COVID-19 Prediction Infrastructure Using Deep Learning

Coronavirus can lead to respiratory illnesses ranging from mild to severe, and even death, which makes early detection critical. However, current COVID-19 (Coronavirus Disease 2019) detection methods are not only expensive but also time-consuming. This poses a challenge, especially with an increasing number of patients and demand for testing kits. Waiting for test results for a few days is not ideal, as the outbreak can spread quickly in the meantime. To address this issue, we propose a COVID-19 prediction infrastructure using deep learning. This innovative android-based application uses a Convolutional Neural Network model, trained on a custom dataset with an accuracy of 97 percent, to predict whether COVID-19 is present or not. With this fast and low-cost approach, users can quickly detect COVID-19 and take appropriate actions to reduce the risk of transmission

Antioxidant and hepatorenal protective effects of bee pollen fractions against propionic acid‐induced autistic feature in rats

In the brain, propionic acid (PA) can cross cell membranes and accumulate within cells, leading to intracellular acidification, which may alter neurotransmitter release (NT), communication between neurons, and behavior. Such elevation in levels of PA constitutes a neurodevelopmental metabolic disorder called propionic acidemia, which could clinically manifest as autism. The purpose of this study was to investigate the protective effects of different fractions of bee pollen (BP) on PA‐induced autism in rats, and to evaluate their effects on the expression of liver and renal biomarkers. Groups of rats received treatments of different fractions of BP at a dose of 250 mg/kg of body weight/day for a period of 1 month. Normal control group I and group II were orally administered with phosphate‐buffered saline and propionic acid, respectively, for 3 days. BP contains various health‐promoting phenolic components. Different fractions of BP administered pre‐ and post‐treatment with PA showed significant reduction in the levels of liver and renal biomarkers (p < .05). Also, a significant enhancement in the levels of glutathione S‐transferase (GST), catalase CAT), and ascorbic acid (VIT C) was observed. Supplementation with BP significantly reduced biochemical changes in the liver, kidneys, and brain of rats with PA‐induced toxicity. It exhibited protective effects against oxidative damage and reactive oxygen species produced by PA‐induced adverse reactions in rats. Taken together, our study shows that BP possesses protective effects in PA‐induced liver and kidney damage

THE IMPACTS OF FLUCTUATIONS IN PUBLIC REVENUE AND EXPENDITURES ON ECONOMIC GROWTH IN PAKISTAN: AN IMPULSE RESPONSE APPROACH: Zia Ur Rahman, Musarat Abbas, Madiha Riaz, Saeed Ur Rahman

Allocation of public revenue among productive and non-productive expenditure and its impact on GDP are not extensively studies in Pakistan. In this study, we examine the relationship between government revenue, productive and nonproductive expenditure and economic growth and sustainability over the period of 1978-2018 with the help of Johansen co-integration, error correction test and Impulse Response Function. The result of the study affirms a significant long run and the short run relationship between the fiscal instruments and the economic growth in Pakistan. Finally, impulse response illustrates that when direct tax is higher than GDP, productive and unproductive expenditures are also increased. It has also been observed some unproductive expenditures smooth the way for the economic growth. For the future better implication of fiscal policy and to achieve economic growth and development Government should take steps to increase its revenue through taxes. Tax collections can be increased by proper utilization of tax revenue and by educating people that tax is the responsibility not the penalt