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.

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.

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.

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.

We report recent advances in the development of a computationally efficient and accurate timedependent RANS-based (T-RANS) method for predicting heavy gas turbulent dispersion in complex urban areas. We analyze two cases: (i) the simplified Kit Fox setup at the field scale, and (ii) a complex urban area (the city of Beijing). The present approach, which models the two-way coupled transport of turbulent airflow and heavy gas concentration, combined with a passive-element method for representing obstacles and buildings, has proven to be both numerically efficient and accurate in predicting the temporal evolution of local concentrations.

We present recent results from large eddy simulations (LES) of bounded, turbulent, double-diffusive convection in seawater, focusing on small-aspect-ratio domains (2:2:1) across an extensive parameter range. We focus on presenting the main instantaneous and time-averaged features of flow, heat, and concentration fields. In addition to analyzing the power spectral density (PSD) and probability density function (PDF) at characteristic locations within the simulated domain, special attention is devoted to a detailed analysis of the budgets of the governing second-order correlations for different strengths of the imposed stable thermal stratification gradient.

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.

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.

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 decisiontree-based 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.

Thoracic aortic aneurysm (TAA) is a local dilation of the thoracic aorta. Although universally used, aneurysm diameter alone is a poor predictor of major complications such as rupture. There is a need for better biomarkers for risk assessment that also reflect the aberrant flow patterns found in TAAs. Furthermore, hypertension is often present in TAA patients and may play a role in progression of aneurysm. The exact relation between TAAs and hypertension is poorly understood. This study aims to create a numerical model of hypertension in the aorta by using computational fluid dynamics. First, a normotensive state was simulated in which flow and resistance were kept unaltered. Second, a hypertensive state was modeled in which blood inflow was increased by 30%. Third, a hypertensive state was modeled in which the proximal and peripheral resistances and capacitance parameters from the three-element Windkessel boundary condition were adjusted to mimic an increase in resistance of the rest of the cardiovascular system. One patient with degenerative TAA and one healthy control were successfully simulated at hypertensive states and were extensively analyzed. Furthermore, three additional TAA patients and controls were simulated to validate our method. Hemodynamic variables such as wall shear stress, oscillatory shear index, endothelial cell activation potential (ECAP), vorticity and helicity were studied to gain more insight on the effects of hypertension on flow patterns in TAAs. By comparing a TAA patient and a control at normotensive state at peak-systole, helicity and vorticity were found to be lower in the TAA patient throughout the entire domain. No major changes in flow and flow derived quantities were observed for the TAA patient and control when resistance was increased. When flow rate was increased, regions with high ECAP values were found to reduce in TAA patients in the aneurysm region which could reduce the risk of thrombogenesis. Thus, it may be important to assess cardiac output in patients with TAA.

The turbulent penetrative convection into a stable convective boundary layer represents an important phenomenon in environmental engineering and atmospheric science. In the present study, we present a series of numerical simulations performed by two modeling approaches: the high-fidelity Large-Eddy Simulations (LES), and the less computationally demanding transient Reynolds-Averaged Approach (TRANS), but with an advanced sub-scale turbulent heat flux model. By simulating different localized heat sources over the ground, and by performing a direct comparative assessment of results obtained by LES and TRANS, we confirmed an overall good agreement in predicting the time evolution of the horizontally averaged temperature profiles. Similarly, the morphology of instantaneous thermal plumes and large convective structures predicted by TRANS were in reasonable agreement with the referent LES predictions.

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.

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.