Red blood cells in retinal vascular disorders.

Microvascular circulation plays a vital role in regulating physiological functions, such as vascular resistance, and maintaining organ health. Pathologies such as hypertension, diabetes, or hematologic diseases affect the microcirculation posing a significant risk to human health. The retinal vasculature provides a unique window for non-invasive visualisation of the human circulation in vivo and retinal vascular image analysis has been established to predict the development of both clinical and subclinical cardiovascular, metabolic, renal and retinal disease in epidemiologic studies. Blood viscosity which was otherwise thought to play a negligible role in determining blood flow based on Poiseuille's law up to the 1970s has now been shown to play an equally if not a more important role in controlling microcirculation and quantifying blood flow. Understanding the hemodynamics/rheology of the microcirculation and its changes in diseased states remains a challenging task; this is due to the particulate nature of blood, the mechanical properties of the cells (such as deformability and aggregability) and the complex architecture of the microvasculature. In our review, we have tried to postulate a possible role of red blood cell (RBC) biomechanical properties and laid down future framework for research related to hemorrheological aspects of blood in patients with retinal vascular disorders.

Aortic dissection simulation models for clinical support: fluid-structure interaction vs. rigid wall models.

BACKGROUND: The management and prognosis of aortic dissection (AD) is often challenging and the use of personalised computational models is being explored as a tool to improve clinical outcome. Including vessel wall motion in such simulations can provide more realistic and potentially accurate results, but requires significant additional computational resources, as well as expertise. With clinical translation as the final aim, trade-offs between complexity, speed and accuracy are inevitable. The present study explores whether modelling wall motion is worth the additional expense in the case of AD, by carrying out fluid-structure interaction (FSI) simulations based on a sample patient case. METHODS: Patient-specific anatomical details were extracted from computed tomography images to provide the fluid domain, from which the vessel wall was extrapolated. Two-way fluid-structure interaction simulations were performed, with coupled Windkessel boundary conditions and hyperelastic wall properties. The blood was modelled using the Carreau-Yasuda viscosity model and turbulence was accounted for via a shear stress transport model. A simulation without wall motion (rigid wall) was carried out for comparison purposes. RESULTS: The displacement of the vessel wall was comparable to reports from imaging studies in terms of intimal flap motion and contraction of the true lumen. Analysis of the haemodynamics around the proximal and distal false lumen in the FSI model showed complex flow structures caused by the expansion and contraction of the vessel wall. These flow patterns led to significantly different predictions of wall shear stress, particularly its oscillatory component, which were not captured by the rigid wall model. CONCLUSIONS: Through comparison with imaging data, the results of the present study indicate that the fluid-structure interaction methodology employed herein is appropriate for simulations of aortic dissection. Regions of high wall shear stress were not significantly altered by the wall motion, however, certain collocated regions of low and oscillatory wall shear stress which may be critical for disease progression were only identified in the FSI simulation. We conclude that, if patient-tailored simulations of aortic dissection are to be used as an interventional planning tool, then the additional complexity, expertise and computational expense required to model wall motion is indeed justified.

Partitioning of dense RBC suspensions in single microfluidic bifurcations: role of cell deformability and bifurcation angle.

Red blood cells (RBCs) are a key determinant of human physiology and their behaviour becomes extremely heterogeneous as they navigate in narrow, bifurcating vessels in the microvasculature, affecting local haemodynamics. This is due to partitioning in bifurcations which is dependent on the biomechanical properties of RBCs, especially deformability. We examine the effect of deformability on the haematocrit distributions of dense RBC suspensions flowing in a single, asymmetric Y-shaped bifurcation, experimentally. Human RBC suspensions (healthy and artificially hardened) at 20% haematocrit (Ht) were perfused through the microchannels at different flow ratios between the outlet branches, and negligible inertia, and imaged to infer cell distributions. Notable differences in the shape of the haematocrit distributions were observed between healthy and hardened RBCs near the bifurcation apex. These lead to more asymmetric distributions for healthy RBCs in the daughter and outlet branches with cells accumulating near the inner channel walls, exhibiting distinct hematocrit peaks which are sharper for healthy RBCs. Although the hematocrit distributions differed locally, similar partitioning characteristics were observed for both suspensions. Comparisons with RBC distributions measured in a T-shaped bifurcation showed that the bifurcation angle affects the haematocrit characteristics of the healthy RBCs and not the hardened ones. The extent of RBC partitioning was found similar in both geometries and suspensions. The study highlights the differences between local and global characteristics which impact RBC distribution in more complex, multi-bifurcation networks.

