In natural tissues, the extracellular matrix composition, cell density and physiological properties are often non-homogeneous. Here we describe a model system, in which the distribution of cells throughout tissue engineering scaffolds after perfusion seeding can be influenced by the pore architecture of the scaffold. Two scaffold types, both with gyroid pore architectures, were designed and built by stereolithography: one with isotropic pore size (412 ± 13 μm) and porosity (62 ± 1%), and another with a gradient in pore size (250-500 μm) and porosity (35%-85%). Computational fluid flow modelling showed a uniform distribution of flow velocities and wall shear rates (15-24 s(-1)) for the isotropic architecture, and a gradient in the distribution of flow velocities and wall shear rates (12-38 s(-1)) for the other architecture. The distribution of cells throughout perfusion-seeded scaffolds was visualised by confocal microscopy. The highest densities of cells correlated with regions of the scaffolds where the pores were larger, and the fluid velocities and wall shear rates were the highest. Under the applied perfusion conditions, cell deposition is mainly determined by local wall shear stress, which, in turn, is strongly influenced by the architecture of the pore network of the scaffold.
The left atrial appendage (LAA) is a complex and heterogeneous protruding structure of the left atrium (LA). In atrial fibrillation patients, it is the location where 90% of the thrombi are formed. However, the role of the LAA in thrombus formation is not fully known yet. The main goal of this work is to perform a sensitivity analysis to identify the most relevant LA and LAA morphological parameters in atrial blood flow dynamics. Simulations were run on synthetic ellipsoidal left atria models where different parameters were individually studied: pulmonary veins and mitral valve dimensions; LAA shape; and LA volume. Our computational analysis confirmed the relation between large LAA ostia, low blood flow velocities and thrombus formation. Additionally, we found that pulmonary vein configuration exerted a critical influence on LAA blood flow patterns. These findings contribute to a better understanding of the LAA and to support clinical decisions for atrial fibrillation patients.
Apart from partial or total joint replacement, no surgical procedure is currently available to treat large and deep cartilage defects associated with advanced diseases such as osteoarthritis. In this work, we developed a perfusion bioreactor system to engineer human cartilage grafts in a size with clinical relevance for unicompartmental resurfacing of human knee joints (50mm diameter x 3mm thick). Computational fluid dynamics models were developed to optimize the flow profile when designing the perfusion chamber. Using the developed system, human chondrocytes could be seeded throughout large 50mm diameter scaffolds with a uniform distribution. Following two weeks culture, tissues grown in the bioreactor were viable and homogeneously cartilaginous, with biomechanical properties approaching those of native cartilage. In contrast, tissues generated by conventional manual production procedures were highly inhomogeneous and contained large necrotic regions. The unprecedented engineering of human cartilage tissues in this large scale opens the practical perspective of grafting functional biological substitutes for the clinical treatment for extensive cartilage defects, possibly in combination with surgical or pharmacological therapies to support durability of the implant. Ongoing efforts are aimed at integrating the up-scaled bioreactor-based processes within a fully automated and closed manufacturing system for safe, standardized, and GMP compliant production of large-scale cartilage grafts.2
Cellular responses to chemical cues are at the core of a myriad of fundamental biological processes ranging from embryonic development to cancer metastasis. Most of these biological processes are also influenced by mechanical cues such as the stiffness of the extracellular matrix. How a biological function is influenced by a synergy between chemical concentration and extracellular matrix stiffness is largely unknown, however, because no current strategy enables the integration of both types of cues in a single experiment. Here we present a robust microfluidic device that generates a stable, linear and diffusive chemical gradient over a biocompatible hydrogel with a well-defined stiffness gradient. Device fabrication relies on patterned PSA (Pressure Sensitive Adhesive) stacks that can be implemented with minimal cost and lab equipment. This technique is suitable for long-term observation of cell migration and application of traction force microscopy. We validate our device by testing MDCK cell scattering in response to perpendicular gradients of hepatocyte growth factor (HGF) and substrate stiffness.
