Atherosclerotic cardiovascular disease risk based on SCORE algorithms and reclassification based on preclinical atherosclerotic plaques.

Background: The aim of this study was to determine the efficacy of the number of carotid and common femoral bifurcations with plaque (NBP) detected by ultrasound in reclassifying atherosclerotic cardiovascular disease (ASCVD) risk obtained from SCORE algorithms. Data from the cohort of 1000 individuals free from ASCVD in the Cyprus Epidemiological Study on Atherosclerosis was used.

Methods: In each predicted ASCVD risk class (low, moderate, high) based on SCORE algorithms and baseline data, the observed 10-year risk of subgroups according to the NBP was used to reclassify participants.

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

Background And Objective: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimensional CBUS plaque segmentation deep learning (DL)-based solutions, achieving promising results.

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0 (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD.

Inhibition of the mitochondrial pyruvate carrier in astrocytes reduces amyloid and tau accumulation in the 3xTgAD mouse model of Alzheimer's disease.

Alzheimer's Disease (AD) is characterized by an accumulation of pathologic amyloid-beta (Aβ) and Tau proteins, neuroinflammation, metabolic changes and neuronal death. Reactive astrocytes participate in these pathophysiological processes by releasing pro-inflammatory molecules and recruiting the immune system, which further reinforces inflammation and contributes to neuronal death. Besides these neurotoxic effects, astrocytes can protect neurons by providing them with high amounts of lactate as energy fuel.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk.