Bacteremia). thereforebecomes founding fast and exact identification of P. aeruginosafrom samples culture very important.However, identification of this species may be problematic due to the marked phenotypic variabilitydemonstrated by samples isolates and the presence of other closely related species. To facilitate species identification, we used 16S ribosomal DNA(rRNA) sequence data

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p