Dyslipidemia

Atherosclerosis through Hierarchical Explainable Neural Network Analysis

In this work, we study the problem pertaining to personalized classification of subclinical atherosclerosis by developing a hierarchical graph neural network framework to leverage two characteristic modalities of a patient: clinical features within the context of the cohort, and molecular data unique to individual patients. Current graph-based methods for disease classification detect patient-sp...

Recommendations for the Management of Diabetes During Ramadan Applying the Principles of the ADA/ EASD Consensus: Update 2025.

Ramadan fasting is a sacred ritual observed by approximately 1.8 billion Muslims each year, most of whom adhere to fasting due to its significance as a core pillar of Islam. Able-bodied Muslims who are capable of fasting are religiously required to do so. Ramadan is profoundly spiritual and of great importance in the Muslim community that occurs for roughly 30 days, in alignment with the lunar cal...

Computed Tomography Advancements in Plaque Analysis: From Histology to Comprehensive Plaque Burden Assessment.

Advancements in coronary computed tomography angiography (CCTA) facilitated the transition from traditional histological approaches to comprehensive p...

Hybrid strategy of coronary atherosclerosis characterization with T1-weighted MRI and CT angiography to non-invasively predict periprocedural myocardial injury.

AIMS: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after pe...

Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests

Clinical laboratory results are ubiquitous in any diagnosis making. Predicting abnormal values of not prescribed tests based on the results of perfo...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's ...

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis.

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance.

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these condition...

PET imaging of atherosclerosis: artificial intelligence applications and recent advancements.

PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such as inflammation and microcalcification. Among ava...

The role of mitochondrial dysfunction in the pathogenesis of atherosclerosis: A new exploration from bioinformatics analysis.

Atherosclerosis (AS) is a complex cardiovascular disease associated with mitochondrial dysfunction (MD), which contributes to plaque formation and ins...

Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding.

BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serve...

Can Copulas Be Used for Feature Selection? A Machine Learning Study on Diabetes Risk Prediction

Accurate diabetes risk prediction relies on identifying key features from complex health datasets, but conventional methods like mutual information ...

Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.

This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusi...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the no...

Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.

Cardiovascular diseases (CVDs) are still one of the leading causes of death globally, underscoring the importance of early and right risk prediction f...

Diagnostic value of small dense low-density lipoprotein and trace elements in coronary artery disease.

Coronary artery disease (CAD) is a worldwide leading cause of death. Considering that 20%-40% of patients with CAD have a long asymptomatic period of ...

Recognizing artery segments on carotid ultrasonography using embedding concatenation of deep image and vision-language models.

Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arterie...

Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis.

Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and...

Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis.

BACKGROUND: Carotid artery calcifications are important markers of cardiovascular health, often associated with atherosclerosis and a higher risk of s...

Processing UK Biobank High Resolution Accelerometry Data for Unsupervised Identification of Activity Profiles and Their Differences in Clinically Relevant Outcome Parameters - The ATLAS Index Revisited.

Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Buildin...