Abstract Breast cancer is a foremost cause of death worldwide, ranks fifth among causes of death from all types of cancers; this is the most common cause of cancer death in women among both developing and developed countries. Breast cancer ranks first among the most frequent cancers in women of Ethiopia. In spite of the high incidence, mortality rate, and survival status among breast cancer patients was not determined in Ethiopia. The purpose of this study was to identify factors affecting the time to death among breast cancer patients attending anti-cancer treatment from September 2015 till December 2018 at Ayder Comprehensive Specialized Hospital. Methods: Hospital-based retrospective cohort study was conducted among breast cancer patients. Kaplan-Meier survival curve together with log-rank test was deployed to test for variations in the survival among predictor variables. Cox regression was used at a 5% level of significance to determine the net effect of each independent variable on the time to death of breast cancer clients. Results: From the Cox proportional model, patients with age, educational status, residence, Baseline Tumor size &Pathology type (LIC) were found to be a statistically significant effect (p < 0.05) on the risk of mortality due to breast cancer and the median survival time of breast cancer patient was 34.50 months. Conclusion: the finding of this study showed that age, educational status, residence, Baseline Tumor size &Pathology type (LIC) were influential affecting time to death of breast cancer patient at the Hospital. It is recommended to make interventions based on these risk factors.
Background and Objectives: In March 2020, approximately 57 million children were affected by massive school closures in the wake of the SARS-CoV-2 pandemic. Many child advocates expressed concerns about the impact of physical school closures and transition to virtual learning on school-aged children's mental health and well-being, particularly those who utilized resources, such as counselling or special education, within the school system. This systematic review was done to identify a) the effect and impact of school closures on the mental health of children in grades K-12, if any, and b) to guide future research on the topic. Methods: A systematic review focused on published articles addressing the effect that COVID-19 related school closures and transition to virtual learning had on school-aged children's and adolescents' mental health. Inclusion criteria included: human studies, scholarly papers, school-aged children, SARS-CoV-2 research, mental health impacts, an article written in English, and research-based in the United States. Exclusion criteria included: not human studies, studies not available in English, individuals over 18 years old, and SARS-CoV or MERS-CoV research. The search was conducted between March 20, 2021, and April 18, 2021. Articles were further screened utilizing the PRISMA flow diagram. Once screened, included articles were reviewed by one member of the research team and a PICO-style analysis was used for each article. After the initial review, a total of 11 articles were included in this systematic review. Learning Points Discussion: We identified several areas of a child's life that school closures limited access to, such as reduced-cost meals, mental health services, and special education. Since the school closures and subsequent transition to online schooling, these resources became unavailable or limited by virtual technology. Children from lower socioeconomic backgrounds and marginalized communities were particularly vulnerable to negative mental health changes due to school closures and decreased access to school-based resources. These individuals belonging to a lower socioeconomic class are more likely to have inadequate computers to utilize in-home learning, have more unstable internet connections, and are less likely to have a caregiver that can stay home to help with their distanced learning. This research will be vital in understanding any adverse effects on children and shaping the future development of school-based programs and their funding.
HIV/AIDS continues to be a major public health concern and cause of death in the world. Even though WHO recommended viral load testing as the preferred monitoring approach to diagnose and confirm ARV treatment failure, but in most cases, factors influencing the trend of viral load were not well identified. The main objective of this study was to modeling the change of viral load and identifying its associated factors among HIV positive patients. In this retrospective longitudinal data analysis, data was collected from 287 HIV positive patients registered for ART between January 2017 and June 2019 in Zewditu hospital and unstructured covariance structure was parsimonious for the data. Linear mixed model with different random effect were applied to the data. Linear mixed model with random intercept and slope were selected as a best model to fit the data based on different model selection criteria. The findings of the study revealed that there was a decrement over time in the log VL of patients with HIV on ART. Furthermore, time, baseline CD4 count, WHO clinical stage, functional status of the patient, adherence, smoking status, initial ART Regimen and time interaction with adherence and WHO stage were found to be significant predictors of log VL evolution. Linear mixed model with random intercept and slope were selected to fit the data based on different information criteria. There was a significant variation in log VL of patients at baseline and through ART treatment time. Therefore, patients should take ART regimens with good adherence to decrease their viral load over time.
The healthcare supply chain begins with the production of medical products. Different stages of supply chain flow may have their own objectives. The healthcare supply chain management process can be inefficient and fragmented because supply chain goals are not always matched within the medical requirements. To choose a certain product, healthcare organizations must consider a variety of demands and perspectives. The MRI team contributes a wealth of knowledge to the essential task of improving our clients’ supply chain and logistics performance. MRI is considered the most efficient medical device for the acquisition and treatment of medical imaging. Medical devices and manufacturing are classified as crucial factors for the supply chain in radiology. The objective of this paper is to present an approach that consists of modeling in 3D a segment of a stenosing aorta with a parallel treatment in order to determine the cost and the time of treatment for the reporting, which can be considered a promoter element to optimize the course of supply chain from manufacture to medical industry. Acceleration has sought to reduce the imaging speed in parallel architecture convergence for cardiac MRI. The image computation time is comparatively long owing to the iterative reconstruction process of 3D models. The aim of this paper is to suggest a CPU-GPU parallel architecture based on multicore to increase the speed of mesh generation in a 3D model of a stenosis aorta. A retrospective cardiac MRI scan with 74 series and 3057 images for a 10-year-old patient with congenital valve and valvular aortic stenosis on close MRI and coarctation (operated and dilated) in the sense of shone syndrome. The 3D mesh model was generated in Standard Tessellation Language (STL), as well as the libraries used to operate with Pymesh and Panda, and the time spent in tracing, decomposing, and finalizing the mesh crucially depends on the number of nuclei used in the parallel processing and the mesh quality chosen. A parallel processing based on four processors are required for the 3D shape refinement.To improve the efficiency of image processing algorithms and medical applications acquired in real-time analysis and control, a hybrid architecture (GPU/GPU) was proposed. The response time of parallel processing based on the CPU-GPU architecture used at the mesh level to achieve a 3D model is critically dependent on the number of kernels required.
Abstract: Obesity (BMI greater than 30.0 kg/m2) is always a trending public health problem and different studies have linked it to the development of heart diseases. However, overweight (BMI between 25.0 kg/m2 and less than 30.0 kg/m2) has very few studies linking it to the development of heart diseases, coupled with the overweight/obesity paradox phenomena which still causes controversy. Therefore, the purpose of this paper is to determine the relationship between overweight and congestive heart failure/coronary heart diseases in the United States. To determine this relationship, the 2011–2012 National Health and Nutrition Examination Survey (NHANES) dataset, which surveyed 9756 individuals in the United States was utilized. The dataset was analyzed using Software for Statistics and Data Software (STATA) and Pearson’s chi-squared test. The p-value using the t-test command in STATA for all these variables was <0.001, which indicated that there is a significant relationship between overweight and congestive heart failure/coronary heart disease across gender, ethnicity, and socioeconomic status. Although the development of congestive heart failure/coronary heart disease in overweight individuals was confirmed with this study, it is surprising to realize that the socioeconomic status of an individual also plays a role. Further research is necessary to determine how a lower or higher socioeconomic status causes overweight leading to heart diseases.
