Fluid dynamics within renal cavities during endoscopic stone surgery: does the position of the flexible ureteroscope and ureteral access sheath affect the outflow rate?

PURPOSE: To evaluate the impact of ureteroscope position within renal cavities as well as different locations of the tip of the ureteral access sheath (UAS) on fluid dynamics during retrograde intrarenal surgery (RIRS). MATERIALS AND METHODS: A prospective observational clinical study was performed. Measurements with a flexible ureteroscope placed in the upper, middle and lower calyces were obtained with the tip of the UAS placed either 2 cm below the pyelo-ureteric junction (PUJ), or at the level of the iliac crest. RESULTS: 74 patients were included. The outflow rates from the middle and upper calyxes were statistically significantly higher compared to the lower calyx, both with the UAS close to the pyelo-ureteric junction and at the iliac crest. When the UAS was withdrawn and positioned at the level of the iliac crest, a significant decrease in outflow rates from the upper (40.1 ± 4.3 ml/min vs 35.8 ± 4.1 ml/min) and middle calyces (40.6 ± 4.0 ml/min vs 36.8 ± 4.6 ml/min) and an increase in the outflow from the lower calyx (28.5 ± 3.3 ml/min vs 33.7 ± 5.7 ml/min) were noted. CONCLUSIONS: Our study showed that higher fluid outflow rates are observed from upper and middle calyces compared to lower calyx. This was true when the UAS was positioned 2 cm below the PUJ and at the iliac crest. Significant worsening of fluid dynamics from upper and middle calyces was observed when the UAS was placed distally at the level of the iliac crest. While the difference was statistically significant, the absolute change was not significant. In contrast, for lower calyces, a statistically significant improvement was documented.

Impact of Hydrodynamic Interactions on the Kinetic Pathway of Protein Folding.

Protein folding is a fundamental process critical to cellular function and human health, but it remains a grand challenge in biophysics. Hydrodynamic interaction (HI) plays a vital role in the self-organization of soft and biological materials, yet its role in protein folding is not fully understood despite folding occurring in a fluid environment. Here, we use the fluid particle dynamics method to investigate many-body hydrodynamic couplings between amino acid residues and fluid motion in the folding kinetics of a coarse-grained four-α-helices bundle protein. Our results reveal that HI helps select fast folding pathways to the native state without being kinetically trapped, significantly speeding up the folding kinetics compared to its absence. First, the directional flow along the protein backbone expedites protein collapse. Then, the incompressibility-induced squeezing flow effects retard the accumulation of non-native hydrophobic contacts, thus preventing the protein from being trapped in local energy minima during the conformational search of the native structure. We also find that the significance of HI in folding kinetics depends on temperature, with a pronounced effect under biologically relevant conditions. Our findings suggest that HI, particularly the short-range squeezing effect, may be crucial in avoiding protein misfolding.

Influence of building spatial patterns on wind environment and air pollution dispersion inside an industrial park based on CFD simulation.

The "spatial pattern-wind environment-air pollution" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of "spatial pattern-wind environment-air pollution" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.

Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation.

Accurate modeling of cerebral hemodynamics is crucial for better understanding the hemodynamics of stroke, for which computational fluid dynamics (CFD) modeling is a viable tool to obtain information. However, a comprehensive study on the accuracy of cerebrovascular CFD models including both transient arterial pressures and flows does not exist. This study systematically assessed the accuracy of different outlet boundary conditions (BCs) comparing CFD modeling and an in-vitro experiment. The experimental setup consisted of an anatomical cerebrovascular phantom and high-resolution flow and pressure data acquisition. The CFD model of the same cerebrovascular geometry comprised five sets of stationary and transient BCs including established techniques and a novel BC, the phase modulation approach. The experiment produced physiological hemodynamics consistent with reported clinical results for total cerebral blood flow, inlet pressure, flow distribution, and flow pulsatility indices (PI). The in-silico model instead yielded time-dependent deviations between 19-66% for flows and 6-26% for pressures. For cerebrovascular CFD modeling, it is recommended to avoid stationary outlet pressure BCs, which caused the highest deviations. The Windkessel and the phase modulation BCs provided realistic flow PI values and cerebrovascular pressures, respectively. However, this study shows that the accuracy of current cerebrovascular CFD models is limited.

