scholarly journals Sirtuin 3 mRNA Expression is Downregulated in the Brain Tissues of Alzheimer’s Disease Patients: A Bioinformatic and Data Mining Approach

2020 ◽  
Vol 26 ◽  
Author(s):  
Shuang Song ◽  
Bin Li ◽  
Zhen Jia ◽  
Li Guo
Keyword(s):  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
Mrna Expression ◽  
Sirtuin 3 ◽  
Data Mining Approach ◽  
The Brain ◽  
Brain Tissues

A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

The Analyst ◽  
2019 ◽  
Vol 144 (23) ◽  
pp. 7049-7056 ◽  
Author(s):  
Emerson A. Fonseca ◽  
Lucas Lafetá ◽  
Renan Cunha ◽  
Hudson Miranda ◽  
João Campos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Model ◽  
Amyloid Plaques ◽  
Model Of Alzheimer’S Disease ◽  
The Brain ◽  
Brain Tissues

We have found different Raman signatures of AB fibrils and in brain tissues from unmixed analysis, providing a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers.

MR Brain Screening using Optimization Techniques – A survey

2021 ◽  
Vol 17 ◽  
Author(s):  
Chitradevi D ◽  
Prabha S.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Symptoms ◽  
Memory Loss ◽  
Amyloid Β ◽  
Cell Loss ◽  
Optimization Techniques ◽  
Amyloid Β Protein ◽  
The Brain ◽  
Brain Tissues

Background: Alzheimer’s disease (AD) is associated with Dementia, and it is also a memory syndrome in the brain. It affects the brain tissues and causes major changes in day-to-day activities. Aging is a major cause of Alzheimer's disease. AD is characterized by two pathological hallmarks as, Amyloid β protein and neurofibrillary tangles of hyperphosphorylated tau protein. The imaging hallmarks for Alzheimer’s disease are namely, swelling, shrinkage of brain tissues due to cell loss, and atrophy in the brain due to protein dissemination. Based on the survey, 60% to 80% of dementia patients belong to Alzheimer’s disease. Introduction: AD is now becoming an increasing and important brain disease. The goal of AD pathology is to cause changes/damage in brain tissues. Alzheimer's disease is thought to begin 20 years or more before symptoms appear, with tiny changes in the brain that are undetectable to the person affected. The changes in a person's brain after a few years are noticeable through symptoms such as language difficulties and memory loss. Neurons in different parts of the brain have detected symptoms such as cognitive impairments and learning disabilities. In this case, neuroimaging tools are necessary to identify the development of pathology which relates to the clinical symptoms. Methods: Several approaches have been tried during the last two decades for brain screening to analyse AD with the process of pre-processing, segmentation and classification. Different individual such as Grey Wolf optimization, Lion Optimization, Ant Lion Optimization and so on. Similarly, hybrid optimization techniques are also attempted to segment the brain sub-regions which helps in identifying the bio-markers to analyse AD. Conclusion: This study discusses a review of neuroimaging technologies for diagnosing Alzheimer's disease, as well as the discovery of hallmarks for the disease and the methodologies for finding hallmarks from brain images to evaluate AD. According to the literature review, most of the techniques predicted higher accuracy (more than 90%), which is beneficial for assessing and screening neurodegenerative illness, particularly Alzheimer's disease.

scholarly journals A detailed investigation in determining Alzheimer’s disease and its risk factor using different classification techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 374-377
Author(s):  
Mahendran Radha ◽  
Anitha M ◽  
Jeyabaskar Suganya
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
Genetic Disorders ◽  
Learning Approaches ◽  
Psychiatric Illnesses ◽  
Data Mining Approach ◽  
Diseased Person ◽  
Linear Svm

The prevalence of genetic disorders has recently crept surprisingly high. Neurodegenerative complications, specifically, pose physical and mental stress to parents and caretakers. These complications may be witnessed in the case of dementia. The general dementia type that accounted for between 60 to 80 per cent of psychiatric illnesses was Alzheimer's disease. At an earlier stage, illness detection serves as a critical task that helps the diseased person to enjoy a decent quality of life. It has become a much necessitated strategy towards relying on automated techniques like data mining approach for early diagnosis and assessment of risk factors concerned with Alzheimer’s. There has been an unprecedented growth of interest concerned with devising novelized approaches proposed in recent times for classifying the disease. However, there is still a grave need for developing an efficacious approach for better prognosis and classification. Data mining is carried out using different machine-learning approaches to assess the risk factors for Alzheimer's disease. Through the present research, and we compared numerous classification methods such as Decision Tree, Linear SVM, KNN, Logistic Regression, Radial SVM, and Random Forest, and finally reported the most outstanding approach in terms of its accuracy.

scholarly journals Metformin Ameliorates Aβ Pathology by Insulin-Degrading Enzyme in a Transgenic Mouse Model of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xin-Yi Lu ◽  
Shun Huang ◽  
Qu-Bo Chen ◽  
Dapeng Zhang ◽  
Wanyan Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transgenic Mice ◽  
Mrna Expression ◽  
Insulin Degrading Enzyme ◽  
Antidiabetic Drug ◽  
Degrading Enzyme ◽  
Expression Levels ◽  
The Brain ◽  
Mrna Expression Levels

Alzheimer’s disease (AD) is the most common neurodegenerative disease. The accumulation of amyloid beta (Aβ) is the main pathology of AD. Metformin, a well-known antidiabetic drug, has been reported to have AD-protective effect. However, the mechanism is still unclear. In this study, we tried to figure out whether metformin could activate insulin-degrading enzyme (IDE) to ameliorate Aβ-induced pathology. Morris water maze and Y-maze results indicated that metformin could improve the learning and memory ability in APPswe/PS1dE9 (APP/PS1) transgenic mice. 18F-FDG PET-CT result showed that metformin could ameliorate the neural dysfunction in APP/PS1 transgenic mice. PCR analysis showed that metformin could effectively improve the mRNA expression level of nerve and synapse-related genes (Syp, Ngf, and Bdnf) in the brain. Metformin decreased oxidative stress (malondialdehyde and superoxide dismutase) and neuroinflammation (IL-1β and IL-6) in APP/PS1 mice. In addition, metformin obviously reduced the Aβ level in the brain of APP/PS1 mice. Metformin did not affect the enzyme activities and mRNA expression levels of Aβ-related secretases (ADAM10, BACE1, and PS1). Meanwhile, metformin also did not affect the mRNA expression levels of Aβ-related transporters (LRP1 and RAGE). Metformin increased the protein levels of p-AMPK and IDE in the brain of APP/PS1 mice, which might be the key mechanism of metformin on AD. In conclusion, the well-known antidiabetic drug, metformin, could be a promising drug for AD treatment.

scholarly journals Differential Expression of mRNAs in the Brain Tissues of Patients with Alzheimer’s Disease Based on GEO Expression Profile and Its Clinical Significance

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Guowei Ma ◽  
Mingyan Liu ◽  
Ke Du ◽  
Xin Zhong ◽  
Shiqiang Gong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Expression ◽  
Brain Tissue ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Venn Diagram ◽  
Ad Prevention ◽  
The Brain ◽  
Brain Tissues

Background. Early diagnosis of Alzheimer’s disease (AD) is an urgent point for AD prevention and treatment. The biomarkers of AD still remain indefinite. Based on the bioinformatics analysis of mRNA differential expressions in the brain tissues and the peripheral blood samples of Alzheimer’s disease (AD) patients, we investigated the target mRNAs that could be used as an AD biomarker and developed a new effective, practical clinical examination program. Methods. We compared the AD peripheral blood mononuclear cells (PBMCs) expression dataset (GEO accession GSE4226 and GSE18309) with AD brain tissue expression datasets (GEO accessions GSE1297 and GSE5281) from GEO in the present study. The GEO gene database was used to download the appropriate gene expression profiles to analyze the differential mRNA expressions between brain tissue and blood of AD patients and normal elderly. The Venn diagram was used to screen out the differential expression of mRNAs between the brain tissue and blood. The protein-protein interaction network map (PPI) was used to view the correlation between the possible genes. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the major affected genes and the function or pathway. Results. Bioinformatics analysis revealed that there were differentially expressed genes in peripheral blood and hippocampus of AD patients. There were 4958 differential mRNAs in GSE18309, 577 differential mRNAs in GSE4226 in AD PBMCs sample, 7464 differential mRNAs in GSE5281, and 317 differential mRNAs in GSE129 in AD brain tissues, when comparing between AD patients and healthy elderly. Two mRNAs of RAB7A and ITGB1 coexpressed in hippocampus and peripheral blood were screened. Furthermore, functions of differential genes were enriched by the PPI network map, GO, and KEGG analysis, and finally the chemotaxis, adhesion, and inflammatory reactions were found out, respectively. Conclusions. ITGB1 and RAB7A mRNA expressions were both changed in hippocampus and PBMCs, highly suggested being used as an AD biomarker with AD. Also, according to the results of this analysis, it is indicated that we can test the blood routine of the elderly for 2-3 years at a frequency of 6 months or one year. When a patient continuously detects the inflammatory manifestations, it is indicated as a potentially high-risk AD patient for AD prevention.

Analysis of HSPA8 and HSPA9 mRNA Expression and Promoter Methylation in the Brain and Blood of Alzheimer's Disease Patients

2013 ◽  
Vol 38 (1) ◽  
pp. 165-170 ◽  
Author(s):  
Patricia Natalia Silva ◽  
Tatiane Katsue Furuya ◽  
Ianna Lacerda Braga ◽  
Lucas Trevizani Rasmussen ◽  
Roger Willian Labio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mrna Expression ◽  
Promoter Methylation ◽  
The Brain

scholarly journals Defect of branched-chain amino acid metabolism promotes the development of Alzheimer’s disease by targeting the mTOR signaling

2018 ◽  
Vol 38 (4) ◽  
Author(s):  
Huajie Li ◽  
Dan Ye ◽  
Wei Xie ◽  
Fei Hua ◽  
Yilin Yang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amino Acid ◽  
Amyloid Β ◽  
Mtor Signaling ◽  
Branched Chain Amino Acid ◽  
Metabolic Defect ◽  
Branched Chain ◽  
The Brain ◽  
Brain Tissues

Diabetes is a risk factor for Alzheimer’s disease (AD) in humans. Branched-chain amino acids (BCAAs, namely valine, leucine, and isoleucine) metabolic defect is observed in human diabetes, which is associated with insulin resistance. But whether BCAAs connect diabetes and AD remains unknown. Here, we show that BCAA metabolic defect may be one of the drivers of AD. BCAA levels were increased in the blood in human patients and mice with diabetes or AD. BCAA-enriched diet promoted the development of AD in mice as evidenced by the behavior and pathological analysis. Branched-chain amino acid transaminase 1 and 2 (BCAT1 and BCAT2) are the two enzymes for the first step metabolism of BCAAs by catalyzing BCAAs to generate branched-chain ketoacids. The expression of Bcat1 but not Bcat2 was significantly down-regulated in the brain tissues of diabetic, aged, and AD mice. Leucine up-regulated the phosphorylation of Tau but not affected the accumulation of amyloid β in the brain tissues or isolated neurons. In addition, knockdown of the expression of Bcat1, which would result in the accumulation of BCAAs, led to the same phenotype as BCAAs supplement in neurons. Interestingly, leucine supplement or Bcat1 knockdown promoted the activation of the mTOR signaling in the brains of AD mice or neurons. Subsequently, mTOR was critically involved in leucine and Bcat1 knockdown-mediated phosphorylation of Tau. Taken together, our findings demonstrated that diabetes-related BCAA accumulation in the brain tissues led to the phosphorylation of Tau and, subsequently, the development of diabetes-related AD.

scholarly journals Integrated Proteomics Reveals Brain-Based Cerebrospinal Fluid Biomarkers in Asymptomatic and Symptomatic Alzheimer’s Disease

2019 ◽  
Author(s):  
Lenora Higginbotham ◽  
Lingyan Ping ◽  
Eric B. Dammer ◽  
Duc M. Duong ◽  
Maotian Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Postmortem Brain ◽  
Csf Biomarkers ◽  
Amyloid Accumulation ◽  
Cerebrospinal Fluid Biomarkers ◽  
Novel Biomarkers ◽  
The Brain ◽  
Brain Tissues

AbstractAlzheimer’s disease (AD) features a complex web of pathological processes beyond amyloid accumulation and tau-mediated neuronal death. To meaningfully advance AD therapeutics, there is an urgent need for novel biomarkers that comprehensively reflect these disease mechanisms. Here we applied an integrative proteomics approach to identify cerebrospinal fluid (CSF) biomarkers linked to a diverse set of pathophysiological processes in the diseased brain. Using multiplex proteomics, we identified >3,500 proteins across 40 CSF samples from control and AD patients and >12,000 proteins across 48 postmortem brain tissues from control, asymptomatic AD (AsymAD), AD, and other neurodegenerative cases. Co-expression network analysis of the brain tissues resolved 44 protein modules, nearly half of which significantly correlated with AD neuropathology. Fifteen modules robustly overlapped with proteins quantified in the CSF, including 271 CSF markers highly altered in AD. These 15 overlapping modules were collapsed into five panels of brain-linked fluid markers representing a variety of cortical functions. Neuron-enriched synaptic and metabolic panels demonstrated decreased levels in the AD brain but increased levels in diseased CSF. Conversely, glial-enriched myelination and immunity panels were highly increased in both the brain and CSF. Using high-throughput proteomic analysis, proteins from these panels were validated in an independent CSF cohort of control, AsymAD, and AD samples. Remarkably, several validated markers were significantly altered in AsymAD CSF and appeared to stratify subpopulations within this cohort. Overall, these brain-linked CSF biomarker panels represent a promising step toward a physiologically comprehensive tool that could meaningfully enhance the prognostic and therapeutic management of AD.

Sign in / Sign up

Export Citation Format

Share Document