Current Alzheimer research最新文献

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, significantly impacting the quality of life for affected individuals. This manuscript explores various innovative therapeutic strategies aimed at enhancing drug delivery to the brain, particularly through the use of nanotechnology. This paper discussed the application of Solid Lipid Nanoparticles (SLNs), dendrimers, and Polymeric Nanoparticles (PNPs) in targeting the Central Nervous System (CNS) to improve bioavailability and therapeutic efficacy. The findings indicate that these advanced delivery systems can enhance brain penetration, reduce Amyloid-Beta (Aβ) deposition, and improve cognitive functions in animal models of AD. Furthermore, the review highlights the challenges associated with these technologies, including limited scalability and potential toxicity, while suggesting future directions for research and development in the field of AD treatment.

Aims: This study aims, to trace the history of age-associated dementia from the earliest historical periods to the beginning of the modern age.

Background: Since the medical literature prior to the early 19th century is relatively scarce, the near absence of senile dementia has been hypothesized.

Objective: Verify the prevalence of senile dementia across different historical periods.

Methods: Beyond the medical literature, reviewed papers addressing legal and social aspects were examined to provide a comprehensive overview of the subject.

Results: While the medical literature on the subject is limited, there are a greater abundance of sources discussing social and legislative aspects. The scientific study of dementia had began only in the early 1800s.

Conclusion: In ancient times, dementia was not particularly rare, but it was often overlooked, as it was considered an inevitable consequence of aging.

Introduction: Alzheimer's disease (AD) is the most common form of dementia, and it is important to diagnose the disease at an early stage to help people with the condition and their families. Recently, artificial intelligence, especially deep learning approaches applied to medical imaging, has shown potential in enhancing AD diagnosis. This comprehensive review investigates the current state of the art in multimodal deep learning for the early diagnosis of Alzheimer's disease using image processing.

Methods: The research underpinning this review spanned several months. Numerous deep learning architectures are examined, including CNNs, transfer learning methods, and combined models that use different imaging modalities, such as structural MRI, functional MRI, and amyloid PET. The latest work on explainable AI (XAI) is also reviewed to improve the understandability of the models and identify the particular regions of the brain related to AD pathology.

Results: The results indicate that multimodal approaches generally outperform single-modality methods, and three-dimensional (volumetric) data provides a better form of representation compared to two-dimensional images.

Discussion: Current challenges are also discussed, including insufficient and/or poorly prepared datasets, computational expense, and the lack of integration with clinical practice. The findings highlight the potential of applying deep learning approaches for early AD diagnosis and for directing future research pathways.

Conclusion: The integration of multimodal imaging with deep learning techniques presents an exciting direction for developing improved AD diagnostic tools. However, significant challenges remain in achieving accurate, reliable, and understandable clinical applications.

Objectives: Dementia has become a major global cause of death, posing significant health and economic challenges. Alzheimer's disease (AD) is the most common type of dementia. Recent studies have shown that long noncoding RNAs (lncRNAs) play a role in AD development. In this context, the current study conducted a comprehensive meta-analysis of high-throughput Gene Expression Omnibus (GEO) datasets to identify significant lncRNAs that could play a crucial role in the pathogenesis of AD.

Methods: Three microarray expression profiles of human subjects diagnosed with AD and corresponding healthy controls were obtained from the GEO database. Afterward, the expression profiles from the chosen microarray datasets were combined. A network of differentially expressed genes (DEGs) was visualized, identifying key hub genes. Subsequently, the two significant lncRNAs, identified as LINC01003 and CHASERR, were chosen based on the number of interactions between hubs and lncRNAs. Blood samples were collected from AD patients as well as from healthy control individuals. Ultimately, the expression levels of CHASERR and LINC01003 were quantitatively assessed in the blood samples of 50 AD patients and 50 healthy controls using the quantitative Real-Time PCR (q-PCR) technique.

Results: Experimental validation showed that CHASERR was differentially expressed in Alzheimer's disease (AD) patients compared to the control group. In contrast, LINC01003 revealed no significant difference between the AD patients and the control group.

Conclusion: This study thoroughly examined the molecular landscape of AD, identifying key differentially expressed genes and highlighting candidate CHASERR as a potential molecular biomarker for AD patients.

Introduction: Alzheimer's disease (AD) represents the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular mechanisms remain largely undefined.

Methods: This study aimed to identify novel biomarkers and therapeutic strategies associated with mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction- related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function, such as the citrate cycle and oxidative phosphorylation.

Results: Employing machine learning techniques, including random forest and LASSO, along with the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2, CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target MRGs.

Conclusion: These findings delineate the intricate role of mitochondrial dysfunction in AD and highlight promising avenues for further exploration of biomarkers and therapeutic interventions in this devastating disease.

Alzheimer's disease (AD) is a degenerative neurological disease characterized by a loss of memory and cognitive ability. One of the main factors influencing the development of AD is the accumulation of amyloid β (Aβ) plaque in the brain. The sequential production of Aβ is mediated by two enzymes: gamma-secretase and β-secretase (BACE1). The goal of beta-secretase inhibitors is to prevent the initial cleavage of amyloid precursor protein (APP), which reduces the production of (Aβ) peptides by limiting the substrate available for gamma-secretase. Simultaneously, gamma-secretase modulators are engineered to specifically modify enzyme performance, reducing the synthesis of the harmful Aβ42 isoform while maintaining vital physiological processes. Targeting both secretases reduces amyloidogenic processing synergistically. Selective inhibitors, which have been recently developed, have also shown good clinical development. They can reduce Aβ levels effectively with minimal side effects. The therapeutic strategy also underlines the importance of early therapy intervention in the preclinical AD phase for an optimum effect. Although there are some problems in the optimization of drug delivery and the alleviation of side effects, targeting beta and gamma secretases remains a promising direction. However, all these strategies still need more research and clinical testing to improve existing treatments and develop new, efficient Alzheimer's disease therapies. This review seeks to examine the therapeutic promise of β- and γ-secretase inhibition in Alzheimer's disease and review recent progress, challenges, and new dual-inhibition approaches.

Alzheimer's disease (AD) is a complex neurodegenerative disorder and a growing global health challenge, driven by increasing life expectancy and an aging population. This review provides a comprehensive exploration of AD pathophysiology, integrating current hypotheses such as the amyloid cascade, tau protein pathology, cholinergic dysfunction, neuroinflammation, vascular contributions, and potential infection-related mechanisms. The multifactorial etiology of AD, encompassing genetic predispositions and environmental factors, underscores its intricate nature. This study delves into the diagnostic advancements, including the identification and utilization of biomarkers for early detection and disease monitoring. Therapeutic approaches are critically evaluated, highlighting anti-amyloid and anti-tau strategies, alongside emerging innovations in stem cell therapy and nanobiotechnology. A detailed examination of clinical trials offers insights into the achievements and setbacks of translating research into effective treatments. By synthesizing epidemiological trends, molecular mechanisms, and therapeutic developments, this review aims to advance our understanding of AD and foster collaborative efforts to develop transformative solutions. It emphasizes the urgency of addressing this multifaceted disease, presenting a nuanced perspective on its complexity while illuminating future directions for research and clinical practice.

Alzheimer's disease (AD) is an age-related, progressive neurodegenerative disorder of cognition with clinical features and anatomical hallmarks of amyloid-β plaques and/or neurofibrillary tangles. New studies revealed that microglia, the native immune cells in the brain, are crucial in the development of AD. The present review aims at outlining various roles of microglia in AD especially targeting their role in neuroinflammation. These indicate that microglial dysfunction contributes to AD pathology by affecting both amyloid-β phagocytosis and tau hyperphosphorylation. Other investigative molecular perpetrators, including TREM2, also influence the microglial relevance to amyloid and tau, as well as the overall disease phase. The functional microglia can protect neurons, while the dysfunctional one has the capability of derailing neuronal potentials and aggravating neurodegeneration. We have also discussed therapeutic strategies that start with targeting microglia to reduce neuroinflammation and reinstate balance. However, certain problems, including the side effects of microglial modulation, cost constraint, and accessibility, are areas of concern. In this review, the author presents the current state of knowledge on the potential of microglia-targeted treatments, their risks, and benefits. Thus, this article emphasizes the importance of the expansion of research to decipher the exact manipulation of microglia in AD with the goal of applying these findings given therapeutic approaches.

Introduction: Nowadays, the large increase in environmental pollutants has led to the occurrence and development of an increasing number of diseases. Studies have shown that exposure to environmental pollutants, such as methyl-4-hydroxybenzoate (MEP) may lead to Alzheimer's disease (AD). Therefore, the purpose of this study was to elucidate the complex effects and potential molecular mechanisms of environmental pollutants MEP on AD.

Methods: Through exhaustive exploration of databases, such as ChEMBL, STITCH, SwissTarget- Prediction, and Gene Expression Omnibus DataSets (GEO DataSets), we have identified a comprehensive list of 46 potential targets closely related to MEP and AD. After rigorous screening using the STRING platform and Cytoscape software, we narrowed the list to nine candidate targets and ultimately identified six hub targets using three proven machine learning methods (LASSO, RF, and SVM): CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5. The "clusterProfiler" R package was used to conduct GO and KEGG enrichment analysis. At the same time, we also constructed disease prediction models for core genes. At last, six hub targets were executed molecular docking.

Results: We derived 46 key target genes related to MEP and AD and conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. MEP might play a role in AD by affecting the pathways of neuroactive ligand-receptor interaction. Nine genes were screened as pivotal targets, followed by machine learning methods to identify six hub targets. Molecular docking analysis showed a good binding ability between MEP and CREBBP, BCL6, CXCR4, GRIN1, GOT2 and ITGA5. In addition, changes in the immune microenvironment revealed a significant impact of immune status on AD.

Discussions: This study revealed that MEP may induce AD through multiple mechanisms, such as oxidative stress, neurotoxicity, and immune regulation, and identified six core targets (CREBBP, BCL6, etc.) and found that they are related to changes in the immune microenvironment, such as T cells and B cells, providing new molecular targets for AD intervention.

Conclusion: Overall, CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5 have been identified as the crucial targets correlating with AD. Our findings provide a theoretical framework for understanding the complex molecular mechanisms underlying the effects of MEP on AD and provide insights for the development of prevention and treatment of AD caused by exposure to MEP.