QIAN CHEN^1,#, XIN LEI^1,#, GUANHUA HU^1,#, YAN WANG², ZHENGQING FANG¹, GUOQUAN WANG¹, HANG SONG¹, SHU YE^1,*, BIAO CAI^1,*

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 >

Amer Alsaraira^1,*, 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 >

Hala Ahmed¹, Hassan Soliman¹, Shaker El-Sappagh^2,3,4, Tamer Abuhmed^4,*, 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 >

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 >

Mudiyala Aparna, Battula Srinivasa Rao^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796

Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into… More >

Aliaa El-Gawady^1,*, BenBella S. Tawfik¹, Mohamed A. Makhlouf^1,2

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5559-5572, 2023, DOI:10.32604/cmc.2023.034734

Abstract Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS… More >

Meshal Alharbi, Shabana R. Ziyad^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5483-5505, 2023, DOI:10.32604/cmc.2023.032341

Abstract Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to… More >

SHOUKAI YU^1,2,3,*, LINGMEI QIAN¹, JUN MA^1,*

Oncology Research, Vol.30, No.1, pp. 13-22, 2022, DOI:10.32604/or.2022.026141

Abstract Apolipoprotein E (APOE), a gene identified as one of the strongest genetic factors contributing to the risk determinant of developing late-onset Alzheimer’s disease (AD), may also contribute to the risk of cancer. However, no pan-cancer analysis has been conducted specifically for the APOE gene. In this study, we investigated the oncogenic role of the APOE gene across cancers by GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas). Based on the available data, we found that most cancer types overexpress APOE, and clear associations exist between the expression level of APOE and prognosis in tumor patients. The expression of… More >

YINGHUI GAO, DENGTAI WEN^*, SHIJIE WANG, JINGFENG WANG

BIOCELL, Vol.47, No.1, pp. 41-49, 2023, DOI:10.32604/biocell.2022.022689

Abstract Presenilin (Psn) protein is associated with organismal aging. Mutations in the Psn gene may lead to Alzheimer’s disease (AD), dilated cardiomyopathy (DCM), and many age-dependent degenerative diseases. These diseases seriously affect the quality of life and longevity of the population and place a huge burden on health care and economic systems around the world. Humans have two types of Psn, presenilin-1 (PSEN1) and presenilin-2 (PSEN2). Mutations in the genes encoding PSEN1, PSEN2, and amyloid precursor protein (APP) have been identified as the major genetic causes of AD. Psn is a complex gene strongly influenced by genetic and environmental factors. The… More >

Zahid Ullah^1,*, Mona Jamjoom²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752

Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such… More >