Biology subjects

Olivares, A. L.

Publications and source records attributed to Olivares, A. L..

3 recordsLinked to original sources

bioRxiv2024

The influence of scaffold deformation and fluid mechanical stimuli on bone tissue differentiation.

Bone tissue engineering enables the self-healing of bone fractures avoiding the graft surgery risks. Scaffolds are designed to transfer global mechanical load to cells, and the structure-blood flow interaction is crucial for cell differentiation, proliferation, and migration. Numerical models often consider the effect of solid on the fluid or vice-versa, nevertheless, fluid-structure interactions (FSI) are not usually explored. The present study aims to develop in-silico FSI models to evaluate tissue differentiation capability of different scaffold designs. This is accomplished by analyzing the relation between scaffold strain deformation and fluid mechanical stimuli developed at the cell microscopic level. Cubic regular structures with cylinder and sphere pore based of 60%, 70% and 80% porosity were modelled in finite element analysis. Static or dynamic compression and inlet steady state or transient state fluid profile were considered. Fluid-structure interactions have been performed, and cell differentiation studies considering the octahedral shear strain and fluid shear stress have been compared. Results indicate that high porous scaffold with low compression and fluid perfusion rates promote bone tissue proliferation. Moreover, mechanical stimulation seems to help bone formation and to inhibit cartilage phenotype. Results showed that neglecting the interaction between the scaffold and fluid flow could lead to substantial overestimation of bone differentiation. This study enhances our understanding of the role of dynamic mechanical simulations in tissue formation; allowing the improvement of scaffold design to face complex bone fractures.

bioengineering↗

bioRxiv2024

Inference of alveolar capillary network connectivity from blood flow dynamics

The intricate structure of the lungs is essential for the gas exchange within the alveolar region. Despite extensive research on the pulmonary vasculature, there are still unresolved questions regarding the connection between capillaries and the vascular tree. A major challenge is obtaining comprehensive experimental data that integrates morphological and physiological aspects. We propose a computational approach that combines data-driven 3D morphological modeling with computational fluid dynamics simulations. This method enables investigating the connectivity of the alveolar capillary network with the vascular tree based on the dynamics of blood flow. We developed 3D sheet-flow models to accurately represent the morphology of the alveolar capillary network and conducted computational fluid dynamics simulations to predict flow velocities and pressure distributions. Our approach focuses on leveraging functional features to identify the most plausible architecture of the system. For given capillary flow velocities and arteriole-to-venule pressure drops, we deduce details about arteriole connectivity. Preliminary connectivity analyses for non-human species indicate that their alveolar capillary network of a single alveolus is linked to at least two arterioles with diameters of 20 {micro}m or a single arteriole with a minimum diameter of 30 {micro}m. Our study provides insights into the structure of the pulmonary microvasculature by evaluating blood flow dynamics. This inverse approach represents a new strategy to exploit the intricate relationship between morphology and physiology, applicable to other tissues and organs. In the future, the availability of experimental data will play a pivotal role in validating and refining the hypotheses analyzed with our computational models. New and noteworthyThe alveolus is pivotal for gas exchange. Due to its complex morphology and dynamic nature, structural experimental studies are challenging. Computational modeling offers an alternative. We developed a databased 3D model of the alveolar capillary network and performed blood flow simulations within it. Choosing a novel perspective, we inferred structure from function. We systematically varied properties of vessels connected to our capillary network and compared simulation results with experimental data to obtain plausible vessel configurations.

physiology↗

bioRxiv2024

Impact of occluder device configurations in in-silico left atrial hemodynamics for the analysis of device-related thrombus

Left atrial appendage occlusion devices (LAAO) are a feasible alternative for non-valvular atrial fibrillation (AF) patients at high risk of thromboembolic stroke and contraindication to antithrombotic therapies. However, optimal LAAO device configurations (i.e., size, type, location) remain unstandardized due to the large anatomical variability of the left atrial appendage (LAA) morphology, leading to a 4-6% incidence of device-related thrombus (DRT). In-silico simulations have the potential to assess DRT risk and identify the key factors, such as suboptimal device positioning. This work presents fluid simulation results computed on 20 patient-specific left atrial geometries, analysing different commercially available LAAO occluders, including plug-type and pacifier-type devices. In addition, we explored two distinct device positions: 1) the real post-LAAO intervention configuration derived from follow-up imaging; and 2) one covering the pulmonary ridge if it was not achieved during the implantation (13 out of 20). In total, 33 different configurations were analysed. In-silico indices indicating high risk of DRT (e.g., low blood flow velocities and flow complexity around the device) were combined with particle deposition analysis based on a discrete phase model. The obtained results revealed that covering the pulmonary ridge with the LAAO device may be one of the key factors to prevent DRT. Moreover, disk-based devices exhibited enhanced adaptability to various LAA morphologies and, generally, demonstrated a lower risk of abnormal events after LAAO implantation.

bioengineering↗