Patient-Specific Haemodynamic Analysis of Virtual Grafting Strategies in Type-B Aortic Dissection: Impact of Compliance Mismatch.

INTRODUCTION: Compliance mismatch between the aortic wall and Dacron Grafts is a clinical problem concerning aortic haemodynamics and morphological degeneration. The aortic stiffness introduced by grafts can lead to an increased left ventricular (LV) afterload. This study quantifies the impact of compliance mismatch by virtually testing different Type-B aortic dissection (TBAD) surgical grafting strategies in patient-specific, compliant computational fluid dynamics (CFD) simulations. MATERIALS AND METHODS: A post-operative case of TBAD was segmented from computed tomography angiography data. Three virtual surgeries were generated using different grafts; two additional cases with compliant grafts were assessed. Compliant CFD simulations were performed using a patient-specific inlet flow rate and three-element Windkessel outlet boundary conditions informed by 2D-Flow MRI data. The wall compliance was calibrated using Cine-MRI images. Pressure, wall shear stress (WSS) indices and energy loss (EL) were computed. RESULTS: Increased aortic stiffness and longer grafts increased aortic pressure and EL. Implementing a compliant graft matching the aortic compliance of the patient reduced the pulse pressure by 11% and EL by 4%. The endothelial cell activation potential (ECAP) differed the most within the aneurysm, where the maximum percentage difference between the reference case and the mid (MDA) and complete (CDA) descending aorta replacements increased by 16% and 20%, respectively. CONCLUSION: This study suggests that by minimising graft length and matching its compliance to the native aorta whilst aligning with surgical requirements, the risk of LV hypertrophy may be reduced. This provides evidence that compliance-matching grafts may enhance patient outcomes.

Modelling lower-limb peripheral arterial disease using clinically available datasets: impact of inflow boundary conditions on hemodynamic indices for restenosis prediction.

BACKGROUND AND OBJECTIVES: The integration of hemodynamic markers as risk factors in restenosis prediction models for lower-limb peripheral arteries is hindered by fragmented clinical datasets. Computed tomography (CT) scans enable vessel geometry reconstruction and can be obtained at different times than the Doppler ultrasound (DUS) images, which provide information on blood flow velocity. Computational fluid dynamics (CFD) simulations allow the computation of near-wall hemodynamic indices, whose accuracy depends on the prescribed inlet boundary condition (BC), derived from the DUS images. This study aims to: (i) investigate the impact of different DUS-derived velocity waveforms on CFD results; (ii) test whether the same vessel areas, subjected to altered hemodynamics, can be detected independently of the applied inlet BC; (iii) suggest suitable DUS images to obtain reliable CFD results. METHODS: CFD simulations were conducted on three patients treated with bypass surgery, using patient-specific DUS-derived inlet BCs recorded at either the same or different time points than the CT scan. The impact of the chosen inflow condition on bypass hemodynamics was assessed in terms of wall shear stress (WSS)-derived quantities. Patient-specific critical thresholds for the hemodynamic indices were applied to identify critical luminal areas and compare the results with a reference obtained with a DUS image acquired in close temporal proximity to the CT scan. RESULTS: The main findings indicate that: (i) DUS-derived inlet velocity waveforms acquired at different time points than the CT scan led to statistically significantly different CFD results (p<0.001); (ii) the same luminal surface areas, exposed to low time-averaged WSS, could be identified independently of the applied inlet BCs; (iii) similar outcomes were observed for the other hemodynamic indices if the prescribed inlet velocity waveform had the same shape and comparable systolic acceleration time to the one recorded in close temporal proximity to the CT scan. CONCLUSIONS: Despite a lack of standardised data collection for diseased lower-limb peripheral arteries, an accurate estimation of luminal areas subjected to altered near-wall hemodynamics is possible independently of the applied inlet BC. This holds if the applied inlet waveform shares some characteristics - derivable from the DUS report - as one matching the acquisition time of the CT scan.

A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries.

