Oxidative stress is performing an essential role in developing Alzheimer's disease (AD), and age-related disorder and other neurodegenerative diseases. In existing research, we have aimed at investigating the daidzein (4',7-dihydroxyisoflavone) effect (10 and 20 mg/kg of body weight), as a free radical scavenger and antioxidant in streptozotocin (STZ) infused AD in rat model. Daidzein treatment led to significant improvement in intracerebroventricular-streptozotocin (ICV-STZ)-induced memory and learning impairments that was evaluated by Morris water maze test and spontaneous locomotor activity. It significantly restored the alterations in malondialdehyde, catalase, superoxide dismutase, and reduced glutathione levels. In addition, histopathological observations in cerebral cortex and hippocampal areas confirmed the neuroprotective effect of daidzein. These outcomes provide experimental proof showing preventive effect of daidzein on memory, learning dysfunction and oxidative stress in case of ICV-STZ rats. In conclusion, daidzein offers a potential treatment module for various neurodegenerative disorders with regard to mental deficits like AD.
Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.