Atrial fibrillation (AF) is the most common clinically significant arrhythmia, often severely disrupting cardiac hemodynamics and drastically increasing the risk of thromboembolic events. Around 90% of such intracardiac thrombus formation in AF patients takes place in the left atrial appendage (LAA). Such thrombus have been related to blood stasis, which at the moment, can be only assessed through noisy imaging data from transesophageal echocardiography (TEE) at one single point in space and time, vastly oversimplifying the characterization of the complex 4D nature of blood flow patterns. Alternatively, attempts have been made to relate LAA morphology to the risk of thrombi formation, some studies suggesting reduced risk of thrombosis on chicken-wing morphologies. Nonetheless, such classification of the LAA morphology has been found to be highly inconsistent and subjective, excluding as well, several fundamental morphological parameters such as the ostium size or the pulmonary vein (PV) orientation among others. More recently, computational fluid dynamics (CFD) have been employed on the left atrium (LA), seeking to assess the risk of thrombogenesis more quantitatively. CFD has proven to be an invaluable tool in establishing a mechanistic relation between patient-specific organ morphology and its characteristic hemodynamics. In fact, it has long been implemented in other human tissues, such as the coronary arteries, cerebral aneurysms and the aorta with unparalleled success, enabling early diagnosis and risk assessment of various cardiovascular diseases. Nevertheless, traditional CFD methods are renowned for their large memory requirements and long computing times, which severely hinders its suitability for time-sensitive clinical applications. Hence, this thesis seeks to harness the immense potential of deep learning (DL) by developing a deep neural network (DNN), with the objective of generating a fast and accurate surrogate of CFD, capable of instantaneously evaluating the risk of thrombus formation in the LAA. Already having revolutionized fields such as data processing, it has only recently begun to employ DNNs in high-dimensional, complex dynamical systems such as fluid dynamics. In fact to our knowledge, this study represents the first successful implementation of a DL surrogate of CFD analysis in a structure as complex as the LAA, which had only been previously attempted in the aorta. For that purpose, two DL architectures have been successfully designed and trained, which receive the specific LAA geometry as an input, and accurately predict its corresponding endothelial cell activation potential (ECAP) map, parameter linked to the risk of thrombosis. The first approach, is based on a simple fully-connected feedforward network, while the latter, also embeds unsupervised learning. An statistical shape model (SSM) of the LAA was created to generate the training dataset, encompassing 210 virtual shapes, on which CFD simulations were performed to attain the ground truth ECAP mappings. Once trained, the final D...
According to clinical studies, around one third of patients with atrial fibrillation (AF) will suffer a stroke during their lifetime. Between 70 and 90% of these strokes are caused by thrombus formed in the left atrial appendage. In patients with contraindications to oral anticoagulants, a left atrial appendage occluder (LAAO) is often implanted to prevent blood flow entering in the LAA. A limited range of LAAO devices is available, with different designs and sizes. Together with the heterogeneity of LAA morphology, these factors make LAAO success dependent on clinician's experience. A sub-optimal LAAO implantation can generate thrombi outside the device, eventually leading to stroke if not treated. The aim of this study was to develop clinician-friendly tools based on biophysical models to optimize LAAO device therapies. A web-based 3D interactive virtual implantation platform, so-called VIDAA, was created to select the most appropriate LAAO configurations (type of device, size, landing zone) for a given patient-specific LAA morphology. An initial LAAO configuration is proposed in VIDAA, automatically computed from LAA shape features (centreline, diameters). The most promising LAAO settings and LAA geometries were exported from VIDAA to build volumetric meshes and run Computational Fluid Dynamics (CFD) simulations to assess blood flow patterns after implantation. Risk of thrombus formation was estimated from the simulated hemodynamics with an index combining information from blood flow velocity and complexity. The combination of the VIDAA platform with in silico indices allowed to identify the LAAO configurations associated to a lower risk of thrombus formation; device positioning was key to the creation of regions with turbulent flows after implantation. Our results demonstrate the potential for optimizing LAAO therapy settings during pre-implant planning based on modeling tools and contribute to reduce the risk of thrombus formation after treatment.
Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria.
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