Introduction: Breast cancer is a significant global health concern, affecting millions of women worldwide. While advancements in diagnosis and treatment have improved survival rates, the impact of this disease extends beyond physical health. It also significantly influences a woman's lifestyle and overall well-being. Objectives: The current study intends to analyze the lifestyle of breast cancer patients who are receiving therapy or are being followed up at the Oncology Teaching Hospital in Medical City, Baghdad, Iraq. Method: The present study uses a descriptive design with an application of an evaluation approach. A convenience sample of 100 women with breast cancer was selected from the Teaching Oncology Hospital at the Medical C

Background: Gallstone disease (GSD) is a significant global health burden with variable prevalence influenced by metabolic, genetic, and infectious factors. Increasing evidence suggests that Gram-positive bacteria, particularly Staphylococcus aureus and Enterococcus species, contribute to gallstone pathogenesis through enzymatic activity and biofilm formation. Objectives: To characterize Gram-positive bacteria within gallstones from Iraqi patients, evaluate their biofilm-forming capacity, and analyze the relationship between bacterial colonization, gallstone type, and cholesterol levels. Methods: A total of 100 gallstones were obtained from patients undergoing elective cholecystectomy between October 2024 and March 2025. Stones were

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)

AH Haider R, N Adil A, AW Makram M, AK Abdulkaleq S, 2010

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

This study included 50 blood samples that were collected from patients with age ranged between 35-65 years. Thirty samples were collected from patients with Type 2 Diabetes Mellitus (T2DM), while 20 blood samples were collected from healthy individuals as a control sample. The polymorphism results of TGF-β1 gene in codon 10: +869*C/T position by using amplification refractory mutation system (ARMS-PCR) showed that the T allele was suggested to have a protective effect, while C allele was associated with an increased risk of T2DM. The TT and CT were suggested to have a protective effect, while CC genotype was associated with an increased risk of T2DM. The polymorphism results of TGF-β1 gene in codon 25: +915*G/C position in samples