Biology subjects

Neidlin, M.

Publications and source records attributed to Neidlin, M..

7 recordsLinked to original sources

bioRxiv2024

Investigation of hemodynamic bulk flow patterns caused by aortic stenosis using a combined 4D Flow MRI-CFD framework

Aortic stenosis (AS) leads to alterations of supra-valvular flow patterns. These patterns might lead to, inter alia, increased damage of red blood cell (RBC) membranes. We investigated these patient specific patterns of a severe AS patient and their reversal in healthy flow through a 4D Flow MRI-based CFD methodology. Computational models of subject-specific aortic geometries were created using in-vivo medical imaging data. Temporally and spatially resolved boundary conditions derived from 4D Flow MRI were implemented for an AS patient and a healthy subject. After validation of the in-silico results with in-vivo data, a healthy inflow profile was set for the AS patient in the CFD model. Pathological versus healthy flow fields were compared regarding their blood flow characteristics, i.e. shear stresses on RBCs and helicity. The accuracy of the 4D Flow MRI-based CFD model was proven with excellent agreement between in-vivo and in-silico velocity fields and R{superscript 2} = 0.9. A pathological high shear stress region in the bulk flow was present during late systole with an increase of 125 % compared to both healthy flow. The physiological bihelical structure with predominantly right-handed helices vanished for the pathological state. Instead, a left-handed helix appeared, accompanied by an overall increase in turbulent kinetic energy in areas of accumulated left-handed helicity. The validated 4D Flow MRI-based CFD model identified marked differences between AS and healthy flow. It suggests that altered turbulent and helical structures in the bulk flow are the cause for increased, potentially damaging forces acting upon RBCs in AS. Author SummaryAortic stenosis (AS) is a condition that alters the flow of blood through the aorta, potentially increasing damage to red blood cells (RBCs). It is therefore crucial to understand the precise nature of these changes, especially in the bulk region where the RBCs are mainly circulating. To investigate this, we developed a computational model based on patient-specific 4D Flow MRI data to simulate blood flow in a patient with severe AS and a healthy individual. Our high-fidelity models show high agreement with real-world data. We then imposed synthetically generated healthy flow conditions in the AS patient to eliminate age- and geometry-related uncertainties. Our results revealed significantly higher shear stresses and changes in helical flow patterns in the AS patient compared to healthy flow, suggesting that these alterations could increase the risk of RBC membrane damage. These findings underscore the importance of deepening our understanding about the interplay between bulk flow alterations and their impact on RBCs, as previous studies were mostly focused on near wall regions. This methodology could also be extended to other cardiovascular conditions, aiding in the development of patient-specific treatment strategies.

bioengineering↗

bioRxiv2024

Generating virtual patient data for in-silico clinical trials of medical devices during extracorporeal membrane oxygenationtreatment

This study introduces a virtual patient generation model as online tool through the generation of high-quality synthetic data, addressing challenges like privacy cocerns and limited dataset sizes. Using a Conditional Tabular Generative Adversarial Network (CTGAN), we generated synthetic data from the Electronic Health Records (EHR) of 767 veno-venous extracorporeal membrane oxygenation (ECMO) patients, focusing on 55 critical therapy parameters. Rigorous preprocessing, imputation, and model tuning ensured that the synthetic data closely mirrored real patient records, achieving a 86.6% coverage score and minimal deviations in data correlations. The tool is integrated into a web platform - https://cve-sim.de/ecmo-vpg, allowing researchers to generate and visualize virtual patient cohorts, potentially enhancing ECMO research and reducing the need for extensive clinical trials.

bioinformatics↗

bioRxiv2022

Generic Framework for Quantifying the Influenceof the Mitral Valve on Ventricular Blood Flow

Blood flow within the left ventricle provides important information regarding cardiac function in health and disease. The mitral valve strongly influences the formation of flow structures and there exist various approaches for the representation of the valve in numerical models of left ventricular blood flow. However, a systematic comparison of the various mitral valve models is missing, making a priori decisions considering the overall models context of use impossible. Within this study, a benchmark setup to compare the influence of mitral valve modeling strategies on intraventricular flow features was developed. Then, five mitral valve models of increasing complexity: no modeling, static wall, 2D and 3D porous medium with time-dependent porosity, and one-way Fluid-Structure-Interaction (FSI) were compared with each other. The flow features velocity, kinetic energy, transmitral pressure drop, vortex formation, flow asymmetry as well as computational cost and ease-of-implementation were evaluated. The one-way FSI approach provides the highest level of flow detail, which is accompanied by the highest numerical costs and challenges with the implementation. As an alternative, the porous medium approach with our new expansion including time-dependent porosity provides good results with up to 10% deviations in the flow features (except the transmitral pressure drop) in comparison to the FSI model and only a fraction (11%) of numerical costs. Taken together, our benchmark setup allows a quantitative comparison of various mitral valve modeling approaches and is provided to the scientific community for further testing and expansion..

biophysics↗

bioRxiv2021

An accelerated thrombosis model for computational fluid dynamics simulations in rotary blood pumps

