Improving Dietary Zinc Bioavailability Using New Food Fortification Approaches: A Promising Tool to Boost Immunity in the Light of COVID-19.

Zinc is a powerful immunomodulatory trace element, and its deficiency in the body is closely associated with changes in immune functions and viral infections, including SARS-CoV-2, the virus responsible for COVID-19. The creation of new forms of zinc delivery to target cells can make it possible to obtain smart chains of food ingredients. Recent evidence supports the idea that the optimal intake of zinc or bioactive compounds in appropriate supplements should be considered as part of a strategy to generate an immune response in the human body. Therefore, controlling the amount of this element in the diet is especially important for populations at risk of zinc deficiency, who are more susceptible to the severe progression of viral infection and disease, such as COVID-19. Convergent approaches such as micro- and nano-encapsulation develop new ways to treat zinc deficiency and make zinc more bioavailable.

Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases

The prediction of new cases of infection is crucial for authorities to get ready for early handling of the virus spread. Methodology Analysis and forecasting of epidemic patterns in new SARS-CoV-2 positive patients are presented in this research using a hybrid deep learning algorithm. The hybrid deep learning method is employed for improving the parameters of long short-term memory (LSTM). To evaluate the effectiveness of the proposed methodology, a dataset was collected based on the recorded cases in the Russian Federation and Chelyabinsk region between 22 January 2020 and 23 August 2022. In addition, five regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE), coefficient of determination (R Square), coefficient of correlation (R), and mean bias error (MBE) when compared with the five base models. The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of SARS-CoV-2.