Development of a UPLC-MS/MS method for the determination of active ingredients of Shuang Hu in rat blood and its application in pharmacokinetics.

A simple and sensitive method using in vivo microdialysis coupled with UPLC-MS/MS was established to evaluate the pharmacokinetics of Shuang Hu tincture (SHZTN). Xevo TQ-S was used to analyze the active ingredients of mesaconitine, hypaconitine, 4-hydroxycinnamic acid, ferulic acid and N-(2, 3-dimethyl phenyl)-2- aminobenzoic acid of SHZTN. Samples were prepared using a methanol precipitation method and the internal standards lannaconitine and p-hydroxybenzoic acid were added. The method validation was conducted according to the guidelines of the Pharmacopoeia of China. A good linear range was obtained in the range of 1-2,000 ng/ml. The intra-day and inter-day precisions were less than 14.7%, and the accuracy range of all the analytes was -10.5-9.3%. The recovery of each analyte was over 95.5%, and matrix effects can be neglected. After a single dose of 20 mg/kg SHZTN, the area under the curve and peak concentration of the five active ingredients were significantly increased by transdermal compared with oral administration, which indicated the high bioavailability of SHZTN. The time to peak concentration of all compounds was <3.4 h, and the half-life was <15.4 h, which indicated that the five compounds have the best absorption and rapid elimination. The method was successfully developed and applied to the pharmacokinetic study of SHZTN.

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.