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Expression of Vascular Endothelial Growth Factor in the Placenta of Iraqi Women Complicated with Hypertensive Disorder
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During pregnancy, high blood pressure disorder is the most common medical complication in pregnancy. It is the foremost cause of maternal mortality and perinatal diseases. Vascular endothelial growth factor (VEGF) affects the growth of vascular endothelial cells, existence, and multiplying, which are known to be expressed in the human placenta. This study aimed to identify the expression VEGF in the placenta of hypertension and normotensive women. In this study, a cross-sectional study from november 2019 to February 2020. A total of 100 placentae involved 50 hypertensive cases and 50 normotensive groups were assessed. VEGF-A expression in two placentas groups was evaluated by immunohistochemistry techniques. Strong and moderate VEGF expression was seen in syncytiotrophoblasts, stromal and endothelial cells of hypertensive cases, while not seen in hypertensive cases. There were statistically significant differences in VEGF-A expression between hypertensive cases and normotensive group. In conclusion, VEGF-A expression was significantly increased in each of syncytiotrophoblasts, stroma and endothelial cells in the placenta of hypertensive cases, and it could be used to predict the development of hypertension.

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Publication Date
Wed Jun 01 2022
Journal Name
Res Militaris
Building the proposed Fama and French Six-Factor Model FF6M-DLE by adding the indebtedness factor and its reflection on the fair value of common stock
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The research aims to determine the required rate of return according to the Fama and French five-factor model, after strengthening it by adding the indebtedness factor to build the Fama and French six-factor model FF6M-DLE. The effect of the indebtedness factor on the company's profitability and the real value of the ordinary shares calculated according to the (equivalent ascertainment) model and its suitability with the company's situation, and an analysis of the fluctuation between the market value and the real value of the ordinary stocks.

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Publication Date
Tue Aug 30 2022
Journal Name
Research Journal Of Pharmacy And Technology
Status of HBeAg in association with other baseline tests in Iraqi patients with CHB
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Chronic Hepatitis B(CHB) is a serious sequel after Hepatitis B Virus infection, although the rate of chronicity is inverse with age but it may be associated with a significant proportion of deaths related to cirrhosis and liver cancer. HBV serology and measurement of hepatocytic enzymes with viral load can predict disease prognosis and response to treatment. HBeAg refers to a high viral replication and associated with more infectivity to others. This study aimed to determine the impact of HBeAg status in chronically infected patients on various baseline tests. A total of 90 patients with CHB infection who were included in this study. Several standardized measures used for liver function tests, viral load and hepatitis B serological

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Publication Date
Mon May 11 2020
Journal Name
Journal Of Arts, Literature, Humanities And Social Sciences
The Kite Runner; A Journey of Growth
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Oscillation of the per capita growth rate
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in this paper cquations of the per capita growth rate are considered sufficient conditions for oscillation of all solutions are obtained the asymptotie behavior of the nonoscillatory solution of all souliotions are obtained

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Publication Date
Mon Sep 22 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Oral manifestations, biochemical, and IL-6 analysis of saliva in major depressive disorder patients under treatment
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Background: Major depressive disorder (MDD) is mental disorder characterized by an all-encompassing low mood accompanied by low self-esteem, and by loss of interest or pleasure in normally enjoyable activities. The aims of the study: were to determine the prevalence of oral manifestation among patients with major depressive disorder receiving antidepressant drugs, and detect alkaline phosphatase (ALP), Total Salivary proteins (TSP), and Interleukin-6 (IL-6) in relation to MDD patients under treatment and to compare with healthy controls. Materials and method: (50) MDD patients; between the ages of 20 years and 60 years.The depression patients are divided into (25) patients under treatment with fluoxetine (Prozac), and (25) patients under tr

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Publication Date
Wed Jun 07 2023
Journal Name
Journal Of The Turkish-german Gynecological Association
Maternal and neonatal outcomes in adolescent pregnant women with one prior Cesarean section in Baghdad
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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

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Publication Date
Mon Sep 22 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Immunohistochemical expression of Cyclin D1 in mucoepidermoid and adenoid cystic carcinoma of the salivary glands
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Background: Cyclin D1 proto-oncogene is an important regulator of (G1 to S) phase progression in many different cell types. The Aims of this study were to evaluate the immunohistochemical expression of Cyclin D1 in mucoepidermoid and adenoid cystic carcinoma of the salivary glands and to correlate the immunoexpression of this protein with the clinicopathological findings. Materials and methods Retrospectively, twelve of archival formalin fixed paraffin embedded tissue samples of salivary Mucoepidermoid and fourteen blocks of adenoid cystic carcinomas obtained from the archives of the department of oral pathology / college of dentistry / Baghdad university, Al-Shaheed Ghazi hospital, were included in this study. Five micrometer sections o

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Publication Date
Sun Aug 31 2025
Journal Name
Stallion Journal For Multidisciplinary Associated Research Studies
Article Review: Evaluation of the Biological Role and Gene Expression of CD144 in Colorectal Cancer
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Colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide, with tumor angiogenesis playing a pivotal role in its progression and metastasis. CD144 (VE-cadherin), a calcium-dependent adhesion molecule, is critical for endothelial cell integrity and has been linked to tumor angiogenesis and cancer stem cell phenotypes. This study aimed to evaluate the immunohistochemical expression of CD144 in benign colorectal lesions, normal adjacent tumor tissue (NRAT), and tumor tissues to elucidate its role in colorectal cancer progression. Multiple techniques, including immunohistochemistry, flow cytometry, Western blot, and qPCR, were used to assess CD144 expression and its association with the VEGF/VEGFR2 signaling pat

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Publication Date
Mon Sep 30 2024
Journal Name
Iraqi Journal Of Science
Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques
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Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

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