Narcissin induces developmental toxicity and cardiotoxicity in zebrafish embryos via Nrf2/HO-1 and calcium signaling pathways.

Narcissin is a natural flavonoid from some edible and traditional medicinal plants. It has been proven to have multiple biological functions and exhibits potential therapeutic effects on hypertension, cancer, and Alzheimer's disease. However, the toxicity of narcissin is largely unknown. Here, we revealed that narcissin treatment led to reduced hatchability, increased malformation rate, shorter body length, and slowed blood flow in zebrafish. Furthermore, bradycardia, pericardial edema, increased SV-BA distance, diminished stroke volume, ejection fraction, and ventricular short-axis shortening rate were also found. A large accumulation of ROS, increased apoptotic cells, and histopathological changes were detected in the heart region. Moreover, the gene expression profiles and molecular docking analysis indicated that Nrf2/HO-1 and calcium signaling pathways were involved in narcissin-induced toxicity. In conclusion, here we provide the first evidence that demonstrates narcissin-induced developmental toxicity and cardiotoxicity in zebrafish via Nrf2/HO-1 and calcium signaling pathways for the first time.

Isoalantolactone protects against ethanol-induced gastric ulcer via alleviating inflammation through regulation of PI3K-Akt signaling pathway and Th17 cell differentiation.

Gastric ulcer (GU) is one of the most prevalent digestive system diseases in humans, and it has been linked to inflammation. Previous studies have demonstrated the anti-inflammatory potential of isoalantolactone (IAL), a sesquiterpene lactone isolated from Radix Inulae. However, the pharmacological effects of IAL on GU and its mechanism of action are still unclear. Hence, the present study is aimed to investigate the anti-inflammatory potential of IAL on GU. Firstly, we assessed the effect of IAL on ethanol-induced injury of human gastric epithelial cells and the levels of inflammatory cytokines in cell culture supernatants. Then, the anti-inflammatory effects of IAL were confirmed in vivo using zebrafish inflammation models. Furthermore, the mechanism of IAL against GU was preliminarily discussed through network pharmacology and molecular docking studies. Quantitative real-time PCR assays were also used to confirm the mechanism of IAL action. ALB, EGFR, SRC, HSP90AA1, and CASP3 were found for the first time as the key targets of the IAL anti-GU. PI3K-Akt signaling pathway and Th17 cell differentiation were identified to play a crucial role in the anti-GU effects of IAL. In conclusion, we found that IAL has anti-inflammatory effects both in vitro and in vivo, and showed potential protective effects against ethanol-induced GU.

Complex networks and deep learning for EEG signal analysis.

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.