Abstract—Background: Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting reproductive- age women, often linked to metabolic issues like insulin resistance. Objective: this study aimed to evaluate ornithine decarboxylase (ODC) and ferric reducing capacity (FRC) levels in women with PCOS, with assess the effects of metformin and Primolut N treatment on their levels. Subjects and Methods: A case− control study was conducted with 150 married Iraqi women, categorized into three groups: 50 healthy controls, 50 untreated PCOS, 50 treated PCOS. Blood samples were analyzed for ODC, FRC levels and hormonal profiles. Statistical analysis applied independent t-test, Pearson’s correlation, ROC curve. Results: The ODC level

Flavonoids were extracted from Zizyphus spina-christi leaves by Ethyl acetate after acid digested and used as antioxidant. The dried extract was added separately to each sample of fat extracted from hallow cow and sheep bones as follows: T1 cow fat, T2 control for cow fat, T3 sheep fat and T4 control for sheep fat (the control T2 and T4 reffered to samples without added antioxidant).
Samples were stored at -18, 5, 25 and 55 °C for 28 days. The storage trials were conducted at -18, 5 and 25 °C for 28 days for T1, T2, T3 and T4. The chemical indices examined initially and at the end of storage period. PVs was 1.46, 1.46, 1.8 and 1.8 meq/ Kg oil respectively, FFA values were 0.245, 0.245, 0.244 and 0.244% respectively and TBA va

ABSTRACT

            The aim of the research is to identify the impact of the areas of organizational change in strategic marketing planning in cement factories operating within the city of Al-Khums, and this effect was measured using a questionnaire consisting of 28 phrases, and it was relied on the descriptive approach that suits the nature of the subject of the research, while the research community consists of heads And members of the board of directors, authorized managers, unit managers and heads of departments in the factories under study, which numbered 55 respondents, during the month of September of the year 2021, and a sample of the compreh

Background: Periodontal diseases are bacterial infections of the gingiva, bone and attachment fibers that support the teeth and hold them in the jaw. Lactoferrin is a multifunctional glycoprotein and it is the main component of neutrophil polymorphonuclear leukocytes that activated during inflammatory processes such as Periodontal diseases Aims of the study: Determine the salivary levels of Lactoferrin and pH and their correlations with clinical periodontal parameters(Plaque Index , Gingival Index , Bleeding on Probing , Probing Pocket Depth , and Clinical Attachment Level ) and the correlation between Lactoferrin with potential of hydrogen ion (PH) ,flow rate and α-amylase of study groups that consisted of patients had gingivitis and pa

This experiment was conducted in the season 2001-2000 in station Ishaqi the company's general industrial crops to plant livestock Vigna radala deleted (Khadrawi) carried out the experiment design panels splinter and order in RCBD with three balls two factors are levels nitrogen fertilizer (120 and, 100.0 kg urea / ha)nitrogen ratio of 46%, which put in the main panels mAIN PLOT and Alkiavat three levels that were placed in secondary panels .....

          The present study includes the effect of the ethanolic and aqueous extract of Piper nigrum against the third instar of Culex pipiens molestus (Diptera:Culicidae) in different concentrations: 1, 1.5 ppm of ethanolic extracts of Piper nigrum causing 100% mortality of larvae in the second day of treatment, while the concentrations 5% of aqueous extracts of Piper nigrum caused 100% mortality of larvae in the second day of treatment. The concentration 2.5% caused 100% mortality of larvae after four days of treatment. The current study also detected the effect of 0.1% concentration of Piper nigrum aqueous extract. The effect of this concentratio

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