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Alzheimer’s disease (AD) is a neurodegenerative disorder that severely impacts cognitive function, posing significant physical and psychological burdens on patients and substantial economic challenges to families and society, particularly in aging populations where its prevalence is rising. Current diagnostic and therapeutic strategies, including pharmacological treatments and non-pharmacological interventions, exhibit considerable limitations in early diagnosis, etiological treatment, and disease management. This study aims to investigate the application of artificial intelligence (AI) in the early diagnosis and progression monitoring of AD through a bibliometric analysis of relevant literature. A systematic search in the Web of Science Core Collection identified 530 publications related to AI and AD, consisting of 361 original research articles and 169 review articles, with a notable increase in annual publication rates, particularly between 2019 and 2024. The United States and China emerged as leading contributors, emphasizing the importance of international collaboration. Institutional analysis revealed that Harvard University and Indiana University System are at the forefront, highlighting the role of academic institutions in fostering interdisciplinary research. Furthermore, the Journal of Alzheimer’s Disease was identified as the most influential publication outlet. Key highly cited papers provided essential theoretical foundations for ongoing research. This study underscores the growing relevance of AI in AD research and suggests promising avenues for future investigations, particularly in enhancing diagnostic accuracy and therapeutic strategies through advanced data analytics and machine learning techniques.

BackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder marked by cognitive decline and memory loss. Recent research underscores the crucial role of astrocytes in AD. This study reviews research trends and contributions on astrocytes in AD from 2000 to 2024, shedding light on the evolving research landscape.MethodsWe conducted a bibliometric analysis using data from the Web of Science Core Collection, covering publications from January 1, 2000, to July 6, 2024, on “Alzheimer’s disease” and “astrocytes.” We identified 5,252 relevant English articles and reviews. For data visualization and analysis, we used VOSviewer, CiteSpace, and the R package “bibliometrix,” examining collaboration networks, co-citation networks, keyword co-occurrence, and thematic evolution.ResultsBetween 2000 and 2024, 5,252 publications were identified, including 4,125 original research articles and 1,127 review articles. Publications increased significantly after 2016. The United States had the most contributions (1,468), followed by China (836). Major institutions were the University of California system (517) and Harvard University (402). The Journal of Alzheimer’s Disease published the most articles (215). Verkhratsky A was the top author with 51 papers and 1,585 co-citations.ConclusionOur extensive bibliometric analysis indicates a significant increase in research on astrocytes in AD over the past 20 years. This study emphasizes the growing acknowledgment of astrocytes’ crucial role in AD pathogenesis and points to future research on their mechanisms and therapeutic potential.

With the extensive use of antibiotics, antibiotics and their induced resistance genes (ARGs) have become new types of pollutants widely distributed in a variety of environmental media. The contamination of antibiotics and ARGs occurring in important living and agricultural regions has aroused wide concern worldwide, especially in lake basins. The Dongting Lake basin is one of the important aquaculture and livestock areas in China, which is accompanied by a large amount of antibiotic use and discharge. However, the occurrence characteristics of antibiotics and ARGs in a multi-environment medium are still unclear. In this study, surface water and sediment samples in East Dongting Lake were collected by season, antibiotics and ARGs were quantitatively analyzed, and the risk quotient method was used to evaluate the ecological risk of antibiotics in surface water. 1) The concentration of antibiotics in the surface water of East Dongting Lake ranged from ND to 943.49 ng/L, with the maximum average concentration of 20.92 ng/L in spring. The concentration of antibiotics in sediments ranged from ND to 177.43 ng/g, with the maximum average concentration of 16.38 ng/g in fall. Ofloxacin (OFL) and sulfamethoxazole (SMX) were the main antibiotic pollutants in East Dongting Lake Basin. 2) sul1 and sul2 were the dominant ARGs in East Dongting Lake Basin. For spatial change, the total abundance of ARGs upstream was higher than that downstream. For seasonal change, the surface water and sediment were characterized by spring > summer > fall. 3) OFL and sulfamethoxazole might pose a significant high risk to aquatic organisms both in three seasons, and the ecological risk of antibiotics in East Dongting Lake is more significant at low temperatures than high. This study could provide important data information of the occurrence and concentration of antibiotics and ARGs in East Dongting Lake Basin.

Background Accurate diagnosis of major depressive disorder (MDD) remains difficult, and one of the key challenges in diagnosing MDD is the lack of reliable diagnostic biomarkers. The objective of this study was to explore gene networks and identify potential biomarkers for MDD. Methods In the present study, we performed a comprehensive analysis of the mRNA expression profiles using blood samples of four patients with MDD and four controls by RNA sequencing. Differentially expressed genes (DEGs) were screened, and functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. All DEGs were inputted to the STRING database to build a PPI network, and the top 10 hub genes were screened using the cytoHubba plugin of the Cytoscape software. The relative expression of 10 key genes was identified by quantitative real-time polymerase chain reaction (qRT-PCR) of blood samples from 50 MDD patients and 50 controls. Plasma levels of SQSTM1 and TNFα were measured using an enzyme-linked immunosorbent assay in blood samples of 44 MDD patients and 44 controls. A sucrose preference test was used to evaluate depression-like behavior in chronic unpredictable mild stress (CUMS) model rats. Immunofluorescence assay and western blotting were performed to study the expression of proteins in the brain samples of CUMS model rats Results We identified 247 DEGs that were closely associated with MDD. Gene ontology analyses suggested that the DEGs were mainly enriched in negative regulation of transcription by RNA polymerase II promoter, cytoplasm, and protein binding. Moreover, Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the DEGs were significantly enriched in the MAPK signaling pathway. Ten hub genes were screened through the PPI network, and qRT-PCR assay revealed that one and six genes were downregulated and upregulated, respectively; however, SMARCA2, PPP3CB, and RAB5C were not detected. Pathway enrichment analysis for the 10 genes showed that the mTOR signaling pathway was also enriched. A strong positive correlation was observed between SQSTM1 and TNFα protein levels in patients with MDD. LC3 II and SQSTM1 protein levels were increased in the CUMS rat model; however, p-mTOR protein levels were decreased. The sucrose preference values decreased in the CUMS rat model. Conclusions We identified 247 DEGs and constructed an MDD-specific network; thereafter, 10 hub genes were selected for further analysis. Our results provide novel insights into the pathogenesis of MDD. Moreover, SQSTM1, which is related to autophagy and inflammatory reactions, may play a key role in MDD. SQSTM1 may be used as a promising therapeutic target in MDD; additionally, more molecular mechanisms have been suggested that should be focused on in future in vivo and in vitro studies.