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Alzheimer's disease (AD) is a common neurodegenerative disease characterized by amyloid-β (Aβ) deposition and neurofibrillary tangles formed by high phosphorylation of tau protein. At present, drug therapy is the main strategy of AD treatment, but its effects are limited to delaying or alleviating AD. Recently, non-pharmacologic intervention has attracted more attention, and more studies have confirmed that non-pharmacologic intervention in AD can improve the patient's cognitive function and quality of life. This paper summarizes the current non-pharmacologic intervention in AD, to provide useful supplementary means for AD intervention. [ABSTRACT FROM AUTHOR]

Background: COVID-19 pandemic is a global crisis which results in millions of deaths and causes long-term neurological sequelae, such as Alzheimer's disease (AD).Objective: We aimed to explore the interaction between COVID-19 and AD by integrating bioinformatics to find the biomarkers which lead to AD occurrence and development with COVID-19 and provide early intervention.Methods: The differential expressed genes (DEGs) were found by GSE147507 and GSE132903, respectively. The common genes between COVID-19 and AD were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPI) network analysis were carried out. Hub genes were found by cytoscape. A multivariate logistic regression model was constructed. NetworkAnalyst was used for the analysis of TF-gene interactions, TF-miRNA coregulatory network, and Protein-chemical Interactions.Results: Forty common DEGs for AD and COVID-19 were found. GO and KEGG analysis indicated that the DEGs were enriched in the calcium signal pathway and other pathways. A PPI network was constructed, and 5 hub genes were identified (ITPR1, ITPR3, ITPKB, RAPGEF3, MFGE8). Four hub genes (ITPR1, ITPR3, ITPKB, RAPGEF3) which were considered as important factors in the development of AD that were affected by COVID-19 were shown by nomogram. Utilizing NetworkAnalyst, the interaction network of 4 hub genes and TF, miRNA, common AD risk genes, and known compounds is displayed, respectively.Conclusion: COVID-19 patients are at high risk of developing AD. Vaccination is required. Four hub genes can be considered as biomarkers for prediction and treatment of AD development caused by COVID-19. Compounds with neuroprotective effects can be used as adjuvant therapy for COVID-19 patients. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is one of many common neurodegenerative diseases without ideal treatment, but early detection and intervention can prevent the disease progression.Objective: This study aimed to identify AD-related glycolysis gene for AD diagnosis and further investigation by integrated bioinformatics analysis.Methods: 122 subjects were recruited from the affiliated hospitals of Ningbo University between 1 October 2015 and 31 December 2016. Their clinical information and methylation levels of 8 glycolysis genes were assessed. Machine learning algorithms were used to establish an AD prediction model. Receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the model. An AD risk factor model was developed by SHapley Additive exPlanations (SHAP) to extract features that had important impacts on AD. Finally, gene expression of AD-related glycolysis genes were validated by AlzData.Results: An AD prediction model was developed using random forest algorithm with the best average ROC_AUC (0.969544). The threshold probability of the model was positive in the range of 0∼0.9875 by DCA. Eight glycolysis genes (GAPDHS, PKLR, PFKFB3, LDHC, DLD, ALDOC, LDHB, HK3) were identified by SHAP. Five of these genes (PFKFB3, DLD, ALDOC, LDHB, LDHC) have significant differences in gene expression between AD and control groups by Alzdata, while three of the genes (HK3, ALDOC, PKLR) are related to the pathogenesis of AD. GAPDHS is involved in the regulatory network of AD risk genes.Conclusion: We identified 8 AD-related glycolysis genes (GAPDHS, PFKFB3, LDHC, HK3, ALDOC, LDHB, PKLR, DLD) as promising candidate biomarkers for early diagnosis of AD by integrated bioinformatics analysis. Machine learning has the advantage in identifying genes. [ABSTRACT FROM AUTHOR]

Highlights: [•] CCK-8S increased the density of filopodia and spines in wild type neurons. [•] CCK-8S increased the density of filopodia and spines in APP/PS1 neurons. [•] CCK-8S could promote the spine genesis both in neuron's early and mature stage. [•] CI988 could block the effect of CCK-8S on spine genesis. [ABSTRACT FROM AUTHOR]