Protective mechanism of Paeonol on central nervous system

Cerebrovascular diseases involve neuronal damage, resulting in degenerative neuropathy and posing a serious threat to human health. The discovery of effective drug components from natural plants and the study of their mechanism are a research idea different from chemical synthetic medicines. Paeonol is the main active component of traditional Chinese medicine Paeonia lactiflora Pall. It widely exists in many medicinal plants and has pharmacological effects such as anti-atherosclerosis, antiplatelet aggregation, anti-oxidation, and anti-inflammatory, which keeps generally used in the treatment of cardiovascular and cerebrovascular diseases. Based on the therapeutic effects of Paeonol for cardiovascular and cerebrovascular diseases, this article reviewed the pharmacological effects of Paeonol in Alzheimer's disease, Parkinson's disease, stroke, epilepsy, diabetes encephalopathy, and other neurological diseases, providing a reference for the research of the mechanism of Paeonol in central nervous system diseases.

Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants

Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.