A nano manganese dioxide (MnO₂) was electrodeposited galvanostatically onto a carbon fiber (CF) surface using the simple method of anodic electrodeposition. The composite electrode was characterized by field emission scanning electron microscopy (FESEM), and X-ray diffraction (XRD). Very few studies investigated the efficiency of this electrode for heavy metals removal, especially chromium. The electrosorption properties of the nano MnO₂/CF electrode were examined by removing Cr(VI) ions from aqueous solutions. NaCl concentration, pH, and cell voltage were studied and optimized using the Box-Behnken design (BDD) to investigate their effects and interactions on the electrosorption process. The results showed that the

The aim of this paper is to identify Nano-particles that have been used in diagnosis and treatment of leishmaniasis in Iraq. All experiments conducted in this field were based on the following nanoparticles: gold nanoparticles, silver nanoparticles, zinc nanoparticles, and sodium chloride nanoparticles. Most of these experiments were reviewed in terms of differences in the concentrations of nanoparticles and the method that was used in the experiments whether it was in vivo or in vitro. These particles used in most experiments succeeded in inhibiting the growth of Leishmania parasites.

Solid dispersion (SD) is one of the most widely used methods to resolve issues accompanied by poorly soluble drugs. The present study was carried out to enhance the solubility and dissolution rate of Aceclofenac (ACE), a BCS class II drug with pH-dependent solubility, by the SD method. Effervescent assisted fusion technique (EFSD) using different hydrophilic carriers (mannitol, urea, Soluplus®, poloxamer 188, and poloxamer 407) in the presence of an effervescent base (sodium bicarbonate and citric acid) in different drug: carrier: effervescent base ratio and the conventional fusion technique (FSD) were used to prepare ACE SD. Solubility, dissolution rate, Fourier transformation infrared spectroscopy (FTIR), PowderX-ray diffraction

Pituitary adenomas are the anterior pituitary tumors. Patients with an Aryl Hydrocarbon Receptor-Interacting Protein (AIP) mutation (AIP- mut) tend to have more aggressive tumors occurring at a younger age. Single nucleotide polymorphisms (SNPs) in many studies have been related to metabolic comorbidities in the general population. Study aims investigated the role of AIP gene SNPs with susceptibility to acromegaly pituitary- adenoma, with levels of LH, FSH, TSH, Testosterone, IGF1,GH, FT4 , Prolactin hormones and blood sugar levels.  The study ‎was conducted on a group of acromegaly patients, including 50 patients) both Genders( with ‎hyperplasia of the ends, and apparently healthy control group. Genotyping of

The dramatic series on television have a great impact on people’sattitudes towards dialects of language varieties, by relating theconceptual pictures or prototypes presented by series’ characters tothose dialects. This study aims to show the influence of TV series onIraqi university learners’ gender and age in relating positive ornegative semantic qualities to their dialects. To this end, 150 Iraqi EFLlearners have participated in this study to examine their attitudestowards Baghdadi, Mousli and Nasiriya dialects. The data arecollected by Lambert, Hodgson, Gardner, Fillenbaum's (1960)matched guise technique and then labeled by Willmorth’s (1988)subjective reaction test. A structured interview is conducted to supportthe data

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class