Physics of Fluids

The Next Generation Impactor (NGI) is one of the regulatory gold standards for characterizing aerodynamic particle size distributions (APSDs) of orally inhaled drug products (OIDPs); however, its reliance on complex, resource-intensive in vitro testing under tightly controlled environmental conditions limits experimental flexibility and introduces variability. In alignment with the growing regulatory emphasis on New Approach Methodologies (NAMs) for drug development, this study presents a rigorously validated computational fluid particle dynamics (CFPD) based virtual NGI (vNGI) as an in silico method complementary to conventional testing. The vNGI replicates a significant portion of the NGI geometry and airflow physics, enabling high-resolution spatiotemporal analysis of aerosol transport and deposition mechanisms that are otherwise inaccessible experimentally. A comprehensive verification and validation framework was implemented, including mesh and particle independence studies, turbulence model assessment, and comparison of stagewise deposition efficiencies with available in vitro data at 30 L/min. The model's capabilities were further extended to low and high flow rates, and two bio-relevant mouth-throat models and polydisperse particle laden aerosol were added. The model demonstrates strong predictive capability for a few stages and provides mechanistic insight into discrepancies in other stages, depending on the type of analysis. Importantly, this work establishes the vNGI as a fit-for-purpose according to NAM by (i) defining a clear context of use for APSD prediction and inhaler performance evaluation, (ii) capturing physically and biologically relevant air-particle interactions, and (iii) demonstrating technical robustness and reproducibility through systematic validation. The platform can potentially further enable simulation of environmental and physiological conditions, such as humidity effects, that are difficult to control experimentally, thereby improving human relevance and reducing reliance on costly and time-consuming in vitro testing. This study positions the vNGI as a scalable, regulatory aligned NAM capable of supporting early stage drug device combination product development, device optimization, and an alternative bioequivalence assessment, contributing to ongoing efforts to enhance predictive performance, reduce experimental burden, and transition toward human centric, inhalation product evaluation.

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

Snake venom composition and its contribution to toxic effects has been heavily researched, but there is a comparative lack of information on venoms fluidic properties and their relationship with fang morphology during the envenomation process. Understanding how venom flows through a fang can shed light on bite site dynamics and potentially explain bite symptoms. In this article we first conduct a broad comparative test of the rheological properties of venom from thirteen snake species, including multiple viperid and elapid snake species, revealing a shear-thinning non-Newtonian flow behavior in all studied species. However, we have not observed strong phylogenetic signal in venom fluidic properties, suggesting that flow properties may vary independently of evolutionary relationships between snake species. Second, we demonstrate that snake venoms fluidic properties can be modeled by other inexpensive, safe, and abundant shear-thinning surrogate fluids. We found that aqueous solutions of bovine serum albumin protein and xanthan gum are useful venom mimics, matching the rheological behavior of venoms from the studied snake species across a range of relevant shear rates. We further evaluated the performance of these snake venom mimics in a simulated venom delivery system, showing good and robust mimetic control of the flow properties as a function of applied pressure. By elucidating the fluidic properties of snake venom and providing a non-toxic, scalable surrogate fluid model to be used in further studies, we provide the biomedical, toxicology, evolutionary biology communities with a tool to study envenomation physics in an inexpensive and safe fashion. We suggest it is possible to design species-specific venom mimics that facilitate research on the biomechanics and fluid dynamics of venom delivery via snake bites, and inform the design of bioinspired puncture and injection devices.

The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic do-decahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

An e-GLUE is a polymer network containing interpenetrating polycations, which can bond the anionic network of mucosa through interfacial polycation bridging under an electric field. Such an electroadhesion involves electrophoresis of polycations, ionic complexation between polycations and the anionic network, and polycation-network entanglement, yet their quantitative understanding is lacking. Here, we formulate a theoretical model to describe electroadhesion of polymer networks by polycation interfacial bridging. We use a diffusion-drift model coupled with a Bell-like field-dependent chain friction to describe the sticky electrophoresis of polycations in an anionic sea. The formation of ionic bonds is determined by local availability of cations and anions over the penetration depth. To debond, a force must either pull polycations out from the e-GLUE network or first dissociate them from ionic complexes and then pull out from the anionic network. We model chain pullout from the bulk networks to the interface as a viscous drag against water. The adhesion strength is calculated by summing the debonding force for each polycation per unit area across all chains. Our model quantitatively links electric field strength, applied duration, polycation chain length, and cation concentration to polycation electrophoresis kinetics, ionic bond formation, and adhesion strength. We further conduct electroadhesion tests, and our model predicts well with the experimental data. Lastly, we discuss the use of the model to guide the e-GLUE design. TOC graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/730541v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@16524c6org.highwire.dtl.DTLVardef@15163aeorg.highwire.dtl.DTLVardef@673949org.highwire.dtl.DTLVardef@e207a0_HPS_FORMAT_FIGEXP M_FIG C_FIG For Table of Contents use only

