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Microglia, as innate immune cells in the brain, closely monitor changes in the internal environment and participate in the maintenance of homeostasis in the central nervous system (CNS). Microglia can be polarized to the M1 or M2 phenotype in response to various stimuli in vivo or in vitro, affecting the functions of peripheral neurons. M2 microglia have attracted increasing attention in recent years owing to their beneficial effects on various diseases and injuries of the CNS, such as traumatic brain injury, stroke, Alzheimer's disease and multiple sclerosis. They exert neuroprotective effects by various mechanisms, e.g., suppressing inflammation, promoting the degradation of misfolded and aggregated proteins, promoting neurite growth, enhancing neurogenesis, inhibiting autophagy and apoptosis, promoting myelination, maintaining blood–brain barrier integrity, and enhancing phagocytic activity.This review summarizes the molecular mechanisms by which M2 microglia exert protective effects on neurons and provides a reference for the selection of therapeutic targets for CNS diseases. [ABSTRACT FROM AUTHOR]

Exploring new therapeutic diseases for existing drugs plays an essential role in reducing drug development costs. However, existing methods for predicting drug–disease association (DDA) lack fusion to multi-neighborhood information, which limits their ability to generalize and forces them to rely on prior knowledge. To this end, we propose a novel DDA model called the Neighborhood Contrastive Learning Heterogeneous Networks (NCH-DDA). NCH-DDA uses both single-neighborhood and multi-neighborhood feature extraction modules to extract important features of drugs and diseases in parallel from multiple potential spaces, such as heterogeneous networks and similarity networks. NCH-DDA fuses single-neighborhood and multi-neighborhood features using contrastive learning to enhance information interaction in different neighborhood spaces, ultimately obtaining universal domain features of drugs and diseases. NCH-DDA uses a combination of predictive loss and triplet loss to reduce dependence on prior knowledge. In different partition schemes of multiple datasets, NCH-DDA achieved the best performance in predicting DDA, outperforming several current state-of-the-art methods. Moreover, NCH-DDA demonstrated better performance in experiments on data sparsity and drug repositioning for Alzheimer's disease, indicating its greater potential in DDA prediction with sparse omics data and drug repositioning applications. • Neighborhood contrastive model for heterogeneous representation and DDA prediction. • MNFEM and SNFEM capture the important features in multiple potential spaces. • Contrast learning aggregated generic neighborhood features of drugs and diseases. • Presented a mixing optimization strategy to reduce the dependence on prior knowledge. • Four of the top five drugs recommended for AD by NCH-DDA were proved to be effective. [ABSTRACT FROM AUTHOR]