AIMC Topic: Alzheimer Disease

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High-Quality CEST Mapping With Lorentzian-Model Informed Neural Representation.

IEEE transactions on bio-medical engineering
Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conven...

Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

Artificial cells, nanomedicine, and biotechnology
The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multi...

Alzheimer's diagnosis by an efficient pipelined gene selection model based on statistical and biological data analysis.

Computational biology and chemistry
Diagnosing Alzheimer's disease based on gene expression data extracted from microarrays is still an open field of research. Due to the availability of whole-genome data through microarrays technology, diagnosis accuracy is expected to be improved. De...

Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.

Brain : a journal of neurology
Neurological disorders result from the complex and poorly understood contributions of many cell types. It is therefore essential to uncover mechanisms behind these disorders and identify specific therapeutic targets. Single-nucleus technologies have ...

HL-BscPF: Hybrid learning facilitates brain cell auto-identification in multiple pathologies.

Life sciences
AIMS: The rapidly growing scale and complexity of single-cell transcriptomic data in brain research make it increasingly difficult for traditional methods to extract meaningful insights efficiently, highlighting the need for artificial intelligence.

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Computers in biology and medicine
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatom...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

Journal of neuroradiology = Journal de neuroradiologie
INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving ther...

Emerging blood biomarkers in Alzheimer's disease: a proteomic perspective.

Clinica chimica acta; international journal of clinical chemistry
Early detection of Alzheimer's disease (AD) remains a formidable clinical challenge, but emerging blood-based assays show promise for identifying at-risk individuals long before cognitive symptoms arise. This is the first comprehensive synthesis comp...

Clinical Trial Eligibility Criteria Decomposition and Parsing with Large Language Models.

Studies in health technology and informatics
Clinical trial eligibility criteria, often presented as complex free text, pose significant challenges for automated processing. This study introduces a Decomposition and Parsing (DP) workflow to address these challenges by systematically breaking do...

Brain Age Prediction: Deep Models Need a Hand to Generalize.

Human brain mapping
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...