Mechanical identification of hyperelastic anisotropic properties of mouse carotid arteries

International audienceThe role of mechanics is known to be of primary order in many arterial diseases; however determining mechanical properties of arteries remains a challenge. This paper discusses the identifiability of a Holzapfel-type material model for a mouse carotid artery, using an inverse method based on a finite element model and 3D digital image correlation measurements of the surface strain during an inflation/extension test. Layer-specific mean fiber angles are successfully determined using a five parameter constitutive model, demonstrating good robustness of the identification procedure. Importantly, we show that a model based on a single thick layer is unable to render the biaxial mechanical response of the artery tested here. On the contrary, difficulties related to the identification of a seven parameter constitutive model are evidenced; such a model leads to multiple solutions. Nevertheless, it is shown that an additional mechanical test, different in nature with the previous one, solves this proble

Biomechanics of porcine renal arteries and role of axial stretch.

International audienceIt is known that arteries experience significant axial stretches in vivo. Several authors have shown that the axial force needed to maintain an artery at its in vivo axial stretch does not change with transient cyclical pressurization over normal ranges. However, the axial force phenomenon of arteries has never been explained with microstructural considerations. In this paper we propose a simple biomechanical model to relate the specific axial force phenomenon of arteries to the predicted load-dependent average collagen fiber orientation. It is shown that (a) the model correctly predicts the authors' experimentally measured biaxial behavior of pig renal arteries and (b) the model predictions are in agreement with additional experimental results reported in the literature. Finally, we discuss the implications of the model for collagen fiber orientation and deposition in arteries

Mechanical identification of layer-specific properties of mouse carotid arteries using 3D-DIC and a hyperelastic anisotropic constitutive model

The role of mechanics is known to be of primary order in many arterial diseases; however, determining mechanical properties of arteries remains a challenge. This paper discusses the identifiability of the passive mechanical properties of a mouse carotid artery, taking into account the orientation of collagen fibres in the medial and adventitial layers. On the basis of 3D digital image correlation measurements of the surface strain during an inflation/extension test, an inverse identification method is set up. It involves a 3D finite element mechanical model of the mechanical test and an optimisation algorithm. A two-layer constitutive model derived from the Holzapfel model is used, with five and then seven parameters. The five-parameter model is successfully identified providing layer-specific fibre angles. The seven-parameter model is over parameterised, yet it is shown that additional data from a simple tension test make the identification of refined layer-specific data reliable.Comment: PB-CMBBE-15.pd

Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning

Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. Accurate analysis of diagnostic images, like computed tomographic angiograms (CTAs), is essential for staging and monitoring the progression of atherosclerosis-related conditions, including peripheral arterial disease (PAD). However, manual analysis of CTA images is time-consuming and tedious. To address this limitation, we employed a deep learning model to segment the vascular system in CTA images of PAD patients undergoing femoral endarterectomy surgery and to measure vascular calcification from the left renal artery to the patella. Utilizing proprietary CTA images of 27 patients undergoing femoral endarterectomy surgery provided by Prisma Health Midlands, we developed a Deep Neural Network (DNN) model to first segment the arterial system, starting from the descending aorta to the patella, and second, to provide a metric of arterial calcification. Our designed DNN achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%. Furthermore, our work is the first to present a robust statistical analysis of automated calcification measurement in the lower extremities using deep learning, attaining a Mean Absolute Percentage Error (MAPE) of 9.5% and a correlation coefficient of 0.978 between automated and manual calcification scores. These findings underscore the potential of deep learning techniques as a rapid and accurate tool for medical professionals to assess calcification in the abdominal aorta and its branches above the patella. The developed DNN model and related documentation in this project are available at GitHub page at https://github.com/pip-alireza/DeepCalcScoring.Comment: Published in MDPI Diagnostic journal, the code can be accessed via the GitHub link in the pape

Sparstolonin B Inhibits Pro-Angiogenic Functions and Blocks Cell Cycle Progression in Endothelial Cells

Sparstolonin B (SsnB) is a novel bioactive compound isolated from Sparganium stoloniferum, an herb historically used in Traditional Chinese Medicine as an anti-tumor agent. Angiogenesis, the process of new capillary formation from existing blood vessels, is dysregulated in many pathological disorders, including diabetic retinopathy, tumor growth, and atherosclerosis. In functional assays, SsnB inhibited endothelial cell tube formation (Matrigel method) and cell migration (Transwell method) in a dose-dependent manner. Microarray experiments with human umbilical vein endothelial cells (HUVECs) and human coronary artery endothelial cells (HCAECs) demonstrated differential expression of several hundred genes in response to SsnB exposure (916 and 356 genes, respectively, with fold change ≥2, p\u3c0.05, unpaired t-test). Microarray data from both cell types showed significant overlap, including genes associated with cell proliferation and cell cycle. Flow cytometric cell cycle analysis of HUVECs treated with SsnB showed an increase of cells in the G1 phase and a decrease of cells in the S phase. Cyclin E2 (CCNE2) and Cell division cycle 6 (CDC6) are regulatory proteins that control cell cycle progression through the G1/S checkpoint. Both CCNE2 and CDC6 were downregulated in the microarray data. Real Time quantitative PCR confirmed that gene expression of CCNE2 and CDC6 in HUVECs was downregulated after SsnB exposure, to 64% and 35% of controls, respectively. The data suggest that SsnB may exert its anti-angiogenic properties in part by downregulating CCNE2 and CDC6, halting progression through the G1/S checkpoint. In the chick chorioallantoic membrane (CAM) assay, SsnB caused significant reduction in capillary length and branching number relative to the vehicle control group. Overall, SsnB caused a significant reduction in angiogenesis (ANOVA, p\u3c0.05), demonstrating its ex vivo efficacy

TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning

Pathological alterations in the human vascular system underlie many chronic diseases, such as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the vascular system, such as computed tomographic angiograms (CTAs) is a time-consuming and tedious process. To address this issue, we propose a deep learning model to segment the vascular system in CTA images of patients undergoing surgery for peripheral arterial disease (PAD). Our study focused on accurately segmenting the vascular system (1) from the descending thoracic aorta to the iliac bifurcation and (2) from the descending thoracic aorta to the knees in CTA images using deep learning techniques. Our approach achieved average Dice accuracies of 93.5% and 80.64% in test dataset for (1) and (2), respectively, highlighting its high accuracy and potential clinical utility. These findings demonstrate the use of deep learning techniques as a valuable tool for medical professionals to analyze the health of the vascular system efficiently and accurately. Please visit the GitHub page for this paper at https://github.com/pip-alireza/TransOnet.Comment: Accepted for the 2023 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, US

The role of networks to overcome large-scale challenges in tomography : the non-clinical tomography users research network

Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives