A Narrative Review of Alzheimer's Disease Stigma.

As the most common form of senile dementia, Alzheimer's disease (AD) is accompanied by a great deal of uncertainty which can lead to fear and stigma for those identified with this devastating disease. As the AD definition evolves from a syndromal to a biological construct, and early diagnoses becomes more commonplace, more confusion and stigma may result. We conducted a narrative review of the literature on AD stigma to consolidate information on this body of research. From the perspective of several stigma theories, we identified relevant studies to inform our understanding of the way in which implementation of the new framework for a biological based AD diagnosis may have resulted in new and emerging stigma. Herein, we discuss the emergence of new AD stigma as our understanding of the definition of the disease changes. We further propose recommendations for future research to reduce the stigma associated with AD.

Alzheimer's Disease and Its Potential Alternative Therapeutics.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease that affects over 5 million individuals in the United States alone. Currently, there are only two kinds of pharmacological interventions available for symptomatic relief of AD; Acetyl Cholinesterase Inhibitors (AChEI) and N-methyl-D-aspartic Acid (NMDA) receptor antagonists and these drugs do not slow down or stop the progression of the disease. Several molecular targets have been implicated in the pathophysiology of AD, such as the tau (τ) protein, Amyloid-beta (Aß), the Amyloid Precursor Protein (APP) and more and several responses have also been observed in the advancement of the disease, such as reduced neurogenesis, neuroinflammation, oxidative stress and iron overload. In this review, we discuss general features of AD and several small molecules across different experimental AD drug classes that have been studied for their effects in the context of the molecular targets and responses associated with the AD progression. These drugs include: Paroxetine, Desferrioxamine (DFO), N-acetylcysteine (NAC), Posiphen/-(-)Phenserine, JTR-009, Carvedilol, LY450139, Intravenous immunoglobulin G 10%, Indomethacin and Lithium Carbonate (Li2CO3).

Identification and Analysis of Alzheimer's Candidate Genes by an Amplitude Deviation Algorithm.

BACKGROUND: Alzheimer's disease (AD) is the most common form of senile dementia. However, its pathological mechanisms are not fully understood. In order to comprehend AD pathological mechanisms, researchers employed AD-related DNA microarray data and diverse computational algorithms. More efficient computational algorithms are needed to process DNA microarray data for identifying AD-related candidate genes. METHODS: In this paper, we propose a specific algorithm that is based on the following observation: When an acrobat walks along a steel-wire, his/her body must have some swing; if the swing can be controlled, then the acrobat can maintain the body balance. Otherwise, the acrobat will fall. Based on this simple idea, we have designed a simple, yet practical, algorithm termed as the Amplitude Deviation Algorithm (ADA). Deviation, overall deviation, deviation amplitude, and 3δ are introduced to characterize ADA. RESULTS: 52 candidate genes for AD have been identified via ADA. The implications for some of the AD candidate genes in AD pathogenesis have been discussed. CONCLUSIONS: Through the analysis of these AD candidate genes, we believe that AD pathogenesis may be related to the abnormality of signal transduction (AGTR1 and PTAFR), the decrease in protein transport capacity (COL5A2 (221729_at), COL5A2 (221730_at), COL4A1), the impairment of axon repair (CNR1), and the intracellular calcium dyshomeostasis (CACNB2, CACNA1E). However, their potential implication for AD pathology should be further validated by wet lab experiments as they were only identified by computation using ADA.

Modulation of SPARC/Hevin Proteins in Alzheimer's Disease Brain Injury.

Alzheimer's disease (AD) is an age-related progressive form of dementia that features neuronal loss, intracellular tau, and extracellular amyloid-ß (Aß) protein deposition. Neurodegeneration is accompanied by neuroinflammation mainly involving microglia, the resident innate immune cell population of the brain. During AD progression, microglia shift their phenotype, and it has been suggested that they express matricellular proteins such as secreted protein acidic and rich in cysteine (SPARC) and Hevin protein, which facilitate the migration of other immune cells, such as blood-derived dendritic cells. We have detected both SPARC and Hevin in postmortem AD brain tissues and confirmed significant alterations in transcript expression using real-time qPCR. We suggest that an infiltration of myeloid-derived immune cells occurs in the areas of diseased tissue. SPARC is highly expressed in AD brain and collocates to Aß protein deposits, thus contributing actively to cerebral inflammation and subsequent tissue repair, and Hevin may be downregulated in the diseased state. However, further research is needed to reveal the exact roles of SPARC and Hevin proteins and associated signaling pathways in AD-related neuroinflammation. Nevertheless, normalizing SPARC/Hevin protein expression such as interdicting heightened SPARC protein expression may confer a novel therapeutic opportunity for modulating AD progression.

Classification of MRI and psychological testing data based on support vector machine.

Alzheimer's disease (AD) is a progressive, and often fatal, brain disease that causes neurodegeneration, resulting in memory loss as well as other cognitive and behavioral problems. Here, we propose a novel multimodal method combining independent components from MRI measures and clinical assessments to distinguish Alzheimer's patients or mild cognitive impairment (MCI) subjects from healthy elderly controls. 70 AD subjects (mean age: 77.15 ± 6.2 years), 98 MCI subjects (mean age: 76.91 ± 5.7 years), and 150 HC subjects (mean age: 75.69 ± 3.8 years) were analyzed. Our method includes the following steps: pre-processing, estimating the number of independent components from the MR image data, extracting effective voxels for classification, and classification using a support vector machine (SVM)-based classifier. As a result, with regards to classifying AD from healthy controls, we achieved a classification accuracy of 97.7%, sensitivity of 99.2%, and specificity of 96.7%; for differentiating MCI from healthy controls, we achieved a classification accuracy of 87.8%, a sensitivity of 86.0%, and a specificity of 89.6; these results are better than those obtained with clinical measurements alone (accuracy of 79.5%, sensitivity of 74.0%, and specificity of 85.1%). We found that (1) both AD patients and MCI subjects showed brain tissue loss, but the volumes of gray matter loss in MCI subjects was far less, supporting the notion that MCI is a prodromal stage of AD; and (2) combining gray matter features from MRI and three commonly used measures of mental status, cognitive function improved classification accuracy, sensitivity, and specificity compared with classification using only independent components or clinical measurements.