This work investigates optimal magnetic nanoparticle (MNP) injection strategies in three-dimensional (3D) tumor models to enhance the magnetic hyperthermia efficacy. We consider three tumor models with increasing geometric complexities: a spherical tumor, a simple irregular tumor (two connected spheres of different sizes), and a complex irregular tumor (three connected spheres of varying sizes). Centrosymmetric MNP distributions are employed for the spherical model, whereas asymmetric distributions are applied for the irregular models. The rapid convergence of the optimization demonstrates the efficiency and effectiveness of this 3D optimization framework. For the spherical tumor model, multi-site injections significantly enhance therapeutic outcomes under a 20-min waiting limit, whereas a single-site injection with a 114.9-min waiting time achieves 100% tumor ablation without damaging adjacent healthy tissue. Two injection sites suffice for the simple irregular tumor model, while a three-site strategy is optimal for the complex irregular model, indicating a relationship between required injection number and tumor geometry. Furthermore, the optimal MNP injection strategies correlate positively with the locations and sizes of the connected spheres. These findings produce more practical optimal strategies and provide broader, clinically relevant guidance for magnetic hyperthermia treatment.

Background: Traditional CFD analyses often rely on static (rigid) vascular geometries, which neglect the physiologically relevant motion of the aortic wall. This simplification can lead to inaccuracies in estimating key hemodynamic biomarkers, such as wall shear stress (WSS) and oscillatory shear index (OSI). Methods: This study introduces the Large Deformation Diffeomorphic Metric Mapping (LDDMM) method to enable computationally efficient simulations of transient blood flow in compliant, subject- and patient-specific aortas derived from 4D Flow MRI data. The proposed framework simplifies CFD pre-processing, improves morphing accuracy, and enables physiologically realistic motion of the thoracic aorta, including its side-branches. The method was applied to two aortic geometries: a healthy case (HC) and a case with thoracic aortic aneurysm (TAA) located in the ascending region. Results: The results were compared with those obtained from fixed aortic geometries extracted at peak systole. Hemodynamic biomarkers showed significant differences between static and moving geometries. For the healthy case (HC), the differences were 18% for the time-averaged wall shear stress (TAWSS) and 46% for the oscillatory shear index (OSI). For the thoracic aorta aneurysm (TAA) case, the corresponding values were 14% and 47%, respectively. Conclusion: These findings highlight the importance of incorporating aortic wall motion in hemodynamic simulations. The developed LDDMM-based framework can be readily extended to other imaging modalities, such as ultrasound or computed tomography, and is recommended for future CFD analyses of compliant aortas.

We report recent advances in the development of a computationally efficient and accurate time-dependent RANS-based (T-RANS) method for predicting turbulent heavy-gas dispersion in complex urban environments. Two test cases were analyzed: (i) the simplified Kit Fox field-scale setup, and (ii) a realistic urban neighborhood in Beijing. In the first case, the T-RANS approach outperformed the simplified atmospheric dispersion model (SLAB), showing significantly better agreement with field measurements due to its ability to capture local flow and turbulence features within obstructed regions. The numerical robustness and computational efficiency of the method were further validated in the real-scale Beijing neighborhood, incorporating detailed building geometries and a mesh of over 100 million control volumes. By utilizing a passive-element approach to resolve complex building geometries and accounting for the buoyancy-driven effects of local gas concentrations, this method provides a high-fidelity, computationally efficient framework for predicting the temporal evolution of turbulent heavy-gas dispersion in complex built environments.

