Efficient Alzheimer's Disease Research Articles

Aβ8-20 Fragment as an Anti-Fibrillogenic and Neuroprotective Agent: Advancing toward Efficient Alzheimer's Disease Treatment.

Alzheimer's disease (AD) is the most common cause of dementia, characterized by a spectrum of symptoms associated with memory loss and cognitive decline with deleterious consequences in everyday life. The lack of specific drugs for the treatment and/or prevention of this pathology makes AD an ever-increasing economic and social emergency. Oligomeric species of amyloid-beta (Aβ) are recognized as the primary cause responsible for synaptic dysfunction and neuronal degeneration, playing a crucial role in the onset of the pathology. Several studies have been focusing on the use of small molecules and peptides targeting oligomeric species to prevent Aβ aggregation and toxicity. Among them, peptide fragments derived from the primary sequence of Aβ have also been used to exploit any eventual recognition abilities toward the full-length Aβ parent peptide. Here, we test the Aβ8-20 fragment which contains the self-recognizing Lys-Leu-Val-Phe-Phe sequence and lacks Arg 5 and Asp 7 and the main part of the C-terminus, key points involved in the aggregation pathway and stabilization of the fibrillary structure of Aβ. In particular, by combining chemical and biological techniques, we show that Aβ8-20 does not undergo random coil to β sheet conformational transition, does not form amyloid fibrils by itself, and is not toxic for neuronal cells. Moreover, we demonstrate that Aβ8-20 mainly interacts with the 4-11 region of Aβ1-42 and inhibits the formation of toxic oligomeric species and Aβ fibrils. Finally, our data show that Aβ8-20 protects neuron-like cells from Aβ1-42 oligomer toxicity. We propose Aβ8-20 as a promising drug candidate for the treatment of AD.

An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs

A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed. Nevertheless, it is still challenging to predict AD by classifying them into AD, Mild Cognitive Impairment (MCI), along with Normal Control (NC) regarding larger features. By utilizing the Momentum Golden Eagle Optimizer-centric Transient Multi-Layer Perceptron network (Momentum GEO-Transient MLP), an effectual AD prediction technique has been proposed to trounce the aforementioned issues. Firstly, the input images are supplied for post-processing. In post-processing, by employing Patch Wise L1 Norm (PWL1N), the image resizing along with noise removal is engendered. Then, by utilizing Truncate Intensity Based Operation (TIBO) from the post-processed images, the unwanted brain parts are taken away. Next, the skull-stripped images are pre-processed. In this, by deploying Carnot Cycle Entropy-centric Global and Local technique (c2EBGAL), the images are normalized along with ameliorated. Afterward, by implementing Modified Emperor Penguins Colony-centered Sparse Subspace Clustering (MEPC-SSC), the pre-processed images are segmented. Then, for extracting the features, the segmented images are utilized; subsequently, the features being extracted are fed to the Momentum GEO-Transient MLPs.For transferring images fromMRI into more compact higher-level features, this system is wielded for fusing features from diverse layers. The parameters, which minimize the computation complexity, are decreased. For AD classification, the proposed technique is analogized to the prevailing methodologies regardingaccuracy, sensitivity, specificity et cetera along with acquired enhanced outcomes. Thus, the proposed system is apt for the AD diagnosis.

HAPPCAP‐AD (Human‐Application Combined Approach for Prevention of Alzheimer's Disease): A novel feasibility study of a personalized midlife intervention to prevent AD

AbstractBackgroundDeveloping efficient Alzheimer's disease (AD) prevention strategies is imperative as AD has no cure. Physical inactivity, obesity, type 2 diabetes [T2D], hypertension, smoking, depression, and low educational attainment, are the seven major potentially modifiable risk factors showing consistent association with AD. We have launched a novel, "real‐life", feasibility study of personalized 18‐months intervention for prevention of cognitive decline in middle‐aged individuals at high AD risk due to a parental family history. Our study implements an innovative approach combining a smartphone application (app) with study team guidance through phone‐calls. Our aims are to investigate: 1) The feasibility of our novel intervention and factors associated with adherence to it; 2) Whether our intervention improves AD risk factors and whether greater adherence is associated with larger improvement; and 3) Whether our intervention improves cognition and cerebral blood flow.MethodWe will recruit one hundred 40‐65 years old AD patients' offspring, to two groups (67 at the intervention group and 33 at the passive control). All participants will have at least two risk factors of the seven major modifiable AD risk factors. The 18‐months intervention will include advice for risk factors improvement, interactive follow‐up using a smartphone app and dynamic feedback on risk factors management for its modification, through both the app and the study team.ResultRecruitment has recently begun, through publishing an online eligibility questionnaire. Thus far, 200 individuals have completed the questionnaire, with 113 fulfilling eligibility criteria‐ among them 86% with physical inactivity, 87% with obesity, 38% with T2D, 17% with hypertension, 13% smoking, and 32% with depression. Thus far 33 participants have been recruited‐ Among them, 10 participants have completed the baseline evaluation, and have been randomized to the study's arms (8 of them had started the active intervention, with an average app‐adherence of 83%). additional 23 participants have completed partial base‐line evaluation.ConclusionResults of this study will provide feasibility data on the adherence to the human‐app approach, characteristics of those who adhere, on whether the approach improves risk factors, and will provide initial data on intervention effects on cognition. At AAIC, interim results will be presented.