The objectives of this study were to expand and implement a Computational Fluid Dynamics (CFD)-Dissolution, Absorption and Clearance (DAC)-Pharmacokinetics (PK) multi-physics modeling framework for simulating the transport of suspension-based nasal corticosteroid sprays. The mean CFD-predicted peak plasma concentration (Cmax) and area under the curve (AUC) of the plasma concentration-time profile, based on three representative nasal airway models (capturing low, medium and high posterior spray deposition), were within one standard deviation of available in vivo PK data for a representative corticosteroid drug (triamcinolone acetonide). The relative differences in mean Cmax between predictions and in vivo data for low dose (110 µg) and high dose (220 µg) cases were 27.8 % and 10.1 %, respectively. The models confirmed the dose-dependent dissolution-limited behavior of nasally delivered triamcinolone acetonide observed in available in vivo data. The total uptake from the nasal cavity decreased from 68.3 % to 51.3 % for the medium deposition model as dose was increased from 110 to 220 µg due to concentration-limited dissolution. The modeling framework is envisioned to facilitate faster development and testing of generic locally acting suspension nasal spray products due to its ability to predict the impact of differences in spray characteristics and patient use parameters on systemic PK.

The cochlea, situated within the inner ear, is a spiral-shaped, liquid-filled organ responsible for hearing. The physiological significance of its shape remains uncertain. Previous research has scarcely addressed the occurrence of transverse flow within the cochlea, particularly in relation to its unique shape. This study aims to investigate the impact of the geometric features of the cochlea on fluid dynamics by characterizing transverse flow induced by harmonically oscillating axial flow in square ducts with curvature and torsion resembling human cochlear anatomy. We examined four geometries to investigate curvature and torsion effects on axial and transverse flow components. Twelve frequencies from 0.125 Hz to 256 Hz were studied, covering infrasound and low-frequency hearing, with mean inlet velocity amplitudes representing levels expected for normal conversation or louder situations. Our simulations show that torsion contributes significantly to transverse flow in unsteady conditions, and that its contribution increases with increasing oscillation frequency. Curvature alone has a small effect on transverse flow strength, which decreases rapidly with increasing frequency. Strikingly, the combined effect of curvature and torsion on transverse flow is greater than expected from a simple superposition of the two effects, especially when the relative contribution of curvature alone becomes negligible. These findings may be relevant to understanding physiological processes in the cochlea, including metabolite transport and wall shear stress. Further studies are needed to investigate possible implications for cochlear mechanics.

UNASSIGNED: To characterize the gas production phenomenon in the animal model of left ventricular assist device (LVAD), and study its mechanism.
UNASSIGNED: An in vitro bubble precipitation experiment was conducted, and the blood samples of Parma spp. animals were divided into ordinary group and oxygen-enriched group according to whether they were oxygenated or not at the time of blood collection, and a static control group was set up respectively. Blood gases were drawn and analyzed before and after the experiment. Activate the pump, and the number of air bubbles in the loop was measured by ultrasound at different rotational speeds; CFD was applied to simulate the flow field in the blood pump, and pressure, fluid velocity vector and shear force diagrams were plotted, and a thrombus model was constructed and the flow field was simulated and plotted as a cloud diagram.
UNASSIGNED: There was a statistical difference in the number of bubbles in the inflow and outflow tubes of the blood pump (P values of 0.04 and 0.023, respectively), and the number of bubbles in the outflow tubes of both groups was significantly higher than the number of bubbles in the inflow tubes. The number of bubbles in the tubes of both the oxygen-enriched and normal groups was significantly higher than that in the inflow group. In both the normal and oxygen-enriched groups, more gas was produced at higher speeds than at lower speeds. Blood gas analysis showed that the reduced gas composition in the blood was mainly oxygen. Flow field simulation results: the high rotation speed group had lower central pressure and greater scalar shear. The thrombus simulation group was more prone to turbulence, sudden pressure changes, and greater shear than the normal group.
UNASSIGNED: Blood gas production is associated with higher partial pressures of blood oxygen, higher rotation speed, and intrapump thrombosis, and the mechanism of pump gas production is degassing of dissolved gases rather than cavitation of water, and the gas released is most likely to have oxygen. The degassing phenomenon is an warning factor for pump thrombosis.