Inhaled drug delivery is a promising strategy for the rapid treatment of respiratory diseases due to its direct targeting of the pulmonary system. Nevertheless, challenges remain in optimizing deposition efficiency, particularly in reaching deeper lung generations and achieving directional control of particle transport. To achieve effective deep-lung aerosol delivery, the present proof-of-concept study proposes computational optimization of particle release strategies. Both non-invasive and invasive approaches are explored, with particular emphasis on release concentration and spatial positioning. Numerical simulations are conducted using a previously validated subject-specific mouth-to-lung model reconstructed from high-resolution Computed Tomography (CT) scans, ensuring anatomical realism and geometrical reproducibility. The results show that concentrated non-invasive release at the mouth plane improves particle penetration through the constricted laryngeal region. Meanwhile, invasive strategies involving focused delivery (such as catheter-based injection) lead to enhanced deposition in the deeper lung regions. Notably, directional control of deposition was preliminarily achieved, with particles preferentially targeting either the left or right lung lobe based on the injection position, offering new potential for site-specific therapy. It is concluded that the presented computational framework can provide detailed insights for optimizing particle transport and deposition in specific lung regions. These detailed insights could provide valuable information for developing novel clinical treatments for respiratory diseases.

BACKGROUND:Understanding the transport of nanoparticles within blood vessels and their distribution in tumor tissues is crucial for the successful implementation of nanotechnological strategies in clinical practice. Although numerous studies have examined nanoparticle transport in blood flow, none have comprehensively investigated all the sequential steps a nanoparticle must undergo prior to internalization by target cells. METHODS:A computational framework was developed in COMSOL Multiphysics to simulate nanoparticle (NP) transport from systemic administration through to tumor cell internalization. The model integrates three coupled stages: (1) NP movement within a non-Newtonian blood flow; (2) trans-endothelial transport; and (3) NP motion within the tumor stroma, incorporating affinity forces to capture ligand-receptor interactions. The tumor geometry was reconstructed, including cancer cells and fibroblasts, to reproduce physiological porosity. Multiple case studies were conducted to evaluate the impact of particle density, injection velocity, and size on NP biodistribution. RESULTS:The computational model effectively simulates nanoparticle transport across all stages. Notably, it is the first model in the literature to incorporate the affinity of functionalized nanoparticles, which facilitates ligand-receptor interactions for targeted delivery. Simulation outcomes indicate that a low Stokes number is critical for ensuring a higher percentage of particles reach the end of the capillary network. Furthermore, surface modification of nanoparticles with ligands promotes more specific distribution within the stroma, reducing the percentage of nanoparticles that fail to reach target cells by approximately 50% CONCLUSIONS: A novel and comprehensive computational model has been developed to include the entire process of nanoparticle distribution following systemic administration, including specific recognition by cellular receptors.

A novel approach to generate left ventricular (LV) pressure–volume (PV) loops from combined 4D Flow MRI and computational fluid dynamics (CFD) is presented. Pressure was calculated from person-specific three-dimensional (3D) CFD models created from LV segmentations and peak-systolic pressure from the one-dimensional 111-artery CFD model, with aortic flow from 4D Flow MRI as input. Ten healthy volunteers underwent scan–rescan MRI. Additionally, one patient without cardiovascular abnormalities underwent MRI and invasive catheter measurement for single-case comparison. Scan–rescan reproducibility was very good overall, with no significant differences in any parameters and ICCs of all parameters but minimum pressure were significant and high (0.55–0.99). Aortic flow of 3D CFD model correlated well with 4D Flow (ICC = 0.74) and stroke volume of LV segmentation (ICC = 0.90). Segmentation volume variability resulted in 12% difference in stroke work and mean external power, while aortic flow variability resulted in 10–11% difference in most parameters. Single-case comparison is promising, with only 1.8 mmHg and 0.005 mmHg/mL difference in minimum pressure and EDPVR, and <10% differences for other parameters. Noninvasive pressure–volume loops can therefore reproducibly be generated from only aortic flow, cine short axis MRI, and brachial pressure measurement. Single-case comparison shows promise, but larger validation studies are needed.

