Patient-specific multi-physics simulations of fibrotic changes in left atrial tissue mechanics impact on hemodynamics.

Stroke is a leading cause of death and disability worldwide. Atrial myopathy, including fibrosis, is associated with an increased risk of ischemic stroke, but the mechanisms underlying this association are poorly understood. Fibrosis modifies myocardial structure, impairing electrical propagation and tissue biomechanics, and creating stagnant flow regions where clots could form. Fibrosis can be mapped non-invasively using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). However, fibrosis maps are not currently incorporated into stroke risk calculations or computational electro-mechano-fluidic models. We present multi-physics simulations of left atrial (LA) myocardial motion and hemodynamics using patient-specific anatomies and fibrotic maps from LGE-MRI. We modify tissue stiffness and active tension generation in fibrotic regions and investigate how these changes affect LA flow for different fibrotic burdens. We find that fibrotic regions and, to a lesser extent, non-fibrotic regions experience reduced myocardial strain, resulting in decreased LA emptying fraction consistent with clinical observations. Both fibrotic tissue stiffening and hypocontractility independently reduce LA function, but together, these two alterations cause more pronounced effects than either one alone. Fibrosis significantly alters flow patterns throughout the atrial chamber, and particularly, the filling and emptying jets of the left atrial appendage (LAA). The effects of fibrosis in LA flow are largely captured by the concomitant changes in LA emptying fraction except inside the LAA, where a multi-factorial behavior is observed. This work illustrates how high-fidelity, multi-physics models can be used to study thrombogenesis mechanisms in a patient-specific manner, shedding light onto the link between atrial fibrosis and ischemic stroke. Key points: Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improvedfib by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging.Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens.Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

Cardiac Stasis Imaging, Stroke and Silent Brain Infarcts in Patients with Non-Ischemic Dilated Cardiomyopathy.

Cardioembolic stroke is one of the most devastating complications of non-ischemic dilated cardiomyopathy (NIDCM). However, in clinical trials of primary prevention, the benefits of anticoagulation are hampered by the risk of bleeding. Indices of cardiac blood stasis may account for the risk of stroke and be useful to individualize primary prevention treatments. We performed a cross-sectional study in patients with NIDCM and no history of atrial fibrillation (AF) from two sources: 1) a prospective enrollment of unselected patients with left ventricular (LV) ejection fraction <45% and 2) a retrospective identification of patients with a history of previous cardioembolic neurological event. The primary endpoint integrated a history of ischemic stroke or the presence intraventricular thrombus, or a silent brain infarction (SBI) by imaging. From echocardiography, we calculated blood flow inside the LV, its residence time (RT) maps and its derived stasis indices. Of the 89 recruited patients, 18 showed a positive endpoint: 9 had a history stroke or TIA and 9 were diagnosed with SBIs in the brain imaging. Averaged RT, performed good to identify the primary endpoint (AUC (95% CI)= 0.75 (0.61-0.89), p= 0.001). When accounting only for identifying a history of stroke or TIA, AUC for was 0.92 (0.85-1.00) with and odds ratio= 7.2 (2.3 - 22.3) per cycle, p< 0.001. These results suggest that, in patients with NIDCM in sinus rhythm, stasis imaging derived from echocardiography may account for the burden of stroke.

Stasis imaging predicts the risk of cardioembolic events related to acute myocardial infarction: the ISBITAMI study.

INTRODUCTION AND OBJECTIVES: In the setting of ST-segment elevation myocardial infarction (STEMI), imaging-based biomarkers could be useful for guiding oral anticoagulation to prevent cardioembolism. Our objective was to test the efficacy of intraventricular blood stasis imaging for predicting a composite primary endpoint of cardioembolic risk during the first 6 months after STEMI. METHODS: We designed a prospective clinical study, Imaging Silent Brain Infarct in Acute Myocardial Infarction (ISBITAMI), including patients with a first STEMI, an ejection fraction ≤ 45% and without atrial fibrillation to assess the performance of stasis metrics to predict cardioembolism. Patients underwent ultrasound-based stasis imaging at enrollment followed by heart and brain magnetic resonance at 1-week and 6-month visits. From the stasis maps, we calculated the average residence time, RT, of blood inside the left ventricle and assessed its performance to predict the primary endpoint. The longitudinal strain of the 4 apical segments was quantified by speckle tracking. RESULTS: A total of 66 patients were assigned to the primary endpoint. Of them, 17 patients had 1 or more events: 3 strokes, 5 silent brain infarctions, and 13 mural thromboses. No systemic embolisms were observed. RT (OR, 3.73; 95%CI, 1.75-7.9; P<.001) and apical strain (OR, 1.47; 95%CI, 1.13-1.92; P=.004) showed complementary prognostic value. The bivariate model showed a c-index=0.86 (95%CI, 0.73-0.95), a negative predictive value of 1.00 (95%CI, 0.94-1.00), and positive predictive value of 0.45 (95%CI, 0.37-0.77). The results were confirmed in a multiple imputation sensitivity analysis. Conventional ultrasound-based metrics were of limited predictive value. CONCLUSIONS: In patients with STEMI and left ventricular systolic dysfunction in sinus rhythm, the risk of cardioembolism may be assessed by echocardiography by combining stasis and strain imaging. Registered at ClinicalTrials.gov (NCT02917213).

Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.

Intraventricular vector flow mapping (VFM) is a growingly adopted echocardiographic modality that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the pressure and shear forces within the fluid and the resulting accelerations. This limitation has rendered VFM unable to combine information from different time frames in an acquisition sequence or derive fluctuating pressure maps. In this study, we leveraged recent advances in artificial intelligence (AI) to develop AI-VFM, a vector flow mapping modality that uses physics-informed neural networks (PINNs) encoding mass conservation and momentum balance inside the LV, and no-slip boundary conditions at the LV endocardium. AI-VFM recovers the flow and pressure fields in the LV from standard echocardiographic scans. It performs phase unwrapping and recovers flow data in areas without input color-Doppler data. AI-VFM also recovers complete flow maps at time points without color-Doppler input data, producing super-resolution flow maps. We show that informing the PINNs with momentum balance is essential to achieving temporal super-resolution and significantly increases the accuracy of AI-VFM compared to informing the PINNs only with mass conservation. AI-VFM is solely informed by each patient's flow physics; it does not utilize explicit smoothness constraints or incorporate data from other patients or flow models. AI-VFM takes 15 minutes to run in off-the-shelf graphics processing units and its underlying PINN framework could be extended to map other flow-associated metrics like blood residence time or the concentration of coagulation species.

Urbanization correlates with the prevalence and richness of blood parasites in Eurasian Blackbirds (Turdus merula).

Urbanization is increasing worldwide, producing severe environmental impacts. Biodiversity is affected by the expansion of cities, with many species being unable to cope with the different human-induced stressors present in these landscapes. However, this knowledge is mainly based on research from taxa such as plants or vertebrates, while other organisms like protozoa have been less studied in this context. The impact of urbanization on the transmission of vector-borne pathogens in wildlife is still unclear despite its relevance for animal and human health. Here, we investigated whether cities are associated with changes in the prevalence and richness of lineages of three vector-borne protozoans (Plasmodium, Haemoproteus and Leucocytozoon) in Eurasian blackbirds (Turdus merula) from multiple urban and forest areas in Europe. Our results show important species-specific differences between these two habitat types. We found a significant lower prevalence of Leucocytozoon in urban birds compared to forest birds, but no differences for Plasmodium and Haemoproteus. Furthermore, the richness of parasite lineages in European cities was higher for Plasmodium but lower for Leucocytozoon than in forests. We also found one Plasmodium lineage exclusively from cities while another of Leucocytozoon was only found in forests suggesting a certain level of habitat specialization for these protozoan vectors. Overall, our findings show that cities provide contrasting opportunities for the transmission of different vector-borne pathogens and generate new scenarios for the interactions between hosts, vectors and parasites.

Dense city centers support less evolutionary unique bird communities than sparser urban areas.

Urbanization alters avian communities, generally lowering the number of species and contemporaneously increasing their functional relatedness, leading to biotic homogenization. Urbanization can also negatively affect the phylogenetic diversity of species assemblages, potentially decreasing their evolutionary distinctiveness. We compare species assemblages in a gradient of building density in seventeen European cities to test whether the evolutionary distinctiveness of communities is shaped by the degree of urbanization. We found a significant decline in the evolutionary uniqueness of avian communities in highly dense urban areas, compared to low and medium-dense areas. Overall, communities from dense city centers supported one million years of evolutionary history less than communities from low-dense urban areas. Such evolutionary homogenization was due to a filtering process of the most evolutionarily unique birds. Metrics related to evolutionary uniqueness have to play a role when assessing the effects of urbanization and can be used to identify local conservation priorities.

Biomechanics of parasite migration within hosts.

The dissemination of protozoan and metazoan parasites through host tissues is hindered by cellular barriers, dense extracellular matrices, and fluid forces in the bloodstream. To overcome these diverse biophysical impediments, parasites implement versatile migratory strategies. Parasite-exerted mechanical forces and upregulation of the host's cellular contractile machinery are the motors for these strategies, and these are comparably better characterized for protozoa than for helminths. Using the examples of the protozoans, Toxoplasma gondii and Plasmodium, and the metazoan, Schistosoma mansoni, we highlight how quantitative tools such as traction force and reflection interference contrast microscopies have improved our understanding of how parasites alter host mechanobiology to promote their migration.

Reduced-order models of wall shear stress patterns in the left atrial appendage from a data-augmented atrial database.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA) and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized. AIM: To develop reduced-order data-driven models of LAA flow patterns during atrial remodeling in order to uncover flow disturbances concurrent with LAA stasis that could add granularity to clinical decision criteria. METHODS: We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on a universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF anatomies. RESULTS: We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns for atrial geometries that underwent adverse atrial remodeling and experienced elevated blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could facilitate data-driven analyses to identify flow features associated with thrombosis risk due to atrial remodeling.

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification.

Background: Extracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. Methods: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. Results: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. Conclusions: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Personalized biomechanical insights in atrial fibrillation: opportunities & challenges.

INTRODUCTION: Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke. AREAS COVERED: In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care. EXPERT OPINION: Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.

People with atrial fibrillation (AF) experience a fast, chaotic heartbeat. AF greatly increases the risk of stroke. The hearts of AF patients often have an accumulation of fibrous tissue (fibrosis). Fibrosis patterns can be detected via medical imaging scans, like MRI. These images can be used to build patient-specific digital representations. These models can be used to explore how fibrosis might cause AF, stroke, and other health risks. Insights from imaging and modeling are becoming more and more useful as tools for personalizing AF treatment.

Efficient multi-fidelity computation of blood coagulation under flow.

Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species' transport, reaction kinetics, and diffusion. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, [Formula: see text], and provide the governing PDEs for [Formula: see text]. This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order (p) allows balancing accuracy and computational cost providing a speedup of over N/p compared to high-fidelity models. Moreover, this cost becomes independent of the number of chemical species in the large computational meshes typical of the arterial and cardiac chamber simulations. Using a coagulation network with N = 9 and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. The thrombin concentration in these models departs from the high-fidelity solution by under 20% (p = 1) and 2% (p = 2) after 20 cardiac cycles. These multi-fidelity models could enable new coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it could be generalized to advance our understanding of other reacting systems affected by flow.

Clinical Trials on Advanced Therapy Investigational Medicinal Products in Spain (2004-2022): Experience and Challenges for the Future.

Clinical investigation is the basis for establishing how useful advanced therapy investigational medicinal products (ATiMP) are for the treatment of serious diseases.In Spain, clinical trials (CT) on ATiMP need to follow the general European legislation on CT with medicinal products plus some specific legislation and guidance depending on the type of ATiMP.This chapter describes the characteristics of CT on ATiMP authorized in Spain in the period 2004-2022 and the legislation applicable along this period. There are clear differences in the clinical trials conducted by non commercial and commercial sponsors: the first have been more involved in CT on somatic cell therapy medicinal products (sCTMP) and tissue-engineered products (TEP), while the second drive more the CT on gene therapy medicinal products (GTMP) in the last years. Difficulties of budget and resources especially by non-commercial sponsors to meet the regulatory requirements are highlighted. The importance of complying with transparency rules with respect to CT on ATiMP is also discussed.

Benchtop Models of Patient-Specific Intraventricular Flow During Heart Failure and LVAD Support.

The characterization of intraventricular flow is critical to evaluate the efficiency of fluid transport and potential thromboembolic risk but challenging to measure directly in advanced heart failure (HF) patients with left ventricular assist device (LVAD) support. The study aims to validate an in-house mock loop (ML) by simulating specific conditions of HF patients with normal and prosthetic mitral valves (MV) and LVAD patients with small and dilated left ventricle volumes, then comparing the flow-related indices result of vortex parameters, residence time (RT), and shear-activation potential (SAP). Patient-specific inputs for the ML studies included heart rate, end-diastolic and end-systolic volumes, ejection fraction, aortic pressure, E/A ratio, and LVAD speed. The ML effectively replicated vortex development and circulation patterns, as well as RT, particularly for HF patient cases. The LVAD velocity fields reflected altered flow paths, in which all or most incoming blood formed a dominant stream directing flow straight from the mitral valve to the apex. RT estimation of patient and ML compared well for all conditions, but SAP was substantially higher in the LVAD cases of the ML. The benchtop system generated comparable and reproducible hemodynamics and fluid dynamics for patient-specific conditions, validating its reliability and clinical relevance. This study demonstrated that ML is a suitable platform to investigate the fluid dynamics of HF and LVAD patients and can be utilized to investigate heart-implant interactions.

Machine Learning and the Conundrum of Stroke Risk Prediction.

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Why do some bird species incorporate more anthropogenic materials into their nests than others?

Many bird species incorporate anthropogenic materials (e.g. sweet wrappers, cigarette butts and plastic strings) into their nests. Anthropogenic materials have become widely available as nesting materials in marine and terrestrial environments globally. These human-made objects can provide important benefits to birds such as serving as reliable signals to conspecifics or protecting against ectoparasites, but they can also incur fundamental survival and energetic costs via offspring entanglement and reduced insulative properties, respectively. From an ecological perspective, several hypotheses have been proposed to explain the use of anthropogenic nest materials (ANMs) by birds but no previous interspecific study has tried to identify the underlying mechanisms of this behaviour. In this study, we performed a systematic literature search and ran phylogenetically controlled comparative analyses to examine interspecific variation in the use of ANM and to examine the influence of several ecological and life-history traits. We found that sexual dimorphism and nest type significantly influenced the use of ANMs by birds providing support for the 'signalling hypothesis' that implies that ANMs reflect the quality of the nest builder. However, we found no support for the 'age' and 'new location' hypotheses, nor for a phylogenetic pattern in this behaviour, suggesting that it is widespread throughout birds. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'.

Efficient multi-fidelity computation of blood coagulation under flow.

Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species' transport, reaction kinetics, and diffusion. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, , and provide the governing PDEs for . This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order( p ) allows balancing accuracy and computational cost, providing a speedup of over N/p compared to high-fidelity models. Using a simplified coagulation network and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. These models depart from the high-fidelity solution by under 16% ( p = 1) and 5% ( p = 2) after 20 cardiac cycles. The favorable accuracy and low computational cost of multi-fidelity models could enable unprecedented coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it can be generalized to advance our understanding of other systems biology networks affected by blood flow.

Distinct platelet F-actin patterns and traction forces on von Willebrand factor versus fibrinogen.

Upon vascular injury, platelets form a hemostatic plug by binding to the subendothelium and to each other. Platelet-to-matrix binding is initially mediated by von Willebrand factor (VWF) and platelet-to-platelet binding is mediated mainly by fibrinogen and VWF. After binding, the actin cytoskeleton of a platelet drives its contraction, generating traction forces that are important to the cessation of bleeding. Our understanding of the relationship between adhesive environment, F-actin morphology, and traction forces is limited. Here, we examined F-actin morphology of platelets attached to surfaces coated with fibrinogen and VWF. We identified distinct F-actin patterns induced by these protein coatings and found that these patterns were identifiable into three classifications via machine learning: solid, nodular, and hollow. We observed that traction forces for platelets were significantly higher on VWF than on fibrinogen coatings and these forces varied by F-actin pattern. In addition, we analyzed the F-actin orientation in platelets and noted that their filaments were more circumferential when on fibrinogen coatings and having a hollow F-actin pattern, while they were more radial on VWF and having a solid F-actin pattern. Finally, we noted that subcellular localization of traction forces corresponded to protein coating and F-actin pattern: VWF-bound, solid platelets had higher forces at their central region while fibrinogen-bound, hollow platelets had higher forces at their periphery. These distinct F-actin patterns on fibrinogen and VWF and their differences in F-actin orientation, force magnitude, and force localization could have implications in hemostasis, thrombus architecture, and venous versus arterial thrombosis.

Pulmonary vein flow split effects in patient-specific simulations of left atrial flow.

Disruptions to left atrial (LA) blood flow, such as those caused by atrial fibrillation (AF), can lead to thrombosis in the left atrial appendage (LAA) and an increased risk of systemic embolism. LA hemodynamics are influenced by various factors, including LA anatomy and function, and pulmonary vein (PV) inflow conditions. In particular, the PV flow split can vary significantly among and within patients depending on multiple factors. In this study, we investigated how changes in PV flow split affect LA flow transport, focusing for the first time on blood stasis in the LAA, using a high-fidelity patient-specific computational fluid dynamics (CFD) model. We use an Immersed Boundary Method, simulating the flow in a fixed, uniform Cartesian mesh and imposing the movement of the LA walls with a moving Lagrangian mesh generated from 4D Computerized Tomography images. We analyzed LA anatomies from eight patients with varying atrial function, including three with AF and either a LAA thrombus or a history of Transient Ischemic Attacks (TIAs). Using four different flow splits (60/40% and 55/45% through right and left PVs, even flow rate, and same velocity through each PV), we found that flow patterns are sensitive to PV flow split variations, particularly in planes parallel to the mitral valve. Changes in PV flow split also had a significant impact on blood stasis and could contribute to increased risk for thrombosis inside the LAA, particularly in patients with AF and previous LAA thrombus or a history of TIAs. Our study highlights the importance of considering patient-specific PV flow split variations when assessing LA hemodynamics and identifying patients at increased risk for thrombosis and stroke. This knowledge is relevant to planning clinical procedures such as AF ablation or the implementation of LAA occluders.

Spatial Overlap and Habitat Selection of Corvid Species in European Cities.

Understanding habitat and spatial overlap in sympatric species of urban areas would aid in predicting species and community modifications in response to global change. Habitat overlap has been widely investigated for specialist species but neglected for generalists living in urban settings. Many corvid species are generalists and are adapted to urban areas. This work aimed to determine the urban habitat requirements and spatial overlap of five corvid species in sixteen European cities during the breeding season. All five studied corvid species had high overlap in their habitat selection while still having particular tendencies. We found three species, the Carrion/Hooded Crow, Rook, and Eurasian Magpie, selected open habitats. The Western Jackdaw avoided areas with bare soil cover, and the Eurasian Jay chose more forested areas. The species with similar habitat selection also had congruent spatial distributions. Our results indicate that although the corvids had some tendencies regarding habitat selection, as generalists, they still tolerated a wide range of urban habitats, which resulted in high overlap in their habitat niches and spatial distributions.

Effects of light and noise pollution on avian communities of European cities are correlated with the species' diet.

Urbanization affects avian community composition in European cities, increasing biotic homogenization. Anthropic pollution (such as light at night and noise) is among the most important drivers shaping bird use in urban areas, where bird species are mainly attracted by urban greenery. In this study, we collected data on 127 breeding bird species at 1349 point counts distributed along a gradient of urbanization in fourteen different European cities. The main aim was to explore the effects of anthropic pollution and city characteristics, on shaping the avian communities, regarding species' diet composition. The green cover of urban areas increased the number of insectivorous and omnivorous bird species, while slightly decreasing the overall diet heterogeneity of the avian communities. The green heterogeneity-a measure of evenness considering the relative coverage of grass, shrubs and trees-was positively correlated with the richness of granivorous, insectivorous, and omnivorous species, increasing the level of diet heterogeneity in the assemblages. Additionally, the effects of light pollution on avian communities were associated with the species' diet. Overall, light pollution negatively affected insectivorous and omnivorous bird species while not affecting granivorous species. The noise pollution, in contrast, was not significantly associated with changes in species assemblages. Our results offer some tips to urban planners, managers, and ecologists, in the challenge of producing more eco-friendly cities for the future.