UNASSIGNED: To review the existing literature on the application of computational fluid dynamics methods to study nasal particle deposition and to summarize and analyze the factors affecting nasal particle deposition in order to provide theoretical references for the development of future transnasal drug delivery devices and the prevention of respiratory-related diseases.
UNASSIGNED: PubMed and CNKI databases.
UNASSIGNED: A search of all current literature (up to and including February 2023) was conducted. Search terms related to the topic of factors influencing nasal particle deposition were identified, and queries were conducted to identify relevant articles.
UNASSIGNED: Both the properties of the particles themselves and the environmental conditions external to the particles can affect particle deposition in the nasal cavity, with particle deposition showing a positive correlation with particle size, particle density, and airflow velocity, with increasing subject age leading to a decrease in deposition, and with the relationship between airflow temperature and humidity still requiring more research to further explore.
UNASSIGNED: With the popularity of computational fluid dynamics, more and more scholars have applied computational fluid dynamics technology to explore the influence of different parameters on particle deposition. By summarizing and analyzing the influence law of various factors on deposition, it can provide a theoretical basis for the future development and application of transnasal drug delivery devices and the prevention of respiratory-related diseases, which makes a significant contribution to the optimization of clinical disease prevention and treatment.
UNASSIGNED: NA.

In this work we present a novel methodology for the numerical simulation of patient-specific aortic dissections. Our proposal, which targets the seamless virtual prototyping of customized scenarios, combines an innovative two-step segmentation procedure with a CutFEM technique capable of dealing with thin-walled bodies such as the intimal flap. First, we generate the fluid mesh from the outer aortic wall disregarding the intimal flap, similarly to what would be done in a healthy aorta. Second, we create a surface mesh from the approximate midline of the intimal flap. This approach allows us to decouple the segmentation of the fluid volume from that of the intimal flap, thereby bypassing the need to create a volumetric mesh around a thin-walled body, an operation widely known to be complex and error-prone. Once the two meshes are obtained, the original configuration of the dissection into true and false lumen is recovered by embedding the surface mesh into the volumetric one and calculating a level set function that implicitly represents the intimal flap in terms of the volumetric mesh entities. We then leverage the capabilities of unfitted mesh methods, specifically relying on a CutFEM technique tailored for thin-walled bodies, to impose the wall boundary conditions over the embedded intimal flap. We tested the method by simulating the flow in four patient-specific aortic dissections, all involving intricate geometrical patterns. In all cases, the preprocess is greatly simplified with no impact on the computational times. Additionally, the obtained results are consistent with clinical evidence and previous research.

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it\'s essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Nasal airflow obstruction correlates with several ailments, such as higher patency, increased friction at the mucosal wall or the so-called Little\'s area, improper air conditioning, and snoring. Nasal dilators are frequently employed, mainly due to their ease of access and use, combined with their non-permanent and non-surgical nature. Their overall efficacy, however, has not been clearly demonstrated so far, with some studies reporting conflicting outcomes, mainly because being based on subjective evaluations. This study employs Computational Fluid Dynamics simulations to analyze the flow inside a real nose, performs an objective assessment of a nasal dilator\'s effect in terms of airflow and air conditioning, reporting flow paths, friction levels, heat and water fluxes and detailed temperature and humidity distributions. Coincidentally, the studied nose presents a septal deviation, with one nostril being wider than the other. The tubes of the dilator used in both nostrils are identical, as with any standard commercial dilator. Consequently, the dilator widens one nostril, as intended, but results in an obstruction in the other. This allows simultaneously addressing two situations, the nominal function of the dilator, as well as an off-design case. Results indicate a 24 % increase in nasal patency in the design situation. The effect, however, is limited, as quantified by appropriate measures, such as the flow-generated friction at the nose surfaces and the temperature fluxes. Hence, the effect of such a dilator in nominal conditions is perhaps not as large as might be hoped. In the off-design situation, nasal resistance increases by 62 %, an undesirable effect, illustrating the consequences of using an inappropriate dilator.

Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters like fractional flow reserve computed tomography simulation (FFRCTS), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis.

OBJECTIVE: To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms.
METHODS: We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets.
RESULTS: The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000).
CONCLUSIONS: Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.