Objective: Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programs is hindered by health care system limitations, patients' comorbidities, and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models. Methods: We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles and abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives-clinical and biomechanical engineering-on restenosis and comprising distinct methodologies, predictors, and study designs. We compared predictive models' performance on discrimination and calibration aspects. We reported the performance of models simulating reocclusion progression, evaluated by comparison with clinical images. Results: Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n = 14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n = 4) are employed. The logistic regression models of the biomechanical engineering perspective (n = 2) show enhanced predictive power when hemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n = 2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups. Conclusions: Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the occurrence of restenosis. Risk stratification models with increased predictive power can be potentially built by adopting machine learning techniques and incorporating critical information regarding vessel hemodynamics arising from biomechanical engineering analyses.

Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing.

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics.

Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, haemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data e.g. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations or machine learning based flow predictions that can pave the way for translation of such models to the clinic.

Continuum microhaemodynamics modelling using inverse rheology.

Modelling blood flow in microvascular networks is challenging due to the complex nature of haemorheology. Zero- and one-dimensional approaches cannot reproduce local haemodynamics, and models that consider individual red blood cells (RBCs) are prohibitively computationally expensive. Continuum approaches could provide an efficient solution, but dependence on a large parameter space and scarcity of experimental data for validation has limited their application. We describe a method to assimilate experimental RBC velocity and concentration data into a continuum numerical modelling framework. Imaging data of RBCs were acquired in a sequentially bifurcating microchannel for various flow conditions. RBC concentration distributions were evaluated and mapped into computational fluid dynamics simulations with rheology prescribed by the Quemada model. Predicted velocities were compared to particle image velocimetry data. A subset of cases was used for parameter optimisation, and the resulting model was applied to a wider data set to evaluate model efficacy. The pre-optimised model reduced errors in predicted velocity by 60% compared to assuming a Newtonian fluid, and optimisation further reduced errors by 40%. Asymmetry of RBC velocity and concentration profiles was demonstrated to play a critical role. Excluding asymmetry in the RBC concentration doubled the error, but excluding spatial distributions of shear rate had little effect. This study demonstrates that a continuum model with optimised rheological parameters can reproduce measured velocity if RBC concentration distributions are known a priori. Developing this approach for RBC transport with more network configurations has the potential to provide an efficient approach for modelling network-scale haemodynamics.

Flows of healthy and hardened RBC suspensions through a micropillar array.

Red blood cell (RBC) deformability is an important haemorheological factor; it is impaired in many pathologies leading to microvascular complications. Several microfluidic platforms have been utilized to examine the role of deformability in RBC flows but their geometries tend to be simplified. In the present study, we extend our previous work on healthy RBC flows in micropillar arrays [1] to probe the effect of impaired RBC deformability on the velocity and haematocrit distributions in microscale RBC flows. Healthy and artificially hardened RBC suspensions at 25% haematocrit were perfused through the micropillar array at various flow rates and imaged. RBC velocities were determined by Particle Image Velocimetry (PIV) and haematocrit distributions were inferred from the image intensity distributions. The pillars divide the flow into two distinct RBC streams separated by a cell-depleted region along the centreline and in the rear/front stagnation points. RBC deformability was not found to significantly affect the velocity distributions; the shape of the velocity profiles in the interstitial space remained the same for healthy and hardened RBCs. Time-averaged and spatiotemporal intensity distributions, however, reveal differences in the dynamics and local distributions of healthy and hardened cells; hardened cells appear to enter the cell-depleted regions more frequently and their interstitial distributions are more uniform. The study highlights the importance of local RBC distributions and the impact of RBC deformability on cell transport in complex microscale flows.

Experimental evaluation of the patient-specific haemodynamics of an aortic dissection model using particle image velocimetry.

