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  • Open Access


    Exploring exosomes to provide evidence for the treatment and prediction of Alzheimer’s disease


    BIOCELL, Vol.47, No.10, pp. 2163-2176, 2023, DOI:10.32604/biocell.2023.031226

    Abstract Exosomes are extracellular vesicles with a 30–150 nm diameter originating from endosomes. In recent years, scientists have regarded exosomes as an ideal small molecule carrier for the targeted treatment of Alzheimer’s disease (AD) across the blood-brain barrier due to their nanoscale size and low immunogenicity. A large amount of evidence shows that exosomes are rich in biomarkers, and it has been found that the changes in biomarker content in blood, cerebrospinal fluid, and urine are often associated with the onset of AD patients. In this paper, some recent advances in the use of exosomes in the treatment of AD are… More > Graphic Abstract

    Exploring exosomes to provide evidence for the treatment and prediction of Alzheimer’s disease

  • Open Access


    Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

    S M Hasan Mahmud1,2, Md Mamun Ali3, Mohammad Fahim Shahriar1, Fahad Ahmed Al-Zahrani4, Kawsar Ahmed5,6,*, Dip Nandi1, Francis M. Bui5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020

    Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

  • Open Access


    An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

    Athena George1, Bejoy Abraham2, Neetha George3, Linu Shine3, Sivakumar Ramachandran4,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2097-2118, 2023, DOI:10.32604/csse.2023.039262

    Abstract Neurodegeneration is the gradual deterioration and eventual death of brain cells, leading to progressive loss of structure and function of neurons in the brain and nervous system. Neurodegenerative disorders, such as Alzheimer’s, Huntington’s, Parkinson’s, amyotrophic lateral sclerosis, multiple system atrophy, and multiple sclerosis, are characterized by progressive deterioration of brain function, resulting in symptoms such as memory impairment, movement difficulties, and cognitive decline. Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases. Magnetic resonance imaging (MRI) is widely used by neurologists for diagnosing brain abnormalities. The majority of the research… More >

  • Open Access


    Bushen Yizhi Formula regulates the IRE1α pathway to alleviate endoplasmic reticulum stress in an Alzheimer’s disease rat model


    BIOCELL, Vol.47, No.7, pp. 1595-1609, 2023, DOI:10.32604/biocell.2023.027697

    Abstract Background: While the Bushen Yizhi Formula can treat Alzheimer’s disease (AD), the yet to be ascertained specific mechanism of action was explored in this work. Methods: Different concentrations of the Bushen Yizhi Formula and amyloid-beta peptide (Aβ) were used to treat rat pheochromocytoma cells (P12) and human neuroblastoma cells (SH-SY5Y). Cell morphological changes were observed to determine the in vitro cell damage. Cell Counting Kit (CCK)-8 assay and flow cytometry were employed to identify cell viability and apoptosis/cell cycle, respectively. Western blotting and immunohistochemistry were employed to measure the expressions of endoplasmic reticulum stress (ERS)-related proteins (GRP78 and CHOP), p-IRE1α,… More >

  • Open Access


    Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

    Jayashree Shetty1, Nisha P. Shetty1,*, Hrushikesh Kothikar1, Saleh Mowla1, Aiswarya Anand1, Veeraj Hegde2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

    Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

  • Open Access


    Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

    Deepthi K. Oommen*, J. Arunnehru

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

    Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >

  • Open Access


    Proteomics analysis provides novel biomarkers and therapeutic target candidates in the treatment of the Huang-Pu-Tong-Qiao formula in an AD rat model


    BIOCELL, Vol.47, No.6, pp. 1265-1277, 2023, DOI:10.32604/biocell.2023.028811

    Abstract Background: Huang-Pu-Tong-Qiao formula (HPTQ), a traditional Chinese herbal formula, has a variety of pharmacological effects. It has been used to treat Alzheimer’s disease (AD) for decades. This study aimed to screen differentially expressed proteins in the hippocampus of AD model rats treated with HPTQ. Proteomic studies of the effects of HPTQ on AD are key to understanding the therapeutic mechanisms of HPTQ and identifying potential therapeutic targets. Methods: We hence used the isobaric tags for relative and absolute quantification (ITRAQ) approach to investigate the differentially expressed proteins in the hippocampus of AD model rats before and after HPTQ administration and… More >

  • Open Access


    An Optimal Framework for Alzheimer’s Disease Diagnosis

    Amer Alsaraira1,*, Samer Alabed1, Eyad Hamad1, Omar Saraereh2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 165-177, 2023, DOI:10.32604/iasc.2023.036950

    Abstract Alzheimer’s disease (AD) is a kind of progressive dementia that is frequently accompanied by brain shrinkage. With the use of the morphological characteristics of MRI brain scans, this paper proposed a method for diagnosing moderate cognitive impairment (MCI) and AD. The anatomical features of 818 subjects were calculated using the FreeSurfer software, and the data were taken from the ADNI dataset. These features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal aging. With these preprocessed characteristics, the extreme learning machine served as a classifier for… More >

  • Open Access


    Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

    Hala Ahmed1, Hassan Soliman1, Shaker El-Sappagh2,3,4, Tamer Abuhmed4,*, Mohammed Elmogy1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2773-2795, 2023, DOI:10.32604/csse.2023.036371

    Abstract The precise diagnosis of Alzheimer’s disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the skull is removed to lead… More >

  • Open Access


    Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

    V. P. Nithya*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2601-2618, 2023, DOI:10.32604/csse.2023.030503

    Abstract The prediction of mild cognitive impairment or Alzheimer’s disease (AD) has gained the attention of huge researchers as the disease occurrence is increasing, and there is a need for earlier prediction. Regrettably, due to the high-dimensionality nature of neural data and the least available samples, modelling an efficient computer diagnostic system is highly solicited. Learning approaches, specifically deep learning approaches, are essential in disease prediction. Deep Learning (DL) approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging. A novel 3D-Convolutional Neural Network (3D-CNN) architecture is proposed to predict AD with Magnetic resonance imaging (MRI) data.… More >

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