PurposeThrombosis is one of the major complications in blood-carrying medical devices and a better understanding to influence design of such devices is desirable. Over the past years many computational models of thrombosis have been developed. However, open questions remain about the applicability and implementation within a pump development process. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. MethodsWe used a two-stage approach to calculate thrombus risk. At the first stage, the velocity and pressure fields were computed by computational fluid dynamic (CFD) simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was implemented in ANSYS CFX and compared with existing clinical data on thrombus deposition within the HeartMate II. ResultsOur model shows good correlation (R2>0.94) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of platelet activation requires an additional 10-20 core hours of computation time. DiscussionThe concentration of activated platelets can be used as a surrogate marker to determine risk regions of thrombus deposition in a blood pump. Model expansion, e.g. by including more chemical species can easily be performed. We make our model openly available by implementing it for the FDA benchmark blood pump. DeclarationsO_ST_ABSFundingC_ST_ABSThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Open access funding enabled and organized by Projekt DEAL. Conflict of interestAll of the authors have nothing to disclose. Availability of data and materialThe raw data can be retrieved by request from the authors. Code availabilityThe implementation of the thrombus model in the FDA benchmark blood pump geometry is available on https://doi.org/10.5281/zenodo.5116063. Authors contributionsAll authors contributed to the study conception and design. CB developed the numerical model, performed the simulations, gathered, analysed and discussed the results. SGH, MN and US were involved in the analysis and discussion of the results. MN supervised the project. MN and CB wrote the manuscript based on the input of all co-authors. All co-authors read and approved the final version of the manuscript.

bioengineering↗

bioRxiv2020

Intraventricular hemodynamics in pediatric patients with single right ventricles reveal deteriorated washout and low vortex for-mation times: An in silico study

The congenital heart disease univentricular heart (UVH) occurs with an incidence of 0.04-0.5% in newborns and is often treated with the Fontan procedure. In this intervention, the cardiac circulation is transformed into a singular circulation with only one ventricular chamber pumping. Hemodynamics the singular ventricle is a major research topic in cardiology and there exists a relationship between fluid dynamical features and cardiac behavior in health and disease. By visualizing the flow using Computational Fluid Dynamics (CFD) models, an option is created to investigate the flow in patient-specific geometries. CFD simulation of the pathological single right ventricle in contrast to the healthy left ventricle is the research object of the present work. The aim is the numerical comparison of the intraventricular flow within the ventricles. Based on this, flow formation in different anatomies of the ventricles is investigated. Patient-specific measurements of ventricles from three-dimensional real-time echocardiographic images served as the basis for the simulations with five single right ventricle (SRV) patients and two subjects with healthy left hearts (LV) investigated. Interpolation of these data reproduced the shape and continuous motion of the heart during a cardiac cycle. This motion was implemented into a CFD model with a moving mesh methodology. For comparison of the ventricles, the vortex formation as well as the occurring turbulent kinetic energy (TKE) and washout were evaluated. Vortex formation was assessed using the dimensionless vortex formation time (VFT). The results show significantly lower values for the VFT and the TKE in SRV patients than for the compared LV Patients. Furthermore, vortex formation does not progress to the apex in SRV patients. These findings were confirmed by a significantly lower washout in SRV patients. Flow simulation within the moving ventricle provides the possibility of more detailed analysis of the ventricular function. Simulation results show altered vortex formation and reduced washout of SRV in comparison to healthy LV. This information could provide important information for the planning and treatment of Fontan patients.

bioengineering↗

bioRxiv2019

Multi-tissue network analysis for drug prioritization in knee osteoarthritis

Knee osteoarthritis (OA) is a joint disease that affects several tissues: cartilage, synovium, meniscus and subchondral bone. The pathophysiology of this complex disease is still not completely understood and existing pharmaceutical strategies are limited to pain relief treatments.\n\nTherefore, a computational method was developed considering the diverse mechanisms and the multi-tissue nature of OA in order to suggest pharmaceutical compounds. Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to identify gene modules that were preserved across four joint tissues. The driver genes of these modules were selected as an input for a network-based drug discovery approach.\n\nWGCNA identified two preserved modules that described functions related to extracellular matrix physiology and immune system responses. Compounds that affected various anti-inflammatory pathways and drugs targeted at coagulation pathways were suggested. 9 out of the top 10 compounds had a proven association with OA and significantly outperformed randomized approaches not including WGCNA. The method presented herein is a viable strategy to identify overlapping molecular mechanisms in multi-tissue diseases such as OA and employ this information for drug discovery and compound prioritization.

systems biology↗

bioRxiv2019

An ex vivo tissue model of cartilage degradation suggests that cartilage state can be determined from secreted key protein patterns

The pathophysiology of osteoarthritis (OA) involves dysregulation of anabolic and catabolic processes associated with a broad panel of cytokines and other secreted proteins and ultimately lead to cartilage degradation. An increased understanding about the interactions of these proteins by means of systematic in vitro analyses may give new ideas regarding pharmaceutical candidates for treatment of OA and related cartilage degradation. Therefore, first an ex vivo tissue model of cartilage degradation was established by culturing full thickness tissue explants with bacterial collagenase II. Then responses of healthy and degrading cartilage were analyzed by measuring protein abundance in tissue supernatant with a 26-multiplex protein profiling assay, after exposing them to a panel of 55 protein stimulations present in synovial joints of OA patients. Multivariate data analysis including exhaustive pairwise variable subset selection was used to identify the most outstanding changes in the measured protein secretions. This revealed that the MMP9 response is outstandingly low in degraded compared to healthy cartilage and that there are several protein pairs like IFNG and MMP9 that can be used for successful discrimination between degraded and healthy samples. Taken together, the results show that the characteristic changes in protein responses discovered seem promising for accurate detection/diagnosis of degrading cartilage in general and OA in particular. More generally the employed ex vivo tissue model seems promising for drug discovery and development projects related to cartilage degradation, for example when trying to uncover the unknown interactions between secreted proteins in healthy and degraded tissues.

bioengineering↗