Aortic Dissection (AD) is a complex pathology that affects the aorta. Diagnosis, management and treatment remain a challenge as it is a highly patient-specific pathology and there is still a limited understanding of the fluid-mechanics phenomena underlying clinical outcomes. Although in vitro models can allow the accurate study of AD flow fields in physical phantoms, they are currently scarce and almost exclusively rely on over simplifying assumptions. In this work, we present the first experimental study of a patient-specific case of AD. An anatomically correct phantom was produced and combined with a state-of-the-art in vitro platform, informed by clinical data, employed to accurately reproduce personalised conditions. The complex AD haemodynamics reproduced by the platform was characterised by flow rate and pressure acquisitions as well as Particle Image Velocimetry (PIV) derived velocity fields. Clinically relevant haemodynamic indices, that can be correlated with AD prognosis - such as velocity, shear rate, turbulent kinetic energy distributions - were extracted in two regions of interest in the aortic domain. The acquired data highlighted the complex nature of the flow (e.g. recirculation regions, low shear rate in the false lumen) and was in very good agreement with the available clinical data and the CFD results of a study conducted alongside, demonstrating the accuracy of the findings. These results demonstrate that the described platform constitutes a powerful, unique tool to reproduce in vitro personalised haemodynamic conditions, which can be used to support the evaluation of surgical procedures, medical devices testing and to validate state-of-the-art numerical models.

A Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations.

PURPOSE: Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections-the so-called nidus-between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus, however the complexity of pAVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs using routinely acquired clinical data. METHODS: A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of a patient-specific pAVM. A porous medium of varying permeability was employed to simulate the sclerosant effect on the nidus haemodynamics. Results were compared against clinical data (digital subtraction angiography, DSA, images) and experimental flow-visualization results in a 3D-printed phantom of the same pAVM. RESULTS: The computational model allowed the simulation of the pAVM haemodynamics and the sclerotherapy-induced changes at different interventional stages. The predicted inlet flow rates closely matched the DSA-derived data, although the post-intervention one was overestimated, probably due to vascular system adaptations not accounted for numerically. The nidus embolization was successfully captured by varying the nidus permeability and increasing its hydraulic resistance from 0.330 to 3970 mmHg s ml-1. The nidus flow rate decreased from 71% of the inlet flow rate pre-intervention to 1%: the flow completely bypassed the nidus post-intervention confirming the success of the procedure. CONCLUSION: The study demonstrates that the haemodynamic effects of the embolisation procedure can be simulated from routinely acquired clinical data via a porous medium with varying permeability as evidenced by the good qualitative agreement between numerical predictions and both in vivo and in vitro data. It provides a fundamental building block towards a computational treatment-planning framework for AVM embolisation.

A novel MRI-based data fusion methodology for efficient, personalised, compliant simulations of aortic haemodynamics.

We present a novel, cost-efficient methodology to simulate aortic haemodynamics in a patient-specific, compliant aorta using an MRI data fusion process. Based on a previously-developed Moving Boundary Method, this technique circumvents the high computational cost and numerous structural modelling assumptions required by traditional Fluid-Structure Interaction techniques. Without the need for Computed Tomography (CT) data, the MRI images required to construct the simulation can be obtained during a single imaging session. Black Blood MR Angiography and 2D Cine-MRI data were used to reconstruct the luminal geometry and calibrate wall movement specifically to each region of the aorta. 4D-Flow MRI and non-invasive pressure measurements informed patient-specific inlet and outlet boundary conditions. Luminal area closely matched 2D Cine-MRI measurements with a mean error of less than 4.6% across the cardiac cycle, while physiological pressure and flow distributions were simulated to within 3.3% of patient-specific targets. Moderate agreement with 4D-Flow MRI velocity data was observed. Despite lower peak velocity, an equivalent rigid-wall simulation predicted a mean Time-Averaged Wall Shear Stress (TAWSS) 13% higher than the compliant simulation. The agreement observed between compliant simulation results and MRI data is testament to the accuracy and efficiency of this MRI-based simulation technique.

Susceptibility of different proteins to flow-induced conformational changes monitored with Raman spectroscopy.

By directly monitoring stirred protein solutions with Raman spectroscopy, the reversible unfolding of proteins caused by fluid shear is examined for several natural proteins with varying structural properties and molecular weight. While complete denaturation is not observed, a wide range of spectral variances occur for the different proteins, indicating subtle conformational changes that appear to be protein-specific. A number of significant overall trends are apparent from the study. For globular proteins, the overall extent of spectral variance increases with protein size and the proportion of beta-structure. For two less structured proteins, fetuin and alpha-casein, the observed changes are of relatively low magnitude, despite the greater molecular structural mobility of these proteins. This implies that other protein-specific factors, such as posttranslational modifications, may also be significant. Individual band changes occurring in the spectral profiles of each individual protein are also discussed in detail.

