Assessment of the Effect of Leonurine Hydrochloride in a Mouse Model of PCOS by Gene Expression Profiling.

Polycystic ovary syndrome (PCOS) is an endocrine disease commonly associated with metabolic disorders in females. Leonurine hydrochloride (Leo) plays an important role in regulating immunity, tumours, uterine smooth muscle, and ovarian function. However, the effect of Leo on PCOS has not been reported. Here, we used dehydroepiandrosterone to establish a mouse model of PCOS, and some mice were then treated with Leo by gavage. We found that Leo could improve the irregular oestros cycle of PCOS mice, reverse the significantly greater serum testosterone (T) and luteinising hormone (LH) levels, significantly reduce the follicle-stimulating hormone (FSH) level, and significantly increase the LH/FSH ratio of PCOS mice. Leo could also change the phenomenon of ovaries in PCOS mice presented with cystic follicular multiplication and a lacking corpus luteum. Transcriptome analysis identified 177 differentially expressed genes related to follicular development between the model and Leo groups. Notably, the cAMP signalling pathway, neuroactive ligand-receptor interactions, the calcium signalling pathway, the ovarian steroidogenesis pathway, and the Lhcgr, Star, Cyp11a, Hsd17b7, Camk2b, Calml4, and Phkg1 genes may be most related to improvements in hormone levels and the numbers of ovarian cystic follicles and corpora lutea in PCOS mice treated by Leo, which provides a reference for further study of the mechanism of Leo.

E2ENNet: An end-to-end neural network for emotional brain-computer interface.

Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet. Methods: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions. Results: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset. Conclusion: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals. Significance: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.