ABSTRACT
@#【Objective】 To provide a new idea for the treatment of depression by summarizing the antidepressant effect and mechanism of active ingredients in functional food, and medicine and food homologous products. 【Methods】 The literature related to the antidepressant of functional food or medicine and food homologous products from September 25, 1996 to September 5, 2022 was collected through PubMed, Google Academic, Web of Science, and China National Knowledge Infrastructure (CNKI) databases. After that, their antidepressant active ingredients and mechanism of action were systematically summarized and analyzed. 【Results】 A total of 146 pieces of literature were involved in the study, including 67 plant-derived functional foods or medicine and food homologous products, 32 antidepressant extracts (including 8 flavonoid extracts), and 87 antidepressant active ingredients. The 87 antidepressant active ingredients include 7 terpenes, 22 saponins, 15 flavonoids, 11 phenylpropanoids, 7 phenols, 6 sugars, 8 alkaloids, and 11 others. 【Conclusion】 The study summarized and analyzed the active ingredients and mechanisms of antidepressants in functional foods and medicine and food homologous products, which provides a new vision for the development of new antidepressants and a potential alternative treatment for patients with depression.
ABSTRACT
Objective@#To compare the effects of Cox proportional hazard regression model (Cox model) and extreme gradient boosting model ( XGBoost model ) on the prediction of the mortality of acute paraquat poisoning (APP).@*Methods@#The APP cases admitted to Qingdao Eighth People's Hospital and Shandong Provincial Hospital from January 1st of 2018 to December 1st of 2020 was recruited and divided into a training group and a verification group by a random number table. The Cox model and XGBoost model were established to select the predictors for APP mortality. Receiver operating characteristic ( ROC ) curve was drawn to analyze the predictive power of the two models, and the calibration was evaluated using Hosmer-Lemeshow test.@*Results@#Totally 150 APP cases were recruited. There were 75 cases each in the training group and in the verification group, with 52 and 55 cases died respectively, accounting for 69.33% and 73.33%. The Cox model showed that paraquat intake, the time from taking poison to seeing a doctor, the time for the first perfusion, the time for the first vomiting, aspartate aminotransferase, alanine aminotransferase, serum creatinine, blood urea nitrogen, white blood cell, lactic acid, creatine kinase isoenzymes, glucose, serum calcium and serum potassium were the predictors of APP mortality ( all P<0.05 ). The XGboost model showed that the predictive power of the factors in a descending order were the time from taking poison to seeing a doctor, the time for the first vomiting, the time for the first perfusion, lactic acid, white blood cell, paraquat intake, serum creatinine, serum potassium, serum calcium, creatine kinase isoenzymes, glucose, aspartate aminotransferase, blood urea nitrogen and alanine aminotransferase. The area under curve ( AUC ) of the XGBoost model for predicting was 0.972, which was greater than 0.921 of the Cox model ( P<0.05 ). The predicted results of the Cox model and XGBoost model were consistent with the actual situation ( P>0.05 ). @*Conclusion@#The Cox model and XGBoost model are consistent in predicting the mortality of APP, but the latter is better.