A Combined In Vivo, In Vitro, In Silico Approach for Patient-Specific Haemodynamic Studies of Aortic Dissection.

The optimal treatment of Type-B aortic dissection (AD) is still a subject of debate, with up to 50% of the cases developing late-term complications requiring invasive intervention. A better understanding of the patient-specific haemodynamic features of AD can provide useful insights on disease progression and support clinical management. In this work, a novel in vitro and in silico framework to perform personalised studies of AD, informed by non-invasive clinical data, is presented. A Type-B AD was investigated in silico using computational fluid dynamics (CFD) and in vitro by means of a state-of-the-art mock circulatory loop and particle image velocimetry (PIV). Both models not only reproduced the anatomical features of the patient, but also imposed physiologically-accurate and personalised boundary conditions. Experimental flow rate and pressure waveforms, as well as detailed velocity fields acquired via PIV, are extensively compared against numerical predictions at different locations in the aorta, showing excellent agreement. This work demonstrates how experimental and numerical tools can be developed in synergy to accurately reproduce patient-specific AD blood flow. The combined platform presented herein constitutes a powerful tool for advanced haemodynamic studies for a range of vascular conditions, allowing not only the validation of CFD models, but also clinical decision support, surgical planning as well as medical device innovation.

Effect of Particle Specific Surface Area on the Rheology of Non-Brownian Silica Suspensions.

Industrial formulations very often involve particles with a broad range of surface characteristics and size distributions. Particle surface asperities (roughness) and porosity increase particle specific surface area and significantly alter suspension rheology, which can be detrimental to the quality of the end product. We examine the rheological properties of two types of non-Brownian, commercial precipitated silicas, with varying specific surface area, namely PS52 and PS226, suspended in a non-aqueous solvent, glycerol, and compare them against those of glass sphere suspensions (GS2) with a similar size distribution. A non-monotonic effect of the specific surface area (S) on suspension rheology is observed, whereby PS52 particles in glycerol are found to exhibit strong shear thinning response, whereas such response is suppressed for glass sphere and PS226 particle suspensions. This behaviour is attributed to the competing mechanisms of particle-particle and particle-solvent interactions. In particular, increasing the specific surface area beyond a certain value results in the repulsive interparticle hydration forces (solvation forces) induced by glycerol overcoming particle frictional contacts and suppressing shear thinning; this is evidenced by the response of the highest specific surface area particles PS226. The study demonstrates the potential of using particle specific surface area as a means to tune the rheology of non-Brownian silica particle suspensions.

Shear-induced unfolding of lysozyme monitored in situ.

Conformational changes due to externally applied physiochemical parameters, including pH, temperature, solvent composition, and mechanical forces, have been extensively reported for numerous proteins. However, investigations on the effect of fluid shear flow on protein conformation remain inconclusive despite its importance not only in the research of protein dynamics but also for biotechnology applications where processes such as pumping, filtration, and mixing may expose protein solutions to changes in protein structure. By combining particle image velocimetry and Raman spectroscopy, we have successfully monitored reversible, shear-induced structural changes of lysozyme in well-characterized flows. Shearing of lysozyme in water altered the protein's backbone structure, whereas similar shear rates in glycerol solution affected the solvent exposure of side-chain residues located toward the exterior of the lysozyme alpha-domain. The results demonstrate the importance of measuring conformational changes in situ and of quantifying fluid stresses by the three-dimensional shear tensor to establish reversible unfolding or misfolding transitions occurring due to flow exposure.

Testosterone Therapy: An Assessment of the Clinical Consequences of Changes in Hematocrit and Blood Flow Characteristics.

INTRODUCTION: Clinical guidelines indicate that hematocrit should be monitored during testosterone replacement therapy (TTh), with action taken if a level of 0.54 is exceeded. AIM: To consider the extent of changes in hematocrit and putative effects on viscosity, blood flow, and mortality rates after TTh. METHODS: We focused on literature describing benefits and possible pitfalls of TTh, including increased hematocrit. We used data from the BLAST RCT to determine change in hematocrit after 30 weeks of TTh and describe a clinical case showing the need for monitoring. We consider the validity of the current hematocrit cutoff value at which TTh may be modified. Ways in which hematocrit alters blood flow in the micro- and macro-vasculature are also considered. MAIN OUTCOME MEASURES: The following measures were assessed: (i) change in hematocrit, (ii) corresponding actions taken in clinical practice, and (iii) possible blood flow changes following change in hematocrit. RESULTS: Analysis of data from the BLAST RCT showed a significant increase in mean hematocrit of 0.01, the increase greater in men with lower baseline values. Although 0 of 61 men given TTh breached the suggested cutoff of 0.54 after 30 weeks, a clinical case demonstrates the need to monitor hematocrit. An association between hematocrit and morbidity and mortality appears likely but not proven and may be evident only in patient subgroups. The consequences of an increased hematocrit may be mediated by alterations in blood viscosity, oxygen delivery, and flow. Their relative impact may vary in different vascular beds. CONCLUSIONS: TTh can effect an increased hematocrit via poorly understood mechanisms and may have harmful effects on blood flow that differ in patient subgroups. At present, there appears no scientific basis for using a hematocrit of 0.54 to modify TTh; other values may be more appropriate in particular patient groups. König CS, Balabani S, Hackett GI, et al. Testosterone Therapy: An Assessment of the Clinical Consequences of Changes in Hematocrit and Blood Flow Characteristics. Sex Med Rev 2019;7:650-660.

Patient-specific haemodynamic simulations of complex aortic dissections informed by commonly available clinical datasets.

Patient-specific computational fluid-dynamics (CFD) can assist the clinical decision-making process for Type-B aortic dissection (AD) by providing detailed information on the complex intra-aortic haemodynamics. This study presents a new approach for the implementation of personalised CFD models using non-invasive, and oftentimes minimal, datasets commonly collected for AD monitoring. An innovative way to account for arterial compliance in rigid-wall simulations using a lumped capacitor is introduced, and a parameter estimation strategy for boundary conditions calibration is proposed. The approach was tested on three complex cases of AD, and the results were successfully compared against invasive blood pressure measurements. Haemodynamic results (e.g. intraluminal pressures, flow partition between the lumina, wall shear-stress based indices) provided information that could not be obtained using imaging alone, providing insight into the state of the disease. It was noted that small tears in the distal intimal flap induce disturbed flow in both lumina. Moreover, oscillatory pressures across the intimal flap were often observed in proximity to the tears in the abdominal region, which could indicate a risk of dynamic obstruction of the true lumen. This study shows how combining commonly available clinical data with computational modelling can be a powerful tool to enhance clinical understanding of AD.

A Preliminary Computational Investigation Into the Flow of PEG in Rat Myocardial Tissue for Regenerative Therapy.

Myocardial infarction (MI), a type of cardiovascular disease, affects a significant proportion of people around the world. Traditionally, non-communicable chronic diseases were largely associated with aging populations in higher income countries. It is now evident that low- to middle-income countries are also affected and in these settings, younger individuals are at high risk. Currently, interventions for MI prolong the time to heart failure. Regenerative medicine and stem cell therapy have the potential to mitigate the effects of MI and to significantly improve the quality of life for patients. The main drawback with these therapies is that many of the injected cells are lost due to the vigorous motion of the heart. Great effort has been directed toward the development of scaffolds which can be injected alongside stem cells, in an attempt to improve retention and cell engraftment. In some cases, the scaffold alone has been seen to improve heart function. This study focuses on a synthetic polyethylene glycol (PEG) based hydrogel which is injected into the heart to improve left ventricular function following MI. Many studies in literature characterize PEG as a Newtonian fluid within a specified shear rate range, on the macroscale. The aim of the study is to characterize the flow of a 20 kDa PEG on the microscale, where the behavior is likely to deviate from macroscale flow patterns. Micro particle image velocimetry (µPIV) is used to observe flow behavior in microchannels, representing the gaps in myocardial tissue. The fluid exhibits non-Newtonian, shear-thinning behavior at this scale. Idealized two-dimensional computational fluid dynamics (CFD) models of PEG flow in microchannels are then developed and validated using the µPIV study. The validated computational model is applied to a realistic, microscopy-derived myocardial tissue model. From the realistic tissue reconstruction, it is evident that the myocardial flow region plays an important role in the distribution of PEG, and therefore, in the retention of material.