Zinc, Copper, Selenium, Magnesium, Manganese, Chromium, Iron, Nickel, Cobalt, Vanadium and Germanium were determined by atomic absorption spectrophotometer (AAS) in blood serum of patients with rheumatoid arthritis, (30) patients (14male and 16female) with age range (37-60) years compared with normal tensive control. The analysis of results showed that the mean value of concentration (Magnesium,  Manganese and Nickel) were significantly higher in patients with rheumatoid arthritis compared to that of healthy, while the mean levels of serum (Zinc, Copper, Selenium, Chromium, Iron, Cobalt and Germanium) were significantly lower than controls. There were no significant changes in overall mean concentration of serum Vanadium in patients

Background: Preterm labor and related prematurity are predisposing factors that increase perinatal morbidity and mortality. Acute phase reactants are inflammatory markers which are positive and negative reactants explained by the reaction of reactants to the subclinical infection that are commonly associated with preterm labor. Objectives: To assess the role of acute phase reactants in pregnant women with preterm delivery. Patients and method: A case control study conducted in Gynecological Department of Baghdad Teaching Hospital from 1st February 2021 to 30th October 2021, on one hundred pregnant women with gestational age (28weeks - 36weeks+6days) who attended outpatient clinic; fifty pregnant women with established preterm labor

In this research, the covariance estimates were used to estimate the population mean in the stratified random sampling and combined regression estimates. were compared by employing the robust variance-covariance matrices estimates with combined regression estimates by employing the traditional variance-covariance matrices estimates when estimating the regression parameter, through the two efficiency criteria (RE) and mean squared error (MSE). We found that robust estimates significantly improved the quality of combined regression estimates by reducing the effect of outliers using robust covariance and covariance matrices estimates (MCD, MVE) when estimating the regression parameter. In addition, the results of the simulation study proved

The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations.  A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for

One of the most important problems in the oil production process and when its continuous flow, is emulsified oil (w/o emulsion), which in turn causes many problems, from the production line to the extended pipelines that are then transported to the oil refining process. It was observed that the nanomaterial (SiO₂) supported the separation process by adding it to the emulsion sample and showed a high separation rate with the demulsifiers (RB6000) and (sebamax) where the percentage of separation was greater than (90 and 80 )%  respectively, and less than that when dealing with (Sodium dodecyl sulfate and Diethylene glycol), the percentage of separation was (60% and 50%) respectively.

   The high proportion

A simplified parallel key was presented in this work for the Taxa of Stackys L. wildly grown in Iraq. Three records within this genus were newly recorded to our country in the present work and they are S. kermanshahansis Rech S. setifera C.A. Mey. subsp setifera, S. setifera ssp iranica (Reck.) The characteristics of these new records were also given with some representative specimens.

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of