Effects of Probiotics and Gut Microbiota on Bone Metabolism in Chickens: A Review.

Broiler leg diseases are a common abnormal bone metabolism issue that leads to poor leg health in growing poultry. Bone metabolism is a complicated regulatory process controlled by genetic, nutritional, feeding management, environmental, or other influencing factors. The gut microbiota constitutes the largest micro-ecosystem in animals and is closely related to many metabolic disorders, including bone disease, by affecting the absorption of nutrients and the barrier function of the gastrointestinal tract and regulating the immune system and even the brain-gut-bone axis. Recently, probiotic-based dietary supplementation has emerged as an emerging strategy to improve bone health in chickens by regulating bone metabolism based on the gut-bone axis. This review aims to summarize the regulatory mechanisms of probiotics in the gut microbiota on bone metabolism and to provide new insights for the prevention and treatment of bone diseases in broiler chickens.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.