A new computational fluid dynamics based noninvasive assessment of portacaval pressure gradient.

Accurate assessment of portacaval pressure gradient (PCG) in patients with portal hypertension (PH) is of great significance both for diagnosis and treatment. This study aims to develop a noninvasive method for assessing PCG in PH patients and evaluate its accuracy and effectiveness. This study recruited 37 PH patients treated with transjugular intrahepatic portosystemic shunt (TIPS). computed tomography angiography was used to create three dimension (3D) models of each patient before and after TIPS. Doppler ultrasound examinations were conducted to obtain the patient's portal vein flow (or splenic vein and superior mesenteric vein). Using computational fluid dynamics (CFD) simulation, the patient's pre-TIPS and post-TIPS PCG was determined by the 3D models and ultrasound measurements. The accuracy of these noninvasive results was then compared to clinical invasive measurements. The results showed a strong linear correlation between the PCG simulated by CFD and the clinical invasive measurements both before and after TIPS (R2 = 0.998, P < 0.001 and R2 = 0.959, P < 0.001). The evaluation accuracy of this noninvasive method reached 94 %, and the influence of ultrasound result errors on the numerical accuracy was found to be marginal if the error was less than 20 %. Furthermore, the information about the hemodynamic environment in the portal system was obtained by this numerical method. Spiral flow patterns were observed in the portal vein of some patients. In a conclusion, this study proposes a noninvasive numerical method for assessing PCG in PH patients before and after TIPS. This method can assist doctors in accurately diagnosing patients and selecting appropriate treatment plans. Additionally, it can be used to further investigate potential biomechanical causes of complications related to TIPS in the future.

Trapping and scattering of a multiflagellated bacterium by a hard surface.

Thiovulum majus, which is one of the fastest known bacteria, swims using hundreds of flagella. Unlike typical pusher cells, which swim in circular paths over hard surfaces, T. majus localize near hard boundaries by turning their flagella to exert a net force normal to the surface. To probe the torques that stabilize this hydrodynamically bound state, the trajectories of several thousand collisions between a T. majus cell and a wall of a quasi-two-dimensional microfluidic chamber are analyzed. Measuring the fraction of cells escaping the wall either to the left or to the right of the point of contact-and how this probability varies with incident angle and time spent in contact with the surface-maps the scattering dynamics onto a first passage problem. These measurements are compared to the prediction of a Fokker-Planck equation to fit the angular velocity of a cell in contact with a hard surface. This analysis reveals a bound state with a narrow basin of attraction in which cells orient their flagella normal to the surface. The escape angle predicted by matching these near field dynamics with the far-field hydrodynamics is consistent with observation. We discuss the significance of these results for the ecology of T. majus and their self-organization into active chiral crystals.

Characterization of cellular responses and cell lysis to elevated hydrodynamic stress from benchtop perfusion bioreactors.

The effective design of perfusion cell culture is currently challenging regarding balancing the operating parameters associated with the hydrodynamic conditions due to increased system complexity. To address this issue, cellular responses of an industrial CHO cell line to different types of hydrodynamic stress in benchtop perfusion bioreactors originating from agitation, sparging, and hollow fibers (HF) in the cell retention devices were systematically investigated here with the analysis of cell lysis. It was found that cell lysis was very common and most associated with the sparging stress, followed by the HF and lastly the agitation, consequently heavily impacting the estimation of process descriptors related to biomass. The results indicated that the agitation stress led to a reduced cell growth with a shift toward a more productive phenotype, suggesting an energy redirection from biomass formation to product synthesis, whereas the sparging stress had a small impact on the intracellular metabolic flux distribution but increased the cell death rate drastically. For HF stress, a similar cell maintenance profile was found as the sparging while the activity of glycolysis and the TCA cycle was significantly impeded, potentially leading to the lack of energy and thus a substantial decrease in cell-specific productivity. Moreover, a novel concept of volume average shear stress was developed to further understand the relations of different types of stress and the observed responses for an improved insight for the perfusion cell culture.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall.

Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.

Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning.

Septoplasty and turbinectomy are among the most common interventions in the field of rhinology. Their constantly debated success rates and the lack of quantitative flow data of the entire nasal airway for planning the surgery necessitate methodological improvement. Thus, physics-based surgery planning is highly desirable. In this work, a novel and accurate method is developed to enhance surgery planning by physical aspects of respiration, i.e., to plan anti-obstructive surgery, for the first time a reinforcement learning algorithm is combined with large-scale computational fluid dynamics simulations. The method is integrated into an automated pipeline based on computed tomography imaging. The proposed surgical intervention is compared to a surgeon's initial plan, or the maximum possible intervention, which allows the quantitative evaluation of the intended surgery. Two criteria are considered: (i) the capability to supply the nasal airway with air expressed by the pressure loss and (ii) the capability to heat incoming air represented by the temperature increase. For a test patient suffering from a deviated septum near the nostrils and a bony spur further downstream, the method recommends surgical interventions exactly at these locations. For equal weights on the two criteria (i) and (ii), the algorithm proposes a slightly weaker correction of the deviated septum at the first location, compared to the surgeon's plan. At the second location, the algorithm proposes to keep the bony spur. For a larger weight on criterion (i), the algorithm tends to widen the nasal passage by removing the bony spur. For a larger weight on criterion (ii), the algorithm's suggestion approaches the pre-surgical state with narrowed channels that favor heat transfer. A second patient is investigated that suffers from enlarged turbinates in the left nasal passage. For equal weights on the two criteria (i) and (ii), the algorithm proposes a nearly complete removal of the inferior turbinate, and a moderate reduction of the middle turbinate. An increased weight on criterion (i) leads to an additional reduction of the middle turbinate, and a larger weight on criterion (ii) yields a solution with only slight reductions of both turbinates, i.e., focusing on a sufficient heat exchange between incoming air and the air-nose interface. The proposed method has the potential to improve the success rates of the aforementioned surgeries and can be extended to further biomedical flows.

Establishment of a hydrodynamic delivery system in ducks.

Chronic hepatitis B virus (HBV) poses a significant global health challenge as it can lead to acute or chronic liver disease and hepatocellular carcinoma (HCC). To establish a safety experimental model, a homolog of HBV-duck HBV (DHBV) is often used for HBV research. Hydrodynamic-based gene delivery (HGD) is an efficient method to introduce exogenous genes into the liver, making it suitable for basic research. In this study, a duck HGD system was first constructed by injecting the reporter plasmid pLIVE-SEAP via the ankle vein. The highest expression of SEAP occurred when ducks were injected with 5 µg/mL plasmid pLIVE-SEAP in 10% bodyweight volume of physiological saline for 6 s. To verify the distribution and expression of exogenous genes in multiple tissues, the relative level of foreign gene DNA and ß-galactosidase staining of LacZ were evaluated, which showed the plasmids and their products were located mainly in the liver. Additionally, ß-galactosidase staining and fluorescence imaging indicated the delivered exogenous genes could be expressed in a short time. Further, the application of the duck HGD model on DHBV treatment was investigated by transferring representative anti-HBV genes IFNα and IFNγ into DHBV-infected ducks. Delivery of plasmids expressing IFNα and IFNγ inhibited DHBV infection and we established a novel efficient HGD method in ducks, which could be useful for drug screening of new genes, mRNAs and proteins for anti-HBV treatment.

Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.