We report on numerical studies of bounded double-diffusive turbulent convection, which involves the combined effects of concentration/solutal and thermal buoyancy forces. Our study focuses on an intermediate range of the characteristic non-dimensional numbers, specifically 10(7) <= Ra-c <= 10(9), and 0 <= Ra-theta <= 10(6). We use fixed values for the concentration and temperature Prandtl numbers (i.e. Pr-c = 700, Pr-theta = 7), which approximately correspond to seawater properties. We apply wall-resolved Large Eddy Simulations (LES) and compare the obtained results with available Direct Numerical Simulations (DNS) in the literature. Our findings show an overall good agreement in predicting the global wall mass and heat transfer coefficients, achieved with significantly reduced computational costs. Furthermore, the local mass and heat transfer distributions reveal a high sensitivity to the strength of the vertically imposed stable thermal stratification. Finally, we present the vertical profiles of the long-term time-averaged first and second moments.

Targeted drug delivery to the deep lung improves therapeutic outcomes, but respiratory system variability complicates drug spray design. Numerical simulations offer insights for individualized treatments but are computationally intensive, highlighting the need for surrogate models for real-time deposition prediction. This study comprehensively explores the multi-task predictive capability of regression models, including Linear regression (LR), Bayesian regression (BR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and CatBoost, for predicting total and regional deposition rates of inhaled particles in airway. A training dataset is obtained from well-validated CFD simulations with realistic human airway model using Euler-Lagrangian method. The results indicate that LR, BR, and SVM yield unsatisfactory predictive accuracy, with average R2 values in range of 0.21 to 0.73. Comparatively, BPNN and decisiontreebased models show great potential in predicting total deposition rate in the upper and central airway. However, for regional deposition rate prediction, BPNN did not consistently yield high accuracy, particularly for oral deposition (R2 = 0.538). Comparatively, XGBoost emerges as optimal model, achieving an R2 approximately close to 1 on both the training and testing datasets, with predictive errors within the range of +/- 0.5. The overall results demonstrate that decision-tree-based models, particularly XGBoost, have superior performance in accurately predicting both total and regional deposition rates of inhaled particles within airway. Despite limitations like geometry complexity and data quantity, the workflow developed in this study is expected to pave the way for future research integrating ML models into drug delivery device design and evaluation.

In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40 subject-specific aortic geometries obtained from 4D Flow MRI, we applied statistical shape modeling to generate 1,000 synthetic aorta geometries. Complete computational fluid dynamics (CFD) simulations of these geometries were performed to obtain ground-truth values. We then trained deep neural networks for each characteristic flow feature using 900 randomly selected aorta geometries. Testing on remaining 100 geometries resulted in average errors of 3.11% for velocity and 4.48% for pressure. For wall shear stress predictions, we applied two approaches: (i) directly derived from the neural network-predicted velocity, and, (ii) predicted from a separate neural network. Both approaches yielded similar accuracy, with average error of 4.8 and 4.7% compared to complete 3D CFD results, respectively. We recommend the second approach for potential clinical use due to its significantly simplified workflow. In conclusion, this proof-of-concept analysis demonstrates the numerical robustness, rapid calculation speed (less than seconds), and good accuracy of the CFD-based machine learning approach in predicting velocity, pressure, and wall shear stress distributions in subject-specific aortic flows.

Magnetic hyperthermia is a promising cancer treatment method that involves complex multiphysics phenomena, including interstitial tissue fluid flow, magnetic nanoparticle (MNP) transport, and temperature evolution. However, these intricate processes have rarely been studied simultaneously, primarily due to the lack of a comprehensive simulation tool. To address this issue, we develop a comprehensive numerical framework in this study. Using this framework, we simulate a circular-shaped tumor embedded in healthy tissue. The treatment process is examined under two scenarios: one considering gravity and the other neglecting it. Without gravity, the interstitial tissue flow remains stationary, and hence MNP transport and temperature evolution are determined solely by diffusion. The optimal treatment time, when the tumor cells are completely ablated, decreases with both the Lewis number and the heat source number, following a power law. When gravity is considered, treatment efficacy deteriorates due to buoyancy-induced MNP movement, significantly extending the time required to completely ablate the tumor cells. This required time increases with both the buoyancy ratio and the Darcy ratio, also following a power law. The results from this study could provide valuable guidelines for practical magnetic hyperthermia treatment.

Numerous studies in the literature have proposed the use of thermo-responsive hydrogels for filling cavities after tumor resection. However, optimizing the injection process is challenging due to the complex interplay of various multi-physics phenomena, such as the coupling of flow and heat transfer, multi-phase interactions, and phase-change dynamics. Therefore, gaining a fundamental understanding of these processes is crucial. In this study, we introduce a thermo-sensitive hydrogel formulated with poloxamer 407 and Gellan gum as a promising filling agent, offering an ideal phase-transition temperature along with suitable elastic and viscous modulus properties. We performed multi-physics simulations to predict the flow and temperature distributions during hydrogel injection. The results suggested that the hydrogel should be kept at 4 °C and injected within 90 s to avoid reaching the transition temperature. Cavity filling simulations indicated a symmetric distribution of the hydrogel, with minimal influence from the syringe's position. The temperature gradient at the cavity edge delays gelation during injection, which is essential to guarantee its administration as a liquid. The hydrogel's viscosity follows a sigmoidal function relative to temperature, taking five minutes to reach its maximum value. In summary, the multi-physics simulations carried out in this study confirm the potential of thermo-responsive hydrogels for use in post-tumor surgery treatment and define the conditions for a proper administration. Furthermore, the proposed model can be widely applied to other thermo-responsive hydrogels or under different conditions.

This paper presents a study of developing a numerical turbulent model in a cavity heated from below using eddy viscosity combined with elliptic relaxation approach. The model uses a set of differential equations that consist of kinetic energy, its dissipation, variance of temperature, velocity scale and elliptic relaxation parameter, which are solved using a finite-volume and Navier-Stokes solver. The unresolved stress tensors and heat flux vectors are modelled with an algebraic formula. The discretization method is carried out by CDS, or second-order differencing scheme, and LUDS, or second-order linear upwind scheme. The model is applied to the natural convection heated from below, known as Rayleigh-Benard convection, in a two-dimensional cavity with a height-to-length aspect ratio of 1:1.5, 1:4, and 1:8. The model has been validated using numerical data from DNS (direct numerical simulation) and experiments. The model produced similar results with both DNS and experiments. It was also shown that the model can visualize the main feature of turbulent convective flow in the enclosure for various aspect ratio.

Background Kidney disease is the most important predictor of death in patients with a Fontan circulation, yet its clinical and hemodynamic correlates have not been well established. Methods and Results A total of 53 ambulatory patients with a Fontan circulation (median age, 16.2 years, 52.8% male patients) underwent advanced cardiovascular magnetic resonance assessment, including 4‐dimensional flow imaging and computational fluid dynamics. Estimated glomerular filtration rate (eGFR) <90 mL/min per 1.73 m2 was observed in 20.8% and albumin‐to‐creatinine ratio >3 mg/mmol in 39.6%. The average eGFR decline rate was −1.83 mL/min per 1.73 m2 per year (95% CI, −2.67 to −0.99; P<0.001). Lower eGFR was associated with older age, larger body surface area at examination, longer time since Fontan procedure, and lower systemic ventricular ejection fraction. Higher albumin‐to‐creatinine ratio was associated with absence of fenestration at the Fontan operation, and older age and lower systemic ventricular ejection fraction at the assessment. Lower cross‐sectional area of the Fontan conduit indexed to flow (r=0.32, P=0.038), higher inferior vena cava–conduit velocity mismatch factor (r=−0.35, P=0.022), higher kinetic energy indexed to flow in the total cavopulmonary connection (r=−0.59, P=0.005), and higher total cavopulmonary connection resistance (r=−0.42, P=0.005 at rest; r=−0.43, P=0.004 during exercise) were all associated with lower eGFR but not with albuminuria. Conclusions Kidney dysfunction and albuminuria are common among clinically well adolescents and young adults with a Fontan circulation. Advanced cardiovascular magnetic resonance–derived metrics indicative of declining Fontan hemodynamics are associated with eGFR and might serve as targets to improve kidney health. Albuminuria might be driven by other factors that need further investigation.

Several studies have recommended the use of hydrogels for localized targeted delivery of chemotherapeutic drugs following tumor removal surgery. This approach aims to both fill the cavity and prevent cancer recurrence. The use of Multiphysics-based simulation emerges as a valuable strategy for minimizing experimental work, providing detailed insights into how drug release occurs in the tissue, and enabling the optimization of the design.In this study, we introduced a mathematical model, utilizing experimental data, to investigate the transport of liposomes carrying MZ1 from a thermosensitive hydrogel and their impact on the viability of breast cancer cells. The proposed comprehensive model considers not just the transport within the interstitial tissue, represented as a porous medium, but also the uptake by cells and its influence on cell viability, along with the potential lymphatic drainage.The six real patient-specific tumor shapes extracted from MRI scans were used to investigate how the size and form of the tumor can modify the transport pattern. The computational results revealed that the concentration of liposomes in the tissue is significantly influenced by their release from the hydrogel, which proved to be the limiting step. Liposome concentrations of approximately 0.1% weight were found to be sufficient in ensuring minimal cell survival in the vicinity of the tumor.

The treatment for asthma and chronic obstructive pulmonary disease relies on forced inhalation of drug particles. Their distribution is essential for maximizing the outcomes. Patient-specific computational fluid dynamics (CFD) simulations can be used to optimize these therapies. In this regard, this study focuses on creating a parametric model of the human respiratory tract from which synthetic anatomies for particle deposition analysis through CFD simulation could be derived. A baseline geometry up to the fourth generation of bronchioles was extracted from a CT dataset. Radial basis function (RBF) mesh morphing acting on a dedicated tree structure was used to modify this baseline mesh, extracting 1000 synthetic anatomies. A total of 26 geometrical parameters affecting branch lengths, angles, and diameters were controlled. Morphed models underwent CFD simulations to analyze airflow and particle dynamics. Mesh morphing was crucial in generating high-quality computational grids, with 96% of the synthetic database being immediately suitable for accurate CFD simulations. Variations in wall shear stress, particle accretion rate, and turbulent kinetic energy across different anatomies highlighted the impact of the anatomical shape on drug delivery and deposition. The study successfully demonstrates the potential of tree-structure-based RBF mesh morphing in generating parametric airways for drug delivery studies.

In the present study, we have applied a combined wall-resolving dynamic Large-Eddy Simulation (LES) (for the velocity field) and Direct Numerical Simulation (DNS) (for the temperature field) approach for mixing of parallel triple-jets with different temperatures of liquid sodium in a turbulent forced convection regime. Because of the high thermal conductivity of sodium (a low-Prandtl fluid), we adopted the dynamic Smagorinsky subgrid closure for the unresolved velocity scales, while the thermal scales are fully resolved. Furthermore, the Time-dependent Reynolds-Averaged Navier-Stokes (T-RANS) approach with the high-Reynolds number variant (i.e. with the wall functions as boundary conditions along solid boundaries) of the four-equation eddy viscosity model (k - e - ke 4) was applied. The fine-mesh LES/DNS provided a close agreement with the experimental data for both velocity and temperature fields (for both first- and second-moments). In contrast, the coarse-mesh LES/DNS overestimated the turbulent kinetic energy profiles at different distances from the inlet plane. The T-RANS results confirmed a good agreement with the mean streamwise velocity and turbulent kinetic energy, as well as the mean temperature profiles. Finally, the analysis of power spectral density distributions of the temperature signal revealed that all simulation techniques captured a dominant flow frequency originating from the induced KelvinHelmholtz instabilities between the side and central jets. The presented combined dynamic LES/DNS approach is recommended for future simulations of the turbulent forced convection flows of low Prandtl fluids, especially if thermal fatigue effects need to be predicted correctly.