Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data by Yang Xi, Qian Wang, Chenxue Wu, Lu Zhang, Ying Chen, Zhu Lan

“…Abstract Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. …”
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Adapting to evolving MRI data: A transfer learning approach for Alzheimer’s disease prediction by Rosanna Turrisi, Sarthak Pati, Giovanni Pioggia, Gennaro Tartarisco

“…Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer’s Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data, leading to potential overfitting. …”
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Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis by Vivens Mubonanyikuzo, Hongjie Yan, Temitope Emmanuel Komolafe, Liang Zhou, Tao Wu, Nizhuan Wang

“… BackgroundAlzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. …”
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Neurogranin in Alzheimer's Disease: Roles in synaptic function, pathology, and potential as a diagnostic biomarker by Rajkumar Bavaharini, Chaitanya Sree Somala, Konda Mani Saravanan, Thirunavukarasou Anand

“…Postsynaptic protein neurogranin (Ng), which plays a role in synaptic plasticity, learning, and memory, has been identified as the candidate biomarker of Alzheimer's disease (AD). Cortical Amyloid β pathology seems to accelerate the onset of clinical symptoms; therefore, it is potentially valuable for early diagnosis of AD and therapeutic intervention. …”
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The use of virtual reality in screening for preclinical Alzheimer's disease: A scoping review protocol. by Yuan Tian, Maneesh V Kuruvilla, Mira Park

“…<h4>Introduction</h4>Preclinical Alzheimer's disease (AD) represents the earliest phase of AD, often years before the onset of mild cognitive impairment (MCI). …”
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Parkinson’s disease and exosomes: from pathogenesis to diagnosis and treatment by A. Mišeikaitė, P. Vaitkienė

“… Parkinson’s disease (PD) is considered the second most common neurodegenerative disease after Alzheimer’s disease, with a mean onset age of 55 years. …”
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Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer’s Disease MRI Data by Ömer Akgüller, Mehmet Ali Balcı, Gabriela Cioca

“…<b>Background</b>: Alzheimer’s disease is a progressive neurological condition marked by a decline in cognitive abilities. …”
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Anti-Amyloid Imaging Abnormality in the Era of Anti-Amyloid Beta Monoclonal Antibodies: Recent Updates for the Radiologist by So Yeong Jeong, Chong Hyun Suh, Jae-Sung Lim, Yangsean Choi, Ho Sung Kim, Sang Joon Kim, Jae-Hong Lee

“…This has increased interest in anti-amyloid immunotherapy for Alzheimer’s disease. Anti-amyloid immunotherapy has shown potential to modify the progression of the disease by specifically binding to amyloid β, a key pathological product in Alzheimer’s disease, and eliminating accumulated amyloid plaques in the brain. …”
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A research landscape analysis on Alzheimer's disease and gerontechnology: Identifying key contributors, hotspots, and emerging trends by Azliyana Azizan, Ph.D, Susi Endrini, Ph.D, Khairul Hafezad Abdullah, Ph.D

“…This paper presents a systematic bibliometric analysis of the research landscape at the intersection of Alzheimer's disease and gerontechnology, identifying key contributors, research hotspots, and emerging trends. …”
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Multimodal MRI Neuroimaging Biomarkers for Cognitive Normal Adults, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease by Ai-Ling Lin, Angela R. Laird, Peter T. Fox, Jia-Hong Gao

“…These advantages have made MRI an important tool to investigate neurodegenerative disorders, including diagnosis, disease progression monitoring, and treatment efficacy evaluation. …”
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Identification of blood plasma protein ratios for distinguishing Alzheimer's disease from healthy controls using machine learning by Ali Safi, Elisa Giunti, Omar Melikechi, Weiming Xia, Noureddine Melikechi

“…Liquid biopsy techniques, especially those involving plasma biomarkers, provide a promising noninvasive method for early diagnosis and disease monitoring. In this study, we analyzed the plasma proteomic profiles of 38 healthy individuals, with an average age of 66.5 years, and 22 patients with Alzheimer's disease, with an average age of 79.7 years. …”
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Interplay between oxidative stress, neuroinflammatory cytokines and melatonin in Alzheimer's disease: Insights from cerebrospinal fluid analysis by Francisco Artime-Naveda, David Hevia, Rebeca Alonso-Arias, Carmen Martínez, Isabel Quirós-González, Rafael Cernuda-Cernuda, Alejando Alvarez-Artime, Iván Menéndez-Valle, Rosa M. Sainz, Juan C. Mayo

“…Abstract: Background: Among neurodegenerative disorders Alzheimer's disease (AD) displays the highest prevalence and the projected increase in its incidence will require new advances in early diagnosis and treatment, particularly for distinguishing AD from other dementias. …”
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APOEε4 alters ApoE and Fabp7 in frontal cortex white matter in prodromal Alzheimer's disease by Marta Moreno-Rodriguez, Sylvia E. Perez, Michael Malek-Ahmadi, Elliott J. Mufson

“…Abstract The ApoE ε4 allele (APOEε4) is a major genetic risk factor for sporadic Alzheimer's disease (AD) and is linked to demyelination and cognitive decline. …”
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Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer&#x2019;s Disease as a Case Study by Rabia Javed, Tahir Abbas, Tariq Shahzad, Khadija Kanwal, Sadaqat Ali Ramay, Muhammad Adnan Khan, Khmaies Ouahada

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Alzheimer’s disease may develop from changes in the immune system, cell cycle, and protein processing following alterations in ribosome function by Akiko Yamakawa, Mutsumi Suganuma, Risa Mitsumori, Shumpei Niida, Kouichi Ozaki, Daichi Shigemizu

“…Abstract The prevalence of Alzheimer’s disease (AD) is increasing as society ages. …”
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Identification of Alzheimer’s Disease Progression Stages Using Topological Measures of Resting-State Functional Connectivity Networks: A Comparative Study by Zhanxiong Wu, Jinhui Wu, Xumin Chen, Xun Li, Jian Shen, Hui Hong

“…A total of 54 subjects from Alzheimer’s Disease Neuroimaging Initiative (ANDI) database, divided into healthy control (HC), mild cognition impairment (MCI), and AD groups, were included in this study. …”
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Enhanced detection of mild cognitive impairment in Alzheimer’s disease: a hybrid model integrating dual biomarkers and advanced machine learning by John Sahaya Rani Alex, R. Roshini, G. Maneesha, Jeetashree Aparajeeta, B. Priyadarshini, Chih-Yang Lin, Chi-Wen Lung

“…Early diagnosis is the most effective strategy to slow the disease's progression. …”
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Sleep quality among sample of Egyptian patients with Alzheimer’s disease: risk factors and role of genetic profiling for ACE, ACE 2 and APO E genotypes by Khaled Ahmed Elbeh, Mohamed Abdala Abbas, Mohammed H. Hassan, Mohamed Moslem Hefny, Shimaa Fathy Sakr, Reham Bakry Mohamed, Mahmoud Ahmed Bekiet, Tarek Desoky

“…Abstract Background Most individuals having Alzheimer’s disease generally experience increased frequency of awakenings as well as prolonged wakefulness periods at night. …”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

“…Abstract Background The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear. Methods This study retrieved AD data sets from the GEO database to identify palmitoylation-associated genes (PRGs). …”
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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems by Sergii Babichev, Igor Liakh, Jiri Skvor

“…To our mind, the implementation of this model promises to significantly improve the efficacy of early diagnosis systems for complex diseases.…”
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