Abstract

Coronavirus has affected many people around the world and caused an increase in the number of hospitalized patients and deaths. The prediction factor may help the physician to classify whether the patient needs more medical attention to decrease mortality and worsening of symptoms. We aimed to study the possible relationship between C reactive protein level and the severity of symptoms and its effect on the prognosis of the disease. And determine patients who require closer respiratory monitoring and more aggressive supportive therapies to avoid poor prognosis. The data was gathered using medical record data, the patient's medical history, and the onset of symptoms, as well as a blood sample to test the

  Breast cancer is the most common cancer among women over the world. To reducing reoccurrence and mortality rates, adjuvant hormonal therapy (AHT) is used for a long period. The major barrier to the effectiveness of the treatment is adherence. Adherence to medicines among patients is challenging. Patient beliefs in medications can be positively or negatively correlated to adherence. Objectives: To investigate the extent of adherence and factors affecting adherence, as well as to investigate the association between beliefs and adherence in women with breast cancer taking AHT. Method: A cross-sectional study included 124 Iraqi women with breast cancer recruited from Middle Euphrates

Bacteriophages have the potential to eliminate both antibiotic-resistant and sensitive bacteria; as a result, they have become a major focus of such research. In contrast to antibiotics, which assault the entire bacterial population without discrimination, bacteriophages have a limited set of characteristics that allow them to target infectious microbes while avoiding friendly species (commensal microbiota). Nevertheless, large groups of naturally occurring bacteriophages that are well-differentiated and selective for the most clinically recognized pathogenic bacterial strains are required. Utilizing genetic engineering techniques that modify the target phage genome to synthesize phages with known characteristics in a brief period o

Leiomyosarcoma (smooth muscle tumor) is a rare type of a malignant neoplasm of smooth
muscle. It is a resistant cancer, generally not very responsive to chemotherapy, radiationand surgery.The
aim of this study was to evaluate photodynamic therapy (PDT) as a possible basis for treatment using
(650 nm)diode laser with methyleneblue (MB) as a photosensitizer on leiomyosarcoma (LMS)cell line
(L20B) in vitro. The cytotoxic effects of 650nm diode laser 0.052 W/cm2 (power density) at different
exposure time (10, 20, and 30 min) with methylene blue at different concentrations (0.01% ,0.1 % ,1%)
was investigated on LMS cell line (L20 B). The cytotoxic effect of laser was increased with increasing
irradiation time for a certai

The research utilizes data produced by the Local Urban Management Directorate in Najaf and the imagery data from the Landsat 9 satellite, after being processed by the GIS tool. The research follows a descriptive and analytical approach; we integrated the Markov chain analysis and the cellular automation approach to predict transformations in city structure as a result of changes in land utilization. The research also aims to identify approaches to detect post-classification transformations in order to determine changes in land utilization. To predict the future land utilization in the city of Kufa, and to evaluate data accuracy, we used the Kappa Indicator to determine the potential applicability of the probability matrix that resulted from

Each Intensity Modulated Radiation Therapy (IMRT) plan needs to be tested and verified before any treatment to check its quality. Octavius 4D-1500 phantom detector is a modern and qualified device for quality assurance procedure. This study aims to compare the common dosimetric criteria 3%/3 mm with 2%/2 mm for H&N plans for the IMRT technique. Twenty-five patients with head and neck (H&N) tumor were with 6MV x-ray photon beam using Monaco 5.1 treatment planning software and exported to Elekta synergy linear accelerator then tested for pretreatment verification study using Octavius 4D-1500 phantom detector. The difference between planned and measured dose were assessed by using local and global gamma index (GI) analysis method at

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