Chronic renal disease (CRD) is a pathophysiologic process with multiple etiologies, resulting in the inexorable attrition of Nephron number and function and frequently leading to end-stage renal disease (ESRD). In turn, ESRD represents a clinical state or condition in which there has been an irreversible loss of endogenous renal function, of a degree sufficient to render the patient permanently dependent upon renal replacement therapy (dialysis of transplantation) in order to avoid life threatening uremia, reflecting a dysfunction of all organ systems as a result of untreated or under treated acute or chronic renal failure. The current study was involved 80 patients, the age range within 25-70 ye

      Ovarian cystic lesions are one of the most common pathologic disorders in gynecology and a common reason for surgery. The purpose of the study was to determine the histopathologic characteristics of benign cystic ovarian lesions and their correlation to age, type, laterality, locularity, and size of ovarian cystic lesions. This is a retrospective study carried out on 100 cases from the archive in the Imam Kadhimian medical city and the educational laboratories of Baghdad medical city, out of 100 patients, the most common age group that underwent cystectomy was 20-40 years old. The vast majority of the cysts were non-neoplastic (67%) while the neoplastic cysts occupy 33% of all cysts. The most common non-neoplastic cyst was cor

Background: Endometrial Cancer (EC) is the malignant tumor originating from endometrium cell (lining of the uterus). EC incidence and mortality have increased in recent years. Routinely used methods for EC diagnosis and treatment are histopathological tissue culture after surgery and postoperative radiotherapy, however there is still not enough efficient treatment for recurrence or progression of this disease. So, there is a critical need for further EC identification by new biological ways for the prognostic diagnosis of it. Objective: This study aimed to look for ways by which could help in diagnosis of EC before the hysterectomy. Materials and Methods: 55 patients with EC and 57 healthy women were involved in this study (up to 45 years)

This study was designed to determine the correlation between Y chromosome azoospermia factor (AZF) subregions microdeletions and oligozoospermia in infertile men. Subjects included 50 infertile men with oligozoospermia who had been referred to the Fertility Center and infertility treatment in Kamal Al-Samarrai Hospital\Baghdad health office-Iraq. DNA was extracted from blood samples. Polymerase chain reaction (PCR) amplification of 3 loci spanning the AZFa, AZFb and AZFc subregions of the Y chromosome using sY84, sY127 and sY254 and were performed. The frequency of deletions involving AZFa subregion of the Y-chromosome was found in twelve of the patients (24%) in oligozoospermic infertile Iraqi men. While the other subregion (AZFb and AZ

Psoriasis is a common, chronic, immune-mediated skin disease with systemic pro-inflammatory activation.  This study was designed to estimate the level of two cytokines, Interleukin-36 (IL-36) and Interleukin-10 (IL-10), in psoriasis female patients. The study was accomplished on 50 Iraqi patients with psoriasis who were referred to the consulting clinic at Al-Yarmouk Teaching Hospital during the period from November 2018 to March 2019. These patients were diagnosed under the supervision of dermatologists. For the purpose of comparison, the study included 30 healthy women as a healthy control group. The serum levels of cytokines  were measured using the enzyme-linked immunosorbent technique (ELISA).The results of this study show

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a