Dyslipidemia

Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing.

The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homoge...

Atherosclerotic Burden and Remodeling Patterns of the Popliteal Artery as Detected in the Magnetic Resonance Imaging Osteoarthritis Initiative Data Set.

Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis Initiative) to study the impact of atherosclerosis risk factors on vessel dimensions and characterize remodeling patterns. Methods and Results Magneti...

Is Machine Learning-derived Low-Density Lipoprotein Cholesterol estimation more reliable than standard closed form equations? Insights from a laboratory database by comparison with a direct homogeneous assay.

BACKGROUND: There is no consensus on the best method to estimate Low Density Lipoprotein-Cholesterol (LDL-C) in routine laboratories.

Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study.

Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role ...

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks.

Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and d...

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associ...

Regression plane concept for analysing continuous cellular processes with machine learning.

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-p...

Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.

Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutiona...

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly lar...

Enzymatic preparation of phytosterol esters with fatty acids from high-oleic sunflower seed oil using response surface methodology.

Phytosterol esters are functional compounds that can effectively reduce plasma cholesterol concentration, and have wide applications in the food indus...

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.

According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly...

Virtual Transcriptomics: Noninvasive Phenotyping of Atherosclerosis by Decoding Plaque Biology From Computed Tomography Angiography Imaging.

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Therapeutic Potentials of Selected Antihypertensive Agents and Their Fixed-Dose Combinations Against Trastuzumab-Mediated Cardiotoxicity.

Trastuzumab () is useful in the clinical management of HER2-positive metastatic breast, gastric, and colorectal carcinoma but has been limited by its ...

Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic.

BACKGROUND: Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits.

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases includi...

Automated Histological Segmentation on Micro Computed Tomography Images of Atherosclerotic Arteries.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these di...

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features.

In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also ...

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of th...