Dyslipidemia

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus (T2DM) patients have been planned to be estimated according to precise and reliable methods, artificial networks and machine learning (ML). Materials and methods In this study, an experimental data set of relevant features (adipocytokines and anthro...

Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs.

Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods have limited application in living foam cells. Here, using optical diffraction tomography (ODT), we performed quantitative morphological and biophysical ...

Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

BACKGROUND AND OBJECTIVE: The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosc...

Bio-transformation of green tea infusion with tannase and its improvement on adipocyte metabolism.

Catechins in green tea possess various health benefits. Enzymatic treatment improves physiological activities by inducing bioconversion of catechins. ...

Using machine learning models to improve stroke risk level classification methods of China national stroke screening.

BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in Chi...

Methylglyoxal-derived advanced glycation end products induce matrix metalloproteinases through activation of ERK/JNK/NF-κB pathway in kidney proximal epithelial cells.

The accumulation of reactive α-dicarbonyl leading to advanced glycation end products (AGEs) have been linked to pathophysiological diseases in many st...

Isolation, Identification and Characteristics of From Diseased Crucian Carp ().

species often cause disease in farmed fish. In the present study, dominant bacteria were isolated from diseased crucian carp (). Based on this, a bac...

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey.

Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of card...

Statistical and machine learning methodology for abdominal aortic aneurysm prediction from ultrasound screenings.

A method of analysis of a database of patients (n = 10 329) screened for an abdominal aortic aneurysm (AAA) is presented. Self-reported height, weight...

Anti-varicella Zoster Virus IgG and hsCRP Levels Correlate with Progression of Coronary Artery Atherosclerosis.

The relationship between high levels of anti-Varicella Zoster Virus (VZV) IgG in cerebrospinal fluid (CSF) and cerebrovascular atherosclerosis commend...

Can we use LDL instead of egg yolk in BotuCrio® extender to cryopreserve sperm from the Mangalarga Marchador stallion?

The objective of this study was to compare the BotuCrio extender with the Merk - egg yolk and the INRA 82 modified by the inclusion of acetamide, meth...

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated...

Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI.

PURPOSE: Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progressi...

Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.

Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, w...

Machine learning and blood pressure.

Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised...

Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic Disease.

BACKGROUND: The diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in detecting ischemia in m...

Radiomics versus Visual and Histogram-based Assessment to Identify Atheromatous Lesions at Coronary CT Angiography: An ex Vivo Study.

Background Visual and histogram-based assessments of coronary CT angiography have limited accuracy in the identification of advanced lesions. Radiomic...

Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease.

BACKGROUND: Chronic kidney disease (CKD) patients show an increased burden of atherosclerosis and high risk of cardiovascular events (CVEs). There are...

Comparisons among Machine Learning Models for the Prediction of Hypercholestrolemia Associated with Exposure to Lead, Mercury, and Cadmium.

Lead, mercury, and cadmium are common environmental pollutants in industrialized countries, but their combined impact on hypercholesterolemia (HC) is ...

Validation of a simple equation for glomerular filtration rate measurement based on plasma iohexol disappearance.

BACKGROUND: A simple equation for glomerular filtration rate (GFR) measurement based on only plasma samples during the slow compartment after injectio...