Fuzzy identification of bioactive components for different efficacies of rhubarb by the back propagation neural network association analysis of UPLC-Q-TOF/MSE and integrated effects

Mechanism of rhubarb in the treatment of hyperlipidemia: A recent review

Hyperlipidemia is a metabolic disorder, which is a major risk factor for atherosclerosis, stroke, and coronary heart disease. Although lipid-lowering treatments have been extensively studied, safer treatments with fewer adverse effects are needed. Rhubarb is a traditional Chinese medicine that has lipid-lowering, anti-inflammatory, and antioxidant properties. Disturbance in lipid metabolism is the basis of tissue damage caused by hyperlipidemia and plays a key role in the development of hyperlipidemia; however, the molecular mechanisms by which rhubarb regulates lipid metabolism to lower lipid levels are yet to be elucidated. We conducted this study to summarize the phytochemical constituents of Rheum officinale and provide a comprehensive review of the molecular mechanisms underlying the regulation of lipid metabolism during hyperlipidemia treatment. It was found that rhubarb extracts, including emodin, rhubarb acid, and rhubarb phenol, regulate total cholesterol, triglyceride, TNF-α, and IL-1β levels through signaling pathways such as C/EBP α, 3T3-L1, PPAR α, and AMPK, thereby improving the hyperlipidemic state. This suggests that rhubarb is a natural drug with lipid-lowering potential, and an in-depth exploration of its lipid-lowering mechanism can provide new ideas for the prevention and treatment of hyperlipidemia.

A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

In many universities and colleges, the government is now paying more attention to the quality of teaching assessment, and research on English teaching quality evaluation is becoming increasingly significant. The goal of this paper is to investigate how to use a fuzzy neural network to assess the quality of English education. The research of the model NN and the evaluation of the quality of English teaching is detailed first. A fuzzy NN is an algorithm that examines the quality of English instruction evaluation using fuzzy criteria. In the case results, the data show that the previous evaluation of quality teaching is between the intermediate and the intermediate level, while the research on the evaluation quality of English teaching based on fuzzy NN shows that the evaluation quality is between the intermediate and the advanced level. Therefore, the combination of Fuzzy Rules (FR) and NN methods can effectively improve the quality of college teachers' English teaching evaluation. In college English teaching, FRs can simplify the analysis steps of evaluation of quality teaching, accurately judge the quality of college English teaching evaluation, and provide support for the improvement of evaluation of quality teaching.