REPS1 as a Potential Biomarker in Alzheimer’s Disease and Vascular Dementia

Identification of Alzheimer's disease and vascular dementia based on a Deep Forest and near-infrared spectroscopy analysis method

Alzheimer's disease (AD) and vascular dementia (VaD) typically do not exhibit distinct differences in clinical manifestations and auxiliary examination results, which leads to a high misdiagnosis rate. However, significant differences in treatment approaches and prognosis between these two diseases underscore the critical need for an accurate diagnosis of AD and VaD. In this study, serum samples from 33 patients with AD patients, 37 patients with VaD, and 130 healthy individuals were collected, employing near-infrared aquaphotomics technology in combination with deep learning for differential diagnoses. Through an analysis of water absorption patterns among different diseases via aquaphotomics, the efficacies of traditional machine learning methods (Support Vector Machine and Decision Trees) and deep learning approaches (Deep Forest) in modeling were compared. Ultimately, by leveraging feature extraction techniques in conjunction with deep learning, a differential diagnostic model for AD and VaD was successfully developed. The results revealed that aquaphotomics could identify a certain correlation between the number of hydrogen bonds in water molecules and the development of AD and VaD; the deep learning model was found to be superior to traditional machine learning models, achieving an accuracy of 98.67 %, sensitivity of 97.33 %, and specificity of 100.00 %. The bands identified using the Competitive Adaptive Reweighting Algorithm method, primarily located at approximately 1300-1500 nm, showed a significant correlation with water molecules containing four hydrogen bonds. These results highlighted the potential role of the water molecule hydrogen-bond network in disease development and were consistent with the aquaphotomics analysis results. Therefore, the differential diagnostic model developed by integrating near-infrared spectroscopy and deep learning was proven to be effective and feasible, providing accurate and rapid diagnostic methods for AD and VaD diagnoses.

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

BACKGROUND

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

OBJECTIVE

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

METHODS

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

RESULTS

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

CONCLUSIONS

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Metagenomics reveals unique gut mycobiome biomarkers in psoriasis

PURPOSE

In present, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy and skin biopsy, and unlike other immune diseases, psoriasis lacks specific indicators in the blood. Therefore, we are required to search novel biomarkers for the diagnosis of psoriasis.

METHODS

In this study, we analyzed the composition and the differences of intestinal fungal communities composition between psoriasis patients and healthy individuals in order to find the intestinal fungal communities associated with the diagnosis of psoriasis. We built a machine learning model and identified potential microbial markers for the diagnosis of psoriasis.

RESULTS

The results of AUROC (area under ROC) showed that Aspergillus puulaauensis (AUROC = 0.779), Kazachstania africana (AUROC = 0.750) and Torulaspora delbrueckii (AUROC = 0.745) had high predictive ability (AUROC > 0.7) for predicting psoriasis, While Fusarium keratoplasticum (AUROC = 0.670) was relatively lower (AUROC < 0.7).

CONCLUSION

The strategy based on the prediction of intestinal fungal communities provides a new idea for the diagnosis of psoriasis and is expected to become an auxiliary diagnostic method for psoriasis.

Downregulation of miR-181c-5p in Alzheimer's disease weakens the response of microglia to Aβ phagocytosis

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aβ1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1β were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aβ1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aβ by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aβ1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aβ1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aβ by microglial cells in AD patients, thereby reducing neuroinflammation.

Bioinformatic Identification of Signaling Pathways and Hub Genes in Vascular Dementia

BACKGROUND

Vascular dementia (VaD) is a prevalent neurodegenerative disease characterized by cognitive impairment due to cerebrovascular factors, affecting a significant portion of the aging population and highlighting the critical need to understand specific targets and mechanisms for effective prevention and treatment strategies. We aimed to identify pathways and crucial genes involved in the progression of VaD through bioinformatics analysis and subsequently validate these findings.

METHODS

We conducted differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) analysis. We utilized pheochromocytoma 12 (PC12) cells to create an in vitro oxygen-glucose deprivation (OGD) model. We investigated the impact of overexpression and interference of adrenoceptor alpha 1D (ADRA1D) on OGD PC12 cells using TdT-mediated dUTP nick-end labeling (TUNEL), reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blot (WB), and Fluo-3-pentaacetoxymethyl ester (Fluo-3 AM) analysis.

RESULTS

We found 187 differentially expressed genes (DEGs) in the red module that were strongly associated with VaD and were primarily enriched in vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathway, and cell adhesion. Among these pathways, we identified ADRA1D as a gene shared by vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction. The TUNEL assay revealed a significant decrease in PC12 cell apoptosis with ADRA1D overexpression (p < 0.01) and a significant increase in apoptosis upon silencing ADRA1D (p < 0.01). RT-qPCR and WB analysis revealed elevated ADRA1D expression (p < 0.001) and decreased phospholipase C beta (PLCβ) and inositol 1,4,5-trisphosphate receptor (IP3R) expression (p < 0.05) with ADRA1D overexpression. Moreover, the Fluo-3 AM assessment indicated significantly lower intracellular Ca2+ levels with ADRA1D overexpression (p < 0.001). Conversely, interference with ADRA1D yielded opposite results.

CONCLUSION

Our study provides a new perspective on the pathogenic mechanisms of VaD and potential avenues for therapeutic intervention. The results highlight the role of ADRA1D in modulating cellular responses to OGD and VaD, suggesting its potential as a target for VaD treatment.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Transcriptome analysis reveals potential marker genes for diagnosis of Alzheimer's disease and vascular dementia

Alzheimer's disease (AD) and vascular dementia (VD) are the two most common forms of dementia, share similar symptoms, and are sometimes difficult to distinguish. To investigate the potential mechanisms by which they differ, we identified differentially expressed genes in blood and brain samples from patients with these diseases, and performed weighted gene co-expression network analysis and other bioinformatics analyses. Weighted gene co-expression network analysis resulted in mining of different modules based on differences in gene expression between these two diseases. Enrichment analysis and generation of a protein-protein interaction network were used to identify core pathways for each disease. Modules were significantly involved in cAMP and AMPK signaling pathway, which may be regulated cell death in AD and VD. Genes of cAMP and neurotrophin signaling pathways, including ATP1A3, PP2A, NCEH1, ITPR1, CAMKK2, and HDAC1, were identified as key markers. Using the least absolute shrinkage and selection operator method, a diagnostic model for AD and VD was generated and verified through analysis of gene expression in blood of patients. Furthermore, single sample gene set enrichment analysis was used to characterize immune cell infiltration into brain tissue. That results showed that infiltration of DCs and pDCs cells was increased, and infiltration of B cells and TFH cells was decreased in the brain tissues of patients with AD and VD. In summary, classification based on target genes showed good diagnostic efficiency, and filled the gap in the diagnostic field or optimizes the existing diagnostic model, which could be used to distinguish between AD and VD.

Potential biomarkers of Alzheimer’s disease and cerebral small vessel disease

Background

Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer’s disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment.

Methods

Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI.

Results

A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD.

Conclusion

The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.

Acupuncture Interventions for Alzheimer’s Disease and Vascular Cognitive Disorders: A Review of Mechanisms

Cognitive impairment (CI) related to Alzheimer's disease (AD) and vascular cognitive disorders (VCDs) has become a key problem worldwide. Importantly, CI is a neuropsychiatric abnormality mainly characterized by learning and memory impairments. The hippocampus is an important brain region controlling learning and memory. Recent studies have highlighted the effects of acupuncture on memory deficits in AD and VCDs. By reviewing the literature published on this topic in the past five years, the present study intends to summarize the effects of acupuncture on memory impairment in AD and VCDs. Focusing on hippocampal synaptic plasticity, we reviewed the mechanisms underlying the effects of acupuncture on memory impairments through regulation of synaptic proteins, AD characteristic proteins, intestinal microbiota, neuroinflammation, microRNA expression, orexin system, energy metabolism, etc., suggesting that hippocampal synaptic plasticity may be the common as well as the core link underlying the above mechanisms. We also discussed the potential strategies to improve the effect of acupuncture. Additionally, the effects of acupuncture on synaptic plasticity through the regulation of vascular–glia–neuron unit were further discussed.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.

METHODS

Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.

RESULTS

DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.

CONCLUSION

AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.