BACKGROUND: Montelukast (Singulair) is a cysteinyl leukotriene receptor antagonist, used for the maintenance treatment of asthma and to relieve symptoms of seasonal allergic rhinitis and asthma, also used for exercise-induced bronchospasm. AIM: This study was performed to determine the prevalence of Montelukast use as an add-on therapy among Iraqi asthmatic patients on treatment. Comparing the effectiveness of regimens with and without montelukast. METHODS: This descriptive cross-sectional study was carried out on 73 Iraqi asthmatic patients on treatment of both sexes with age range (18-60) years old, attending Al-Kindy Teaching Hospital and Al-Zahraa Centre of Asthma and Allergy, Baghdad, for the period between February

BACKGROUND: Montelukast (Singulair) is a cysteinyl leukotriene receptor antagonist, used for the maintenance treatment of asthma and to relieve symptoms of seasonal allergic rhinitis and asthma, also used for exercise-induced bronchospasm. AIM: This study was performed to determine the prevalence of Montelukast use as an add-on therapy among Iraqi asthmatic patients on treatment. Comparing the effectiveness of regimens with and without montelukast. METHODS: This descriptive cross-sectional study was carried out on 73 Iraqi asthmatic patients on treatment of both sexes with age range (18-60) years old, attending Al-Kindy Teaching Hospital and Al-Zahraa Centre of Asthma and Allergy, Baghdad, for the period between February

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different

Due to the easily access to the satellite images, Google Earth (GE) images have become more popular than other online virtual globes. However, the popularity of GE is not an indication of its accuracy. A considerable amount of literature has been published on evaluating the positional accuracy of GE data; however there are few studies which have investigated the subject of improving the GE accuracy. In this paper, a practical method for enhancing the horizontal positional accuracy of GE is suggested by establishing ten reference points, in University of Baghdad main campus, using different Global Navigation Satellite System (GNSS) observation techniques: Rapid Static, Post-Processing Kinematic, and Network. Then, the GE image for the study

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

A poetic vision appeared in the poem of Al-Abbas bin Mardas Al-Sulami. He lived in the eras of pre-Islamic times and the emergence of Islam. The focus of this research: is the study of poetic text in the pre-Islamic era and the era of early Islam. The research followed a method in treating poetic texts, as it is based on presenting poetic texts from the collection of Al-Abbas bin Mardas, explaining the features of his poetic vision, and examining all the external factors that surrounded the poet and influenced his vision and all his thoughts. The results of this research was that Al-Abbas Ibn Mardas revealed some of the positions that he found contradicted the authentic Arab value before the advent of Islam, and the poet Abbas bin Mardas tr