Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.
Proton pump inhibitors (PPIs) are widely prescribed medications for the management of various gastrointestinal disorders, primarily gastroesophageal reflux disease (GERD) and peptic ulcers. However, recent concerns have emerged regarding their potential adverse effects on kidney function and their role in the progression of chronic kidney disease (CKD). This systematic review aims to comprehensively analyze the existing literature to assess the impact of PPI use on kidney function and CKD progression. We took information from PubMed, PubMed Central (PMC), and Google Scholar articles from the last 10 years, from 2013 to 2023, and looked for links between PPI use and a number of kidney-related outcomes. These included acute kidney injury, a drop in the estimated glomerular filtration rate (eGFR), and new cases of CKD. The findings of this systematic review highlight the need for a thorough evaluation of the benefits and risks associated with PPI use, particularly in patients with pre-existing kidney conditions, in order to inform clinical decision-making and improve were taken out and looked at to see if there were any links between PPI use and different kidney-related events, such as acute kidney injury, a drop in the estimated eGFR, and the development of CKD. The review also explores potential mechanisms underlying PPI-induced nephrotoxicity. The findings of this systematic review highlight the need for a thorough evaluation of the benefits and risks associated with PPI use, particularly in patients with pre-existing kidney conditions, in order to inform clinical decision-making and improve patient care. Further research is warranted to better understand the complex interplay between PPIs, kidney function, and CKD progression.
The study aims to assess the health-related Quality of Life (HRQOL) and its association with socio-demographic factors among the Afghan refugees residing in Quetta, Pakistan. For this purpose, a cross-sectional, descriptive study design by adopting Euro QOL five dimensions questionnaire (EQ-5D) for the assessment of HRQOL was conducted by approaching Afghan refugees from the camp and other areas of Quetta, Pakistan. Furthermore, this study also involved descriptive analysis to expound participant's demographic characteristics while inferential statistics (Kruskal-Wallis and Mann-Whitney test, P < 0.05) were used to compare EQ-5D scale scores. All analyses were performed using SPSS v 20. Herein, a total of 729 participants were enrolled and were subsequently (n = 246, 33.7%) categorized based on their age of 22-31 years (31.30 ± 15.40). The results of mean EQ-5D descriptive score (0.85 ± 0.20) and EQ-VAS score (78.60 ± 11.10) indicated better HRQOL in the current study respondents as compared to studies conducted in other refugee camps around the globe. In addition, demographic characteristics including age, marital status, locality, years of living as refugees, life as a refugee residing out of Pakistan, place of residence in Afghanistan, educational qualification, occupation, and arrested for crime were the statistically significant predictors (P < 0.05) of EQ-5D index scores. However, gender, living status, monthly income, preferred place of treatment were non-significant predictors (P > 0.05). The results of current study provided evidence for a model that correlated with participant's socio-demographic information and HRQOL. Moreover, this study also revealed a baseline assessment for the health status of Afghan refugees, interestingly, these results could be applied for improving HRQOL of the given participants. In conclusion, the HRQOL of Afghan refugees residing in Quetta, Pakistan can largely be improved by providing adequate healthcare facilities, education and employment opportunities, mental and social support, and providing adequate housing and basic necessities of life.