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Highly specific amyloid and tau PET ligands for ATN classification in suspected Alzheimer's disease patients.
Ann Nucl Med
January 2025
Department of Radiological Sciences, School of Health Science, Fukushima Medical University, 10-6 Sakae, Fukushima City, Fukushima, 960-8516, Japan.
Objective: This study aims to accurately classify ATN profiles using highly specific amyloid and tau PET ligands and MRI in patients with cognitive impairment and suspected Alzheimer's disease (AD). It also aims to explore the relationship between quantified amyloid and tau deposition and cognitive function.
Methods: Twenty-seven patients (15 women and 12 men; age range: 64-81 years) were included in this study.
Fecal propionate is a signature of insulin resistance in polycystic ovary syndrome.
Front Cell Infect Microbiol
January 2025
Center of Reproductive Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Objective: To investigate the roles of fecal short-chain fatty acids (SCFAs) in polycystic ovary syndrome (PCOS).
Methods: The levels of SCFAs (acetate, propionate, and butyrate) in 83 patients with PCOS and 63 controls were measured, and their relationships with various metabolic parameters were analyzed. Intestinal microbiome analysis was conducted to identify relevant bacteria.
Multiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions.
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
Early detection of canine hemangiosarcoma via cfDNA fragmentation and copy number alterations in liquid biopsies using machine learning.
Front Vet Sci
January 2025
GenesisEgo, Seoul, Republic of Korea.
Hemangiosarcoma is a highly malignant tumor commonly affecting canines, originating from endothelial cells that line blood vessels, underscoring the importance of early detection. This canine cancer is analogous to human angiosarcoma, and the development of liquid biopsies leveraging cell-free DNA (cfDNA) represents a promising step forward in early cancer diagnosis. In this study, we utilized Whole Genome Sequencing (WGS) to analyze fragment sizes and copy number alterations (CNAs) in cfDNA from 21 hemangiosarcoma-affected and 36 healthy dogs, aiming to enhance early cancer detection accuracy through machine learning models.
View Article and Find Full Text PDFAn Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.
J Urol
January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO.
Purpose: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) prior to biopsy and applied artificial intelligence models to these DBSI metrics to predict csPCa.
Materials And Methods: Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences prior to prostate biopsy.
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