The commercial maturation of microfluidics remains bottlenecked by empirical prototyping and an absence of predictive digital design capabilities. Because optimizing advanced technologies such as passive particle separation fundamentally hinges on the precise coupling of fluid dynamics and particle mechanics, conventional two-dimensional or decoupled fluid simulations inherently fail to capture authentic multiscale behaviors. To bridge this gap, we establish a high-fidelity three-dimensional fluid-structure interaction framework combining a high-order Arbitrary Lagrangian-Eulerian mapping-based finite element method with a localized hierarchical dynamic mesh strategy. Engineered to accurately resolve complex multiscale hydrodynamics, this architecture utilizes deterministic lateral displacement structures as a stringent test case. Validated against experimental data for rigid microspheres and tumor cells, the framework predicts transport trajectories and critical separation diameters with sub-micron precision. Crucially, the simulation explicitly resolves the M-shaped spatial fluctuation of local size thresholds alongside the dynamic vertical migration of particles. Unveiling these hidden physical mechanisms provides a deterministic explanation for highly debated phenomena such as mixed-mode transport. By enabling the rigorous in silico evaluation of complex non-periodic architectures, this framework serves as a powerful instrument for predictive structural optimization. Such capabilities establish the essential infrastructure for microfluidic digital design, accelerating the transition from empirical trial-and-error to precision simulation-driven engineering.

Targeted drug delivery offers a promising approach for personalized medicine in treating vascular stenosis. However, biomechanical constraints, such as drug washout by high-velocity central blood flow and unintended absorption by healthy vascular walls, complicate the determination of optimal dosing locations. Conventional three-dimensional computational fluid dynamics (CFD) provides precise flow analysis but incurs prohibitive computational costs, making long-term tracking of plaque growth and reverse-engineering of optimal delivery highly inefficient. In this study, we propose a pseudo-3D stochastic growth model that dramatically reduces computational load while capturing the essential dynamics of plaque progression and regression. By modeling the advection-diffusion of lipid and drug particles as a discrete Markov process within a Stokes flow field, we simulate the morphological evolution of plaques under continuous and interrupted targeted therapies. Furthermore, by formulating the drug transport process as an absorbing Markov chain with boundaries at the healthy walls and vessel outlet, we calculate the exact reaching probability and mean first passage time (MFPT) to the plaque. Based on these probability distributions, we discover continuous "Optimal Dosing Curves", which indicate the most effective spatial coordinates for catheter-based drug release to maximize therapeutic efficacy. This mathematical framework not only elucidates the stochastic nature of vascular plaque dynamics but also provides a scalable, computationally efficient foundation for optimizing targeted drug delivery in personalized medicine.

To explore the safety of combined use of lidocaine/prilocaine aerosol and condoms of different materials, this study conducted compatibility tests between them. By observing changes in various physical properties of condom materials after exposure to the aerosol, the compatibility of different polymer materials with the aerosol was analyzed.The results showed that within 15 minutes of exposure to the aerosol, there was no significant difference in all physical properties of natural rubber latex condoms compared with the blank control group (P>0.05), indicating they can be used together. In contrast, obvious changes in physical properties of polyurethane condoms occurred within 5 minutes of exposure (P<0.05), and their performances failed to meet industrial application standards, so combined use is strictly prohibited.This study clarifies the compatibility differences between two mainstream condom materials and lidocaine/prilocaine aerosol, providing experimental evidence and theoretical references for rational matching in clinical and daily use as well as avoiding potential safety risks.

The endotracheal tube resistance dominates the total airway resistance in most intubated patients. Mucus deposition and biofilm formation can rapidly increase tube resistance and thereby contribute to serious ventilatory impairments, including dynamic hyperinflation, intrinsic PEEP build-up, added work of breathing, and patient-ventilator asynchrony. During controlled mechanical ventilation, an increased tube resistance can be inferred from the difference between peak and plateau pressure, but this approach fails during pressure-supported spontaneous breathing. Here, we present a method that estimates the linear and nonlinear components of tube resistance from naturally occurring airway pressure and flow fluctuations at the airway opening, without a tracheal pressure sensor and without applying mandatory forced oscillations. This is achieved by solving the equation of motion using band-pass filtered airway pressure and flow signals. Band-pass filtering isolates the relevant resistive and inertive pressure losses across the tube by removing slow contributions from muscle pressure and lung elastance as well as high-frequency noise. The method accurately recovers both linear and nonlinear tube resistance parameters with < 10% error and < 2% bias. Moreover, it enables real-time implementation of full Automatic Tube Compensation (ATC), even in the presence of severe tube obstructions. Continuous estimation of endotracheal tube resistance from naturally occurring airway pressure and flow fluctuations enables real-time detection of clinically relevant tube narrowing and may help improve patient safety, reduce patient-ventilator asynchrony, and facilitate weaning.

Staphylococcus aureus (S. aureus) is the most common pathogen associated with skin infections worldwide. Significant efforts have been made to identify and develop innovative therapeutic strategies against S. aureus as alternatives to conventional antibiotics. Physical plasma has a broad range of potential uses, with non-destructive disinfection being one of its earliest applications. Although the literature emphasizes the antibacterial properties of cold atmospheric plasma (CAP), the effect of plasma on S. aureus on damaged skin susceptible to S. aureus invasion through the itch-scratch cycle has not been studied to date. Thus, we examined the effectiveness of CAP treatment on S. aureus bacteria in atopic dermatitis lesions using floating electrode dielectric barrier discharge devices, as well as helium and argon plasma jets. Heat distribution on the skin target, ultraviolet C radiