Chemopreventive role of probiotics against cancer: a comprehensive mechanistic review.

Probiotics use different mechanisms such as intestinal barrier improvement, bacterial translocation and maintaining gut microbiota homeostasis to treat cancer. Probiotics' ability to induce apoptosis against tumor cells makes them more effective to treat cancer. Moreover, probiotics stimulate immune function through an immunomodulation mechanism that induces an anti-tumor effect. There are different strains of probiotics, but the most important ones are lactic acid bacteria (LAB) having antagonistic and anti-mutagenic activities. Live and dead probiotics have anti-inflammatory, anti-proliferative, anti-oxidant and anti-metastatic properties which are useful to fight against different diseases, especially cancer. The main focus of this article is to review the anti-cancerous properties of probiotics and their role in the reduction of different types of cancer. However, further investigations are in progress to improve the efficiency of probiotics in cancer treatment.

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.