S. Meenakshi Ammal^*, P. S. Manoharan

Computer Systems Science and Engineering, Vol.44, No.1, pp. 367-390, 2023, DOI:10.32604/csse.2023.025230 - 01 June 2022

Abstract Worldwide, many elders are suffering from Alzheimer’s disease (AD). The elders with AD exhibit various abnormalities in their activities, such as sleep disturbances, wandering aimlessly, forgetting activities, etc., which are the strong signs and symptoms of AD progression. Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage. The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients (ADP) using wearables. In the proposed work, a publicly available dataset collected using wearables is applied. Currently,… 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 - 06 December 2022

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… More >

Atif Mehmood^1,*, Ahed Abugabah¹, Ahmed Ali AlZubi², Louis Sanzogni³

Computer Systems Science and Engineering, Vol.43, No.1, pp. 305-315, 2022, DOI:10.32604/csse.2022.018520 - 23 March 2022

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of… More >

RUXIN ZHANG¹, CHENGGANG LI², RUOCHEN DU¹, YITONG YUAN¹, BICHUN ZHAO¹, YUJUAN ZHANG¹, CHUNFANG WANG^1,*

BIOCELL, Vol.46, No.3, pp. 583-592, 2022, DOI:10.32604/biocell.2022.017248 - 18 November 2021

Abstract Extracellular β-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) are the pathological hallmarks of Alzheimer’s disease (AD). Studies have shown that aggregates of extracellular Aβ can induce neuroinflammation mediated neurotoxic signaling through microglial activation and release of pro-inflammatory factors. Thus, modulation of Aβ might be a potential therapeutic strategy for modifying disease progression. Recently, a large number of reports have confirmed the beneficial effects of mesenchymal stem cells (MSCs) on AD. It is believed to reduce neuroinflammation, reduce Aβ amyloid deposits and NFTs, increase acetylcholine levels, promote neurogenesis, reduce neuronal damage, and improve working memory and More >

Himanshu Padole^*, S. D. Joshi, Tapan K. Gandhi

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310 - 09 October 2021

Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization More >

Nora Shoaip¹, Amira Rezk¹, Shaker EL-Sappagh^2,3, Tamer Abuhmed^4,*, Sherif Barakat¹, Mohammed Elmogy⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3531-3548, 2021, DOI:10.32604/cmc.2021.019069 - 24 August 2021

Abstract Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction. In this regard, the need becomes more urgent for biomarker-based detection. A key issue in understanding AD is the need… More >

NARAYANAN NAMPOOTHIRI V. P.¹, VIGNESH SUNDARARAJAN¹, PALLAVI DAN¹, G. DEVANAND VENKATASUBBU^2,*, SAHABUDEEN SHEIK MOHIDEEN^1,*

BIOCELL, Vol.45, No.5, pp. 1251-1262, 2021, DOI:10.32604/biocell.2021.015090 - 12 July 2021

Abstract Alzheimer’s disease (AD) is one of the most common forms of dementia. Cognitive dysfunction and memory loss are the two main clinical symptoms of AD. Drosophila melanogaster models of AD, which are based on overexpression of human amyloid β (Aβ) or human tau (hTau) protein, have been used to study the mechanism underlying AD and to screen potential therapeutic compounds. Drugs that are currently available for AD provide only symptomatic relief. Huge unmet medical needs exists to slow, stop, or reverse the progression of AD. Thymoquinone (TQ) is an active ingredient isolated from Nigella sativa (NS) and… More >

Shuqiao Liu, Junliang Li, Xiaojie Li^*

Journal on Internet of Things, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jiot.2020.09797 - 22 September 2020

Abstract In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and… More >