Stress distribution analysis in healthy and stenosed carotid artery models reconstructed from in vivo ultrasonography

Interplay of damage and repair in the control of epithelial tissue integrity in response to cyclic loading

Epithelial tissues are continuously exposed to cyclic stretch in vivo. Physiological stretching has been found to regulate soft tissue function at the molecular, cellular, and tissue scales, allowing tissues to preserve their homeostasis and adapt to challenges. In contrast, dysregulated or pathological stretching can induce damage and tissue fragilisation. Many mechanisms have been described for the repair of epithelial tissues across a range of timescales. In this review, we present the timescales of (i) physiological cyclic loading regimes, (ii) strain-regulated remodeling and damage accumulation, and (iii) repair mechanisms in epithelial tissues. We discuss how the response to cyclic loading in biological tissues differs from synthetic materials, in that damage can be partially or fully reversed by repair mechanisms acting on timescales shorter than cyclic loading. We highlight that timescales are critical to understanding the interplay between damage and repair in tissues that experience cyclic loading, opening up new avenues for exploring soft tissue homeostasis.

Contrast-Enhanced Ultrasound Combined With 2D Strain Imaging and Histopathological Multimodal Assessment of Carotid Plaque Vulnerability

OBJECTIVE

The aim of this study was to explore the value of contrast-enhanced ultrasound (CEUS) combined with 2-D strain imaging in evaluating carotid plaque vulnerability and the correlations among CEUS perfusion parameters, strain parameters and histopathological findings in different plaque segments.

METHODS

Patients with carotid artery stenosis who underwent carotid endarterectomy (CEA) at the First Affiliated Hospital of Xinjiang Medical University from September 2020 to June 2021 underwent preoperative carotid artery 2-D ultrasonography and CEUS. The plaques were divided into three segments: the proximal end of the shoulder, central cap and distal end of the shoulder. The peak intensity (PI) value and strain rate parameters of the regions of interest were analyzed. Plaques were divided into a stable group (8 cases) and an unstable group (19 cases). The microvascular density (MVD) and vascular endothelial growth factor (VEGF) expression of each patch in the unstable group were analyzed.

RESULTS

The peak strain during the systolic period in each plaque segment in both groups showed the following pattern: proximal end shoulder > distal end shoulder > top (p < 0.05). The PI value for CEUS is also represented. In the unstable group, the PI values of each segment of the plaque were positively correlated with the MVD, near-center PI value and VEGF average optical density value. The average optical density of each segment was positively correlated with the MVD (p < 0.05). There were positive correlations between the PI values of the proximal and distal shoulder and the strain values (p < 0.05), and the MVD value of each segment, VEGF value and strain value were positively correlated (p < 0.05).

CONCLUSION

PI and the pathological tissue components represented by CEUS were positively correlated with the mechanical parameters of the plaque along the long axis. There may be overlap between the high shear stress area of the plaque and the neovascular aggregation area, and the combination of the two has certain significance for assessing the vulnerability of the plaque.

Biomechanical changes of the common carotid artery and internal jugular vein in patients with multiple sclerosis

PURPOSE

Investigations of the hemodynamic changes of the venous system in patients with multiple sclerosis (MS) have shown contradictory results. Herein, the biomechanical parameters of the internal jugular vein (IJV) and common carotid artery (CCA) of MS patients were extracted and compared to healthy individuals.

METHODS

B-mode and Doppler sequential ultrasound images of 64 IJVs and CCAs of women including 22 healthy individuals, 22 relapsing-remitting multiple sclerosis (RRMS) patients, and 20 primary-progressive multiple sclerosis (PPMS) patients were recorded and processed. The biomechanical parameters of the IJV and the CCA walls during three cardiac cycles were calculated.

RESULTS

The IJV maximum and minimum pressures were higher in the MS patients than in the healthy subjects, by 31% and 19% in RRMS patients and 39% and 24% in PPMS patients. The venous wall thicknesses in RRMS and PPMS patients were 51% and 60% higher than in healthy subjects, respectively. IJV distensibility in RRMS and PPMS patients was 70% and 75% lower, and compliance was 40% and 59% lower than in healthy subjects. The maximum intima-media thicknesses of the CCAs were 38% and 24%, and the minimum intima-media thicknesses were 27% and 23% higher in RRMS and PPMS patients than in healthy individuals, respectively. The shear modulus of CCA walls in RRMS and PPMS patients was 17% and 31%, and the radial elastic moduli were 47% and 9% higher than in healthy individuals.

CONCLUSION

Some physical and biomechanical parameters of the CCA and IJV showed significant differences between MS patients and healthy individuals.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.