Influence of the co-exposure of microplastics and tetrabromobisphenol A on human gut: Simulation in vitro with human cell Caco-2 and gut microbiota.

Microplastics (MPs) pollution becomes an emergent threat to the ecosystem, and its joint effect with organic contaminants will cause more severe consequences. Recently, MPs has been observed in human feces, suggesting that we are exposed to an uncertain danger. In this study, the joint effect of polyethylene microplastics particles (PEMPs) and Tetrabromobisphenol A (TBBPA) on human gut was explored through the simulation experiment in vitro with human cell Caco-2 and gut microbiota. The toxicity of TBBPA and PEMPs on Caco-2 human cells was considered by physiological and biochemical indexes such as cell proliferation, cell cycle, reactive oxygen species, lactate dehydrogenase release, and mitochondrial membrane potential. Besides, microbial community diversity, community structure, and function changes of gut microbiota were investigated using Illumina 16S rRNA gene MiSeq sequencing to reveal the influence of TBBPA and PEMPs on human gut microbiota. The results indicated that both PEMPs and TBBPA would deteriorate the status of Caco-2 cells, and TBBPA played a major role in it; meanwhile, PEMPs affected Caco-2 cells at high concentrations. Particularly, TBBPA and PEMPs exhibited a joint effect on Caco-2 cells to a certain degree. TBBPA selectivity inhibited the growth of gram-positive bacteria such as Enterococcus and Lactobacillus, contributing to the thriving of gram-negative bacteria such as Escherichia and Bacteroides. The existence of PEMPs would enhance the proportion of Clostridium, Bacteroides, and Escherichia. Community composition changed dramatically with the interference of PEMPs and TBBPA; this was undesirable to the healthy homeostasis of the human gut. PICRUSt analysis determined both PEMPs and TBBPA interfered with the metabolism pathways of gut microbiota. Hence, the threat of MPs and TBBPA to humans should arouse vigilance.

Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model.

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children's health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results.