Objectives: To assess the quality of life for adult patients with peptic ulcers in the city of Sulaimani.
Methodology: A descriptive study, using the assessment approach was conducted on patients with peptic ulcer
disease from January 12th, 2009 to September 30th, 2009. A purposive "non-probability" sample of (100) paƟents
(males and females) with peptic ulcers who attended Kurdistan Center for Gastroenterology and Hepatology were
selected for the study. A questionnaire was adapted from the World Health Organization quality of life questionnaire
(1998) for the purpose of the study. It is comprised of (3) parts that included sociodemographic characteristics form,
medical history form and adult peptic ulcers patients' quality of life (QoL) questionnaire. A pilot study was carried out
on (10) paƟents for the period of January 12th through January 28th, 2009 to determine the study quesƟonnaire's
reliability through the use of stability (test-retest method). A panel of (14) experts was involved in the determination
of the questionnaire's content validity. Data were analyzed through the application of the descriptive statistical data
analysis approach (frequency, percentage, mean of scores) and inferential statistical data analysis approach (Pearson
correlation coefficient and chi-square).
Results: The findings of the study indicated that quality of life was affected in physical domain and that age, gender,
marital status, and monthly income had significant relationship with quality of life domains.
Recommendations: The study recommends that young adults, particularly Individuals with blood group (O) should be
highly aware from gastrointestinal (GI) symptoms and avoids risk factors for the development of peptic ulcer disease
(PUD). Also, the study recommends providing health assurance and conducting further multi centers studies in order
to demonstrate the differences in QoL issues for PUD patients.
Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration
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This case series aims to evaluate patients affected with post COVID‐19 mucormycosis from clinical presentation to surgical and pharmacological treatment to improve the disease prognosis.
This case series was conducted at a specialized surgery hospital in Baghdad Medical City for over 10 months. Fifteen cases who had mild to severe COVID‐19 infections followed by symptoms similar to aggressive periodontitis, such as mobility and bone resorption around the multiple maxillary teeth, were included in this case series.
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Ebastine (EBS) is a non-sedating antihistamine with a long duration of action. This drug has predominantly hydrophobic property causing a low solubility and low bioavailability. Surface solid dispersions (SSD) is an effective technique for improving the solubility and dissolution rate of poorly soluble drugs by using hydrophilic water insoluble carriers.
The present study aims to enhance the solubility and dissolution rate of EBS by using surface solid dispersion technique. Avicel® PH101, Avicel® PH 102, croscarmellose sodium(CCS) and sodium starch glycolate(SSG) were used as water insoluble hydrophilic carriers.
The SSD formulations of EBS were prepared by the solvent evaporation method in different drug: carrier
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