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In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the pivotal role of microglia in AD pathology, our analysis revealed 11 distinct microglial subclusters, with 4 exhibiting obviously alterations in AD and HC groups. The investigation of cell–cell communication networks unveiled intricate interactions between AD-related microglia and various cell types within the central nervous system (CNS). Integration of WGCNA and scRNA-seq facilitated the identification of critical genes associated with AD-related microglia, providing insights into their involvement in processes such as peptide chain elongation, synapse-related functions, and cell adhesion. The identification of 9 hub genes, including USP3, through the least absolute shrinkage and selection operator (LASSO) and COX regression analyses, presents potential therapeutic targets. Furthermore, the development of a CNN-based model showcases the application of deep learning in enhancing diagnostic accuracy for AD. Overall, our findings significantly contribute to unraveling the molecular intricacies of microglial responses in AD, offering promising avenues for targeted therapeutic interventions and improved diagnostic precision. [ABSTRACT FROM AUTHOR]

At present, social capital is considered to be one of the important reasons for promoting economic development and causing regional economic differences, but in the existing research, there is little literature on the impact of regional social capital on enterprises' green innovation behavior and green total factor productivity (GTFP), so this paper aims to enrich the research in this area. This paper builds a regional social capital evaluation index system and uses the super-SBM model to measure the enterprise GTFP. Then, this paper brings regional social capital, enterprise green innovation and GTFP into a unified framework for the first time and further reveals the quantitative relationship between the three by using OLS and Tobit two-step methods based on the panel data of 30 provinces from 2011 to 2019. The results show that regional social capital has a positive effect on enterprise GTFP and green innovation (except for strategic green innovation output), enterprise green innovation output has a positive role in promoting GTFP, and enterprise green innovation capital investment has a masking effect between regional social capital and GTFP. Furthermore, the expansive study finds that there are differences in the impact of regional social capital on green innovation and the GTFP of heterogeneous enterprises, and financing constraints have a positive regulatory effect on the relationship between regional social capital and the GTFP of state-owned enterprises, while having an inhibitory effect on the GTFP of private enterprises. Fiscal decentralization has a partial mediating effect between regional social capital and enterprise GTFP, while urbanization and CO2 emissions have a masking effect. Additionally, this paper aims to provide a reference for the improvement of regional social capital theory, the strategic choice of green innovation of enterprises, and the high-quality development of the economy. [ABSTRACT FROM AUTHOR]