A multiphase model of growth factor-regulated atherosclerotic cap formation

Blood Lipoproteins Shape the Phenotype and Lipid Content of Early Atherosclerotic Lesion Macrophages: A Dual-Structured Mathematical Model

Macrophages in atherosclerotic lesions exhibit a spectrum of behaviours or phenotypes. The phenotypic distribution of monocyte-derived macrophages (MDMs), its correlation with MDM lipid content, and relation to blood lipoprotein densities are not well understood. Of particular interest is the balance between low density lipoproteins (LDL) and high density lipoproteins (HDL), which carry bad and good cholesterol respectively. To address these issues, we have developed a mathematical model for early atherosclerosis in which the MDM population is structured by phenotype and lipid content. The model admits a simpler, closed subsystem whose analysis shows how lesion composition becomes more pathological as the blood density of LDL increases relative to the HDL capacity. We use asymptotic analysis to derive a power-law relationship between MDM phenotype and lipid content at steady-state. This relationship enables us to understand why, for example, lipid-laden MDMs have a more inflammatory phenotype than lipid-poor MDMs when blood LDL lipid density greatly exceeds HDL capacity. We show further that the MDM phenotype distribution always attains a local maximum, while the lipid content distribution may be unimodal, adopt a quasi-uniform profile or decrease monotonically. Pathological lesions exhibit a local maximum in both the phenotype and lipid content MDM distributions, with the maximum at an inflammatory phenotype and near the lipid content capacity respectively. These results illustrate how macrophage heterogeneity arises in early atherosclerosis and provide a framework for future model validation through comparison with single-cell RNA sequencing data.

Machine learning and image analysis in vascular surgery

Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-learn" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.

Shear stress and plaque microenvironment induce heterogeneity: A multiscale microenvironment evolution model

BACKGROUND

Both clinical images and in vivo observations have demonstrated the heterogeneity in atherosclerotic plaque composition. However, the quantitative mechanisms that contribute to the heterogeneity, such as the wall shear stress (WSS) and the interplays among microenvironmental factors are still unclear.

METHODS

We develop a multiscale model coupling computational fluid dynamics, interactions of microenvironmental factors and evolutions of cellular behaviors to investigate the formation of plaque heterogeneity in a three-dimensional vessel segment. The model involves WSS, lipid deposition and inflammatory response to reveal the dynamic balance existed between the lipid metabolism and the phagocytosis of macrophages.

RESULTS

The dynamic balance in microenvironment is influenced by both the WSS and the interactions with microenvironmental factors, and consequently results in the longitudinal heterogeneity observed in plaque pathology. In addition, plaque heterogeneity can be reduced by decreasing low WSS area at downstream, as well as by altering the phagocytic abilities of macrophage on lipoproteins, which may be used to develop future plaque regression strategies.

CONCLUSIONS

This multiscale modeling provides a framework to understand the mechanisms in dynamics of plaque composition and also provides quantitative information to better risk stratification of plaque vulnerability in future clinical practice.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Toward the Effective Bioengineering of a Pathological Tissue for Cardiovascular Disease Modeling: Old Strategies and New Frontiers for Prevention, Diagnosis, and Therapy

Cardiovascular diseases (CVDs) still represent the primary cause of mortality worldwide. Preclinical modeling by recapitulating human pathophysiology is fundamental to advance the comprehension of these diseases and propose effective strategies for their prevention, diagnosis, and treatment. In silico, in vivo, and in vitro models have been applied to dissect many cardiovascular pathologies. Computational and bioinformatic simulations allow developing algorithmic disease models considering all known variables and severity degrees of disease. In vivo studies based on small or large animals have a long tradition and largely contribute to the current treatment and management of CVDs. In vitro investigation with two-dimensional cell culture demonstrates its suitability to analyze the behavior of single, diseased cellular types. The introduction of induced pluripotent stem cell technology and the application of bioengineering principles raised the bar toward in vitro three-dimensional modeling by enabling the development of pathological tissue equivalents. This review article intends to describe the advantages and disadvantages of past and present modeling approaches applied to provide insights on some of the most relevant congenital and acquired CVDs, such as rhythm disturbances, bicuspid aortic valve, cardiac infections and autoimmunity, cardiovascular fibrosis, atherosclerosis, and calcific aortic valve stenosis.

Mathematical model of atherosclerotic aneurysm

Atherosclerosis is a major cause of abdominal aortic aneurysm (AAA) and up to 80% of AAA patients have atherosclerosis. Therefore it is critical to understand the relationship and interactions between atherosclerosis and AAA to treat atherosclerotic aneurysm patients more effectively. In this paper, we develop a mathematical model to mimic the progression of atherosclerotic aneurysms by including both the multi-layer structured arterial wall and the pathophysiology of atherosclerotic aneurysms. The model is given by a system of partial differential equations with free boundaries. Our results reveal a 2D biomarker, the cholesterol ratio and DDR1 level, assessing the risk of atherosclerotic aneurysms. The efficacy of different treatment plans is also explored via our model and suggests that the dosage of anti-cholesterol drugs is significant to slow down the progression of atherosclerotic aneurysms while the additional anti-DDR1 injection can further reduce the risk.