We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the ability of a single model to reconstruct three-dimensional stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions. Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Auto-encoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations. Our experiments achieved mean squared errors in the true flow reconstruction of O(1.0 × 10-4), and root mean squared residuals of O(1.0 × 10-2) for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the hidden pressure field and 0.82 for the derived wall shear stress field. By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20× the input resolution.

Establishment of a murine hepatocellular carcinoma model by hydrodynamic injection and characterization of the immune tumor microenvironment.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant neoplasms. Current treatments for HCC, such as tyrosine kinase inhibitors, have limited efficacy, highlighting the urgent need for better therapies. Immunotherapies, including anti-programmed death receptor 1 (PD-1) and anti-Cytotoxic T-lymphocyte associated protein 4 (CTLA-4), and more recently, the combination of anti-PD-L1 and anti-vascular endothelial growth factor (VEGF) monoclonal antibodies, have shown efficacy against HCC, resulting in Food and Drug Administration (FDA) approval. However, these immunotherapies only show efficiency in a small proportion of patients, meaning there is a great need to improve and optimize treatments against HCC. Accurate animal models that mimic human HCC are necessary to help better understand the nature of these tumors, which in turn will allow the development and testing of new treatments. Existing pre-clinical HCC models can be divided into non-genetic and genetic models. Non-genetic models involve implanting human or murine HCC cell lines or inducing tumors using chemical compounds or dietary modifications. These models have limitations, including slow tumor development and a lack of resemblance to human HCC. Genetic models, on the other hand, manipulate gene expression to induce HCC in mice and provide a better understanding of the effects of specific genes on tumor development. One method commonly used to generate HCC is hydrodynamic tail vein injection (HTVI), which consists of the delivery of oncogenes directly to the liver, resulting in expression and subsequent hepatocyte transformation. Usually, Sleeping Beauty transposase-containing plasmids are used to achieve stable and long-term gene expression. Once the HCC tumor is generated, and a proper tumor microenvironment (TME) is established, it is important to study the immune compartment of the TME, which plays a crucial role in HCC development and response to treatment. Techniques like flow cytometry can be used to analyze the immune cell populations in HCC tumors and assess their impact on tumor development and survival in mice. In this article, we thoroughly describe an example of the methodology to successfully generate HCC murine models via HTVI, and we propose a way to characterize the immune TME by flow cytometry.

Exploring the Link between Hydrodynamic Size and Immunoglobulins of Circulating Immune Complexes in Rheumatoid Arthritis Patients.

The function of immune complexes in rheumatoid arthritis (RA) is related to their composition and size. Using dynamic light scattering (DLS), we investigated the link between the RA circulating immune complex (CIC) particles' size and the CIC immunoglobulin level. In this study, 30 RA patients and 30 healthy individuals were included. IgA, IgG, and IgM were found in all analyzed CICs, but more IgA and IgG were found in RA than in control CICs. In both control and RA CICs, DLS detected 50 particles that differed in size and clustered around two size groups: with a 7.5-164 nm radius and with a 342-1718 nm radius. An increased level of IgA in RA CICs, compared to control ones, was associated with more than 50% of CIC particles. In RA, compared to the control, a higher number of CICs with 28.2 nm, 531 nm, 712 nm, and 1718 nm particles and a lower number of CICs with 78.8 nm particles were detected. This particle distribution pattern did not reflect the changes in the CIC immunoglobulin level. Thus, RA elevated CIC IgA was linked with all these particles (except the 1718 nm particle), the IgM increase was linked with 43.8 nm and 712 nm particles, and the IgG increase was linked with the 712 nm particle only. This study provides the very first data on the association between CIC particles' size, CIC immunoglobulin level, and RA. It opens the possibility that the size of CICs determined by DLS can be used as a criterion in RA diagnosis or monitoring after a large-scale study confirmation.

The genetic basis of hydrocephalus: genes, pathways, mechanisms, and global impact.

Hydrocephalus (HC) is a heterogenous disease characterized by alterations in cerebrospinal fluid (CSF) dynamics that may cause increased intracranial pressure. HC is a component of a wide array of genetic syndromes as well as a secondary consequence of brain injury (intraventricular hemorrhage (IVH), infection, etc.) that can present across the age spectrum, highlighting the phenotypic heterogeneity of the disease. Surgical treatments include ventricular shunting and endoscopic third ventriculostomy with or without choroid plexus cauterization, both of which are prone to failure, and no effective pharmacologic treatments for HC have been developed. Thus, there is an urgent need to understand the genetic architecture and molecular pathogenesis of HC. Without this knowledge, the development of preventive, diagnostic, and therapeutic measures is impeded. However, the genetics of HC is extraordinarily complex, based on studies of varying size, scope, and rigor. This review serves to provide a comprehensive overview of genes, pathways, mechanisms, and global impact of genetics contributing to all etiologies of HC in humans.

Degradation of Procion brilliant yellow H-E6G using ultrasonic and hydrodynamic cavitation combined with oxidants with demonstration at pilot scale.

The current study focuses on the degradation of Procion brilliant yellow H-E6G, an azo dye, using ultrasonic and hydrodynamic cavitation (HC), evaluating the impact of various parameters on the extent of degradation. The use of only ultrasound showed less oxidation capacity as indicated by only 19.1% degradation at an optimized power of 140 W, pH of 2.5, temperature of 40°C, and initial dye concentration of 15 ppm. The effectiveness of hybrid approaches involving US + H2 O2 , US + Fenton, and US + H2 O2 + potassium persulfate (KPS) was subsequently evaluated under optimized conditions. A notable enhancement in decolorization extent was observed for combined operations, including US + H2 O2 , US + Fenton, and US + H2 O2 + KPS (dual oxidant scheme) with the actual decolorization extents as 80.6%, 85%, and 92.2% respectively. An optimized scheme of US + H2 O2 + KPS was also utilized to decolorize the dye at a pilot scale using a US flow cell and also an HC reactor that yielded 91.8% and 88% reductions in initial concentration. The dye decolorization was elucidated to follow first-order kinetics for all the individual and combination approaches. The obtained values of the rate constants were also utilized for the evaluation of the synergistic index. A toxicity analysis was also performed on the dye, both before and following treatment, utilizing two bacterial strains. A comparative analysis of various treatment approaches has been presented focusing on factors such as cavitational yield, operational expenses, and energy requirements. The study elucidated that the combination of US + H2 O2 + KPS effectively removes Procion brilliant yellow H-E6G giving 92.2% as the maximum degradation at an operating cost of 0.1862 $/L. PRACTITIONER POINTS: First depiction of cavitative degradation of Procion brilliant yellow H-E6G Optimizing the equipment operating parameters and chemical oxidants Demonstration of optimized treatment scheme at pilot scale Evaluation of various approaches based on synergy and costs of treatment US + H2 O2  + KPS is the best approach for dye degradation.

Reliability analysis using Limit equilibrium and Smoothed Particle Hydrodynamics-based method for homogeneous soil slopes.

This paper develops a combined method to predict the volume of sliding mass for homogeneous slopes in an efficient manner. Firstly, the failure surface with minimum factor of safety (FS) in Limit Equilibrium Method is equated to that one determined by Smoothed Particle Hydrodynamics algorithm to obtain the threshold displacement value for unstable and stable particles. Secondly, the threshold displacement value is used to identify the volume of sliding mass using SPH. Finally, a regression model is developed based on a finite number of SPH simulations for homogeneous soil slopes. The proposed LEM-SPH based method is illustrated through a cohesive soil slope. It is concluded that the use of failure surface with minimum FS in LEM tends to underestimate the volume of sliding mass and to give an unconservative risk value. The Coefficient of Variation (Cov) of volume of sliding mass are 0.14, 0.28, 0.4, 0.48, 0.53 for Cov of soil properties = 0.2, 0.3, 0.4, 0.5, and 0.6, respectively. The uncertainty of soil properties has a significant effect on the mean value of volume of sliding mass and therefore the landslide risk value. The proposed method is necessitated for cases where large uncertainties in soil properties exist.

Motion tracking of fish and bubble clouds in synthetic aperture sonar data.

Data captured by a Synthetic Aperture Sonar (SAS) near Mobile Bay during the 2021 Undersea Remote Sensing experiment funded by the Office of Naval Research reveals near surface bubble clouds from wave breaking events and a large aggregation of fish. Tools developed for using SAS data to image hydrodynamic features in the water column were applied to observations of the bubble clouds and fish aggregation. Combining imagery and height data captured by the sonar array with a detection and tracking algorithm enables the trajectories, velocities, and behavior of fish in the aggregation to be observed. Fitting the velocity and height data of the tracked objects to a Gaussian mixture model and performing cluster analysis enables an estimate of the near-surface ambient velocity via observation of the movement of the bubble traces and the general direction of motion of the fish aggregation. We find that the velocity traces associated with bubbles are consistent with ambient currents as opposed to the direction of propagating wave crests while velocities of fish indicate relatively large, pelagic species.

Smoothed particle hydrodynamics based FSI simulation of the native and mechanical heart valves in a patient-specific aortic model.

The failure of the aortic heart valve is common, resulting in deterioration of the pumping function of the heart. For the end stage valve failure, bi-leaflet mechanical valve (most popular artificial valve) is implanted. However, due to its non-physiological behaviour, a significant alteration is observed in the normal haemodynamics of the aorta. While in-vivo experimentation of a human heart valve (native and artificial) is a formidable task, in-silico study using computational fluid dynamics (CFD) with fluid structure interaction (FSI) is an effective and economic tool for investigating the haemodynamics of natural and artificial heart valves. In the present work, a haemodynamic model of a natural and mechanical heart valve has been developed using meshless particle-based smoothed particle hydrodynamics (SPH). In order to further enhance its clinical relevance, this study employs a patient-specific vascular geometry and presents a successful validation against traditional finite volume method and 4D magnetic resonance imaging (MRI) data. The results have demonstrated that SPH is ideally suited to simulate the heart valve function due to its Lagrangian description of motion, which is a favourable feature for FSI. In addition, a novel methodology for the estimation of the wall shear stress (WSS) and other related haemodynamic parameters have been proposed from the SPH perspective. Finally, a detailed comparison of the haemodynamic parameters has been carried out for both native and mechanical aortic valve, with a particular emphasis on the clinical risks associated with the mechanical valve.

Exploring Host-Guest Interactions within a 600 kDa DegP Protease Cage Complex Using Hydrodynamics Measurements and Methyl-TROSY NMR.

The DegP protease-chaperone operates within the periplasm of Gram-negative bacteria, where it assists in the regulation of protein homeostasis, promotes virulence, and is essential to survival under stress. To carry out these tasks, DegP forms a network of preorganized apo oligomers that facilitate the capture of substrates within distributions of cage-like complexes which expand to encapsulate clients of various sizes. Although the architectures of DegP cage complexes are well understood, little is known about the structures, dynamics, and interactions of client proteins within DegP cages and the relationship between client structural dynamics and function. Here, we probe host-guest interactions within a 600 kDa DegP cage complex throughout the DegP activation cycle using a model α-helical client protein through a combination of hydrodynamics measurements, methyl-transverse relaxation optimized spectroscopy-based solution nuclear magnetic resonance studies, and proteolytic activity assays. We find that in the presence of the client, DegP cages assemble cooperatively with few intermediates. Our data further show that the N-terminal half of the bound client, which projects into the interior of the cages, is predominantly unfolded and flexible, and exchanges between multiple conformational states over a wide range of time scales. Finally, we show that a concerted structural transition of the protease domains of DegP occurs upon client engagement, leading to activation. Together, our findings support a model of DegP as a highly cooperative and dynamic molecular machine that stabilizes unfolded states of clients, primarily via interactions with their C-termini, giving rise to efficient cleavage.