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| http://dx.doi.org/10.5195/ijms.2022.1695 | DOI Listing |
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Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.
Alzheimers Res Ther
January 2025
Department of Neurology, Faculty of Medicine, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu, Oita, 879-5593, Japan.
Background: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatment. This study developed machine learning models to classify positron emission tomography (PET) Aβ-positivity in participants with preclinical and prodromal AD using data accessible to primary care physicians.
View Article and Find Full Text PDFUncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision.
Biol Direct
January 2025
Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
Introduction: Diabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely underexplored.
Methods: In this study, the expression patterns of glycolysis-related genes (GRGs) were first analyzed using the GSE30122, GSE30528, and GSE96804 datasets, followed by an evaluation of the immune landscape in DN.
Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.
Mol Divers
January 2025
State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, 116024, Liaoning, China.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifactorial pathophysiology of AD, monotargeted agents can only alleviate symptoms but not cure AD. Acetylcholinesterase (AChE) and Monoamine oxidase B (MAO-B) are two key targets in the treatment of AD, molecules that inhibiting both targets are considered promising avenue to develop more effective AD therapies.
View Article and Find Full Text PDFA Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.
J Imaging Inform Med
January 2025
Health Informatics, College of Public Health, George Mason University, Fairfax, VA, 22030, USA.
Breast cancer remains a significant global health concern and is a leading cause of mortality among women. The accuracy of breast cancer diagnosis can be greatly improved with the assistance of automatic segmentation of breast ultrasound images. Research has demonstrated the effectiveness of convolutional neural networks (CNNs) and transformers in segmenting these images.
View Article and Find Full Text PDFDistinct neural computations scale the violation of expected reward and emotion in social transgressions.
Commun Biol
January 2025
Department of Psychology, The University of Hong Kong, Hong Kong, China.
Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions.
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