With 549,393 new cases recorded in 2018, bladder cancer is one of the most common malignancies worldwide. Urinary bladder cancer is the cause of about 3 percent of all new cancer diagnoses and 2.1 percent of all cancer deaths. This study aims to evaluate the efficiency of the N-myc downstream-regulated gene 1(NDRG1) as a biomarker for bladder cancer patients in the Iraqi population. One hundred individuals in the case-control study were enrolled and divided into two groups. The first group included 50 patients diagnosed with a bladder mass and investigated by undergoing cystoscopy examination for transurethral resection of bladder tumor (TURB). The second group included 50 healthy individuals who had normal bladder tissue. The resul

Background: Cancer is rising as a significant global public health concern. The global cancer burden is escalating, exerting considerable physical, emotional, and financial strain on people, families, communities, and healthcare systems. Objective: To explore the challenges faced in cancer management from the perspectives of physicians. Methods: A qualitative study was conducted between November 2024 and February 2025. Physicians were recruited from three different centers in Baghdad and Karbala using purposive and snowball sampling. The data collection was concluded upon reaching a saturation point. Results: This study included twenty-six oncologists. There was about parity between the two genders, with a slight male predominance.

Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective

Rapid and accurate identification of Methicillin Resistant Staphylococcus aureus is essential in limiting the spread of this bacterium. The aim of study is the detection of Methicillin Resistant Staphylococcus aureus (MRSA) and determining their susceptibility to some antimicrobial agent. A total of fifty clinical Staphylococcus aureus, isolated from the nose of health work staff in surgery unit of Kalar general hospital and from ear of patients attended to the same hospital. The susceptibilities of isolates were determined by the disc diffusion method with oxacillin (1 ?g) and cefoxitin (30 ?g), and by the mannitol salt agar supplemented with cefoxitin (MSA-CFOX), susceptibilities of isolates to other antimicrobial agent were determined b

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob