A computational study of artery curvature and endograft oversize influence on seal zone behavior in endovascular aortic repair.

Thoracic endovascular aortic repair (TEVAR) is a minimally invasive procedure involving the placement of an endograft inside the dissection or an aneurysm to direct blood flow and prevent rupture. A significant challenge in endovascular surgery is the geometrical mismatch between the endograft and the artery, which can lead to endoleak formation, a condition where blood leaks between the endograft and the vessel wall. This study uses computational modeling to investigate the effects of artery curvature and endograft oversizing, the selection of an endograft with a larger diameter than the artery, on endoleak creation. Finite element analysis is employed to simulate the deployment of endografts in arteries with varying curvature and diameter. Numerical simulations are conducted to assess the seal zone and to quantify the potential endoleak volume as a function of curvature and oversizing. A theoretical framework is developed to explain the mechanisms of endoleak formation along with proof-of-concept experiments. Two main mechanisms of endoleak creation are identified: local buckling due to diameter mismatch and global buckling due to centerline curvature mismatch. Local buckling, characterized by excess graft material buckling and wrinkle formation, increases with higher levels of oversizing, leading to a larger potential endoleak volume. Global buckling, where the endograft bends or deforms to conform to the centerline curvature of the artery, is observed to require a certain degree of oversizing to bridge the curvature mismatch. This study highlights the importance of considering both curvature and diameter mismatch in the design and clinical use of endografts. Understanding the mechanisms of endoleak formation can provide valuable insights for optimizing endograft design and surgical planning, leading to improved clinical outcomes in endovascular aortic procedures.

Predicting Young's Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning.

Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Using a data set of 63 industrially relevant and complex elastomers, we demonstrate that accurate machine learning predictions are possible when monomer chemistry is used to estimate interactions at interchain length scales. Here, these features were used to accurately (r2 = 0.91) predict the Young's modulus of polyurethane and polyurethane-urea elastomers. Furthermore, by a query of the trained model for compositions that yield a target modulus within the range of accessible values, the capabilities of using this methodology as a design tool are demonstrated. The presented methodology could become increasingly useful in building models for materials with small data sets and may guide the interpretation of the underlying physicochemical forces.

Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning.

The glass transition temperature (Tg) is a fundamental property of polymers that strongly influences both mechanical and flow characteristics of the material. In many important polymers, configurational entropy of side chains is a dominant factor determining it. In contrast, the thermal transition in polyurethanes is thought to be determined by a combination of steric and electronic factors from the dispersed hard segments within the soft segment medium. Here, we present a machine learning model for the Tg in linear polyurethanes and aim to uncover the underlying physicochemical parameters that determine this. The model was trained on literature data from 43 industrially relevant combinations of polyols and isocyanates using descriptors derived from quantum chemistry, cheminformatics, and solution thermodynamics forming the feature space. Random forest and regularized regression were then compared to build a sparse linear model from six descriptors. Consistent with empirical understanding of polyurethane chemistry, this study indicates the characteristics of isocyanate monomers strongly determine the increase in Tg. Accurate predictions of Tg from the model are demonstrated, and the significance of the features is discussed. The results suggest that the tools of machine learning can provide both physical insights as well as accurate predictions of complex material properties.

Dynamic Luminal Topography: A Potential Strategy to Prevent Vascular Graft Thrombosis.

AIM: Biologic interfaces play important roles in tissue function. The vascular lumen-blood interface represents a surface where dynamic interactions between the endothelium and circulating blood cells are critical in preventing thrombosis. The arterial lumen possesses a uniform wrinkled surface determined by the underlying internal elastic lamina. The function of this structure is not known, but computational analyses of artificial surfaces with dynamic topography, oscillating between smooth and wrinkled configurations, support the ability of this surface structure to shed adherent material (Genzer and Groenewold, 2006; Bixler and Bhushan, 2012; Li et al., 2014). We hypothesized that incorporating a luminal surface capable of cyclical wrinkling/flattening during the cardiac cycle into vascular graft technology may represent a novel mechanism of resisting platelet adhesion and thrombosis. METHODS AND RESULTS: Bilayer silicone grafts possessing luminal corrugations that cyclically wrinkle and flatten during pulsatile flow were fabricated based on material strain mismatch. When placed into a pulsatile flow circuit with activated platelets, these grafts exhibited significantly reduced platelet deposition compared to grafts with smooth luminal surfaces. Constrained wrinkled grafts with static topography during pulsatile flow were more susceptible to platelet accumulation than dynamic wrinkled grafts and behaved similar to the smooth grafts under pulsatile flow. Wrinkled grafts under continuous flow conditions also exhibited marked increases in platelet accumulation. CONCLUSION: These findings provide evidence that grafts with dynamic luminal topography resist platelet accumulation and support the application of this structure in vascular graft technology to improve the performance of prosthetic grafts. They also suggest that this corrugated structure in arteries may represent an inherent, self-cleaning mechanism in the vasculature.