Acupoint combinations used for treatment of Alzheimer's disease: A data mining analysis

Abstract Alzheimer's disease (AD) is the most frequent cause of dementia. Misfolded protein pathological hallmarks of AD are brain deposits of amyloid-β (Aβ) plaques and phosphorylated tau neurofibrillary tangles. However, doubts about the role of Aβ in AD pathology have been raised as Aβ is a common component of extracellular brain deposits found, also by in vivo imaging, in non-demented aged individuals. It has been suggested that some individuals are more prone to Aβ neurotoxicity and hence more likely to develop AD when aging brains start accumulating Aβ plaques. Here, we applied genome-wide transcriptomic profiling of lymphoblastoid cells lines (LCLs) from healthy individuals and AD patients for identifying genes that predict sensitivity to Aβ. Real-time PCR validation identified 3.78-fold lower expression of RGS2 (regulator of G-protein signaling 2; P=0.0085) in LCLs from healthy individuals exhibiting high vs low Aβ sensitivity. Furthermore, RGS2 showed 3.3-fold lower expression (P=0.0008) in AD LCLs compared with controls. Notably, RGS2 expression in AD LCLs correlated with the patients’ cognitive function. Lower RGS2 expression levels were also discovered in published expression data sets from postmortem AD brain tissues as well as in mild cognitive impairment and AD blood samples compared with controls. In conclusion, Aβ sensitivity phenotyping followed by transcriptomic profiling and published patient data mining identified reduced peripheral and brain expression levels of RGS2, a key regulator of G-protein-coupled receptor signaling and neuronal plasticity. RGS2 is suggested as a novel AD biomarker (alongside other genes) toward early AD detection and future disease modifying therapeutics.

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

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v12i1.4149 ◽

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.

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