Background : It had been indentified by histological, histochemical and morphometrical studies that peganum harmala is a mammogenic herb and borage officinalis is a lactogenic one . To complete our investigation about these two herbs , we performed electron microscopical study . Materials and methods : Rats were grouped according to their physiological status into three groups . Each group was subdivided in to three subgroups : one control and two experimental . The two experimental group were treated daily; the 1st one with an aqueous extract of peganum harmala seeds and the 2nd with an aqueous extract of borage officinalis flowers . After two weeks of treatment , mammary glands were employed for electron microscopical study . Results : In virgin rats , the epithelial and myoepithelial cells were partially differentiated when harmal was given and completely differentiated when borage was given . In pregnant rats , harmal and borage optimize mammary parenchymal growth and induce lactation when these herbs were given. In lactating rats ,these herbs exhibited a picture similar to control lactating group but the budding of lipid droplets and the swelling of secretary vesicles were markedly increased . Conclusion: Both harmal and borage stimulate the release of prolactin and induce galactogenesis during pregnancy and promote it during lactation . Key Words : Mammary gland , Electron microscope , Harmal , Borage
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
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Background:This is a prospective study of three children presented to us in the Orbital clinic in AL ShahidGazi Al Hariri Hospital with painless proptosiswith suspension of Hydatid disease.Objectives: : Orbital hydatid disease is a rare lesion accounting for less than 1% of the total lesions of the body (1, 2). Orbital cysts presented as a primary lesion in our study which is rare to have such lesion without involvement of other organs (3). Humans represent the intermediate host where the commonly affected organ are liver and the lung (10-15%) (4). Methods:This is a prospective study of three Children presented to us in the Orbital clinic in Al Shahid Ghazi Alhariri Hospital with painless proptosis with suspension of Hydatid disease, dep
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Endometriosis is a painful disease that affects around 5% of women of reproductive age. In endometriosis, ectopic endometrial cells or seeded endometrial debris grow in abnormal locations including the peritoneal cavity. Common manifestations of endometriosis include dyspareunia, dysmenorrhea, chronic pelvic pain and often infertility and symptomatic relief or surgical removal are mainstays of treatment. Endometriosis both promotes and responds to estrogen imbalance, leading to intestinal bacterial estrobolome dysregulation and a subsequent induction of inflammation.
In the current study, we investigated the linkage be
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
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