Search

Deposition of aggregated amyloid beta (Aβ) protein is considered to be a major causative factor that is associated with the development of oxidative stress and neuroinflammation in the pathogenesis of Alzheimer's disease (AD). Selenium nanoparticles (SeNPs) have been experimentally using for treatment of neurological disease due to their low toxicity, high bioavailability, and multiple bioactivities. This study was conducted to investigate the protective effects of biogenic SeNPs by Lactobacillus casei ATCC 393 against Aβ25–35-induced toxicity in PC12 cells and its association with oxidative stress and inflammation. The results showed that SeNPs had no cytotoxicity on PC12 cells. Moreover, SeNPs entered cells through cellular endocytosis, which effectively attenuated Aβ25–35-induced toxicity in PC12 cells. In addition, compared with Aβ25–35 model group, SeNP pretreatment significantly enhanced the antioxidant capacity, inhibited the overproduction of reactive oxygen species (ROS), effectively regulated the inflammatory response, decreased the activity of acetylcholinesterase, significantly reduced the expression level of caspase-1 and the ratio of Bcl-2/Bax, and upregulated the expression level of p53. Furthermore, compared with Aβ25–35 model group, SeNPs effectively promoted the phosphorylation of Akt and cAMP-response element-binding protein (CREB), and upregulated the expression level of brain-derived neurotrophic factor (BDNF). In addition, the Akt inhibitor (AKT inhibitor VIII, AKTi-1/2) could reverse the protective effects of SeNPs on PC12 cells. The Akt agonist (SC79) had a similar effect on PC12 cells as that of SeNPs. Overall, this study demonstrated that biogenic SeNPs can effectively alleviate the Aβ25–35-induced toxicity in PC12 cells via Akt/CREB/BDNF signaling pathway. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common cognitive disorder. In recent years, many computer-aided diagnosis techniques have been proposed for AD diagnosis and progression predictions. Among them, graph neural networks (GNNs) have received extensive attention owing to their ability to effectively fuse multimodal features and model the correlation between samples. However, many GNNs for node classification use an entire dataset to construct a large fixed-graph structure, which cannot be used for independent testing. To overcome this limitation while maintaining the advantages of the GNN, we propose an auto-metric GNN (AMGNN) model for AD diagnosis. First, a metric-based meta-learning strategy is introduced to realize inductive learning for independent testing through multiple node classification tasks. In the meta-tasks, the small graphs help make the model insensitive to the sample size, thus improving the performance under small sample size conditions. Furthermore, an AMGNN layer with a probability constraint is designed to realize node similarity metric learning and effectively fuse multimodal data. We verified the model on two tasks based on the TADPOLE dataset: early AD diagnosis and mild cognitive impairment (MCI) conversion prediction. Our model provides excellent performance on both tasks with accuracies of 94.44% and 87.50% and median accuracies of 94.19% and 86.25%, respectively. These results show that our model improves flexibility while ensuring a good classification performance, thus promoting the development of graph-based deep learning algorithms for disease diagnosis. [ABSTRACT FROM AUTHOR]