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Influence of medical conditions on the diagnostic accuracy of plasma p-tau217 and p-tau217/Aβ42
INTRODUCTION: Blood-based biomarkers (BBMs) can enable early detection of brain amyloid beta (Aβ) pathology in cognitively unimpaired individuals. However, the extent to which common medical conditions affect biomarker performance remains unclear.
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Levetiracetam prevents Aβ<sub>42</sub> production through SV2a-dependent modulation of App processing in Alzheimer's disease models
In Alzheimer's disease (AD), amyloid-beta (Aβ) peptides are produced by proteolytic cleavage of the amyloid precursor protein (APP), which can occur during synaptic vesicle (SV) cycling at presynapses. Precisely how amyloidogenic APP processing may impair presynaptic proteostasis and how to therapeutically target this process remains poorly understood. Using App knock-in mouse models of early Aβ pathology, we found proteins with hampered degradation accumulate at presynaptic sites. At this mild...
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Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury
The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (N = 13,394, 5,900 males; mean age:...
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Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data
Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and...
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Lumipulse-Measured Cerebrospinal Fluid Biomarkers for the Early Detection of Alzheimer Disease
BACKGROUND AND OBJECTIVES: CSF biomarkers of Aβ42 and phosphorylated tau (p-tau181) are used clinically for the detection of Alzheimer disease (AD) pathology during life. CSF biomarker validation studies have largely used clinical diagnoses and/or amyloid PET imaging as the reference standard. The few existing CSF-to-autopsy studies have been restricted to late-stage AD. This CSF-to-autopsy study investigated associations between CSF biomarkers of AD and AD neuropathologic changes among brain...
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Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury
The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults ( N = 13,394 , 5,900 males; mean...
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Multi-omics after O-GlcNAc alteration identified cellular processes promoting aneuploidy after loss of O-GlcNAc transferase
CONCLUSION: These data show how a multi-Omics platform can disentangle the pleotropic nature of O-GlcNAc to discern how OGT fine-tunes multiple cellular pathways involved in aneuploidy.
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Blood-derived mitochondrial DNA copy number is associated with Alzheimer disease, Alzheimer-related biomarkers and serum metabolites
CONCLUSIONS: Our study indicates that mtDNA-CN measured in blood is predictive of AD and is associated with AD biomarkers including plasma NFL particularly in females. Further, we illustrate that decreased mtDNA-CN possibly increases AD risk through dysregulation of mitochondrial lipid metabolism and inflammation.
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Inhibiting mtDNA transcript translation alters Alzheimer's disease-associated biology
INTRODUCTION: Alzheimer's disease (AD) features changes in mitochondrial structure and function. Investigators debate where to position mitochondrial pathology within the chronology and context of other AD features.
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APOE4 carriers display loss of anticipatory cerebral vascular regulation over AD progression
INTRODUCTION: Maintenance of cerebral blood flow during orthostasis is impaired with aging and associated with cognitive decline, but the effect of Apolipoprotein 4-allele (APOE4) is unknown.
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NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research
NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimer's Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data...
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LEAP! Rx: A randomized trial of a pragmatic approach to lifestyle medicine
INTRODUCTION: Clinicians lack the tools to incorporate physical activity into clinical care for Alzheimer's disease prevention. We tested a 52-week exercise and health education program (Lifestyle Empowerment for Alzheimer's Prevention [LEAP! Rx]) that integrates clinician referrals and community-based fitness resources.
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Linkage Disequilibrium-Informed Deep Learning Framework to Identify Genetic Loci for Alzheimer's Disease Using Whole Genome Sequencing Data
The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD)...
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Referral Sources Across Racial and Ethnic Groups at Alzheimer's Disease Research Centers
CONCLUSIONS: Further studies are needed to better understand the systemic and structural factors that contribute to differences in referral sources and disparities in recruitment of URG into ADRD studies.
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Adverse Events During a 12-month Multi-Site and Dose-Response Aerobic Exercise Intervention
CONCLUSIONS: While aerobic exercise increased the risk of intervention-related AE, the overall risk remained low. Higher adherence to the exercise regimen was associated with fewer AE. These findings suggest aerobic exercise is generally safe in older adults, with the benefits outweighing the risks.