Probiotic Supplements and Food Products: Comparison for Different Targets

Background and Objective: Currently, probiotics are mainly used in processed foods or nutritional supplements mainly due to their impact on health. The probiotic markets have a considerable share either in food or drug industry. In this article, drug supplements and food products containing probiotic microorganisms are considered in a comparative approach from different aspects including functional, efficacy, hedonistic and economical.Results and Conclusion: However, the impact of ingesting probiotics via food products or drug supplements is not actually the same from consumer’s point of view as well as from clinical efficacy. Consumer attitudes appear to be reflected in expectations on nutritional, functional and health effects side and also from the point of nutrition economics.Conflict of interest: The authors declare no conflict of interest

Predicting the status of COVID-19 active cases using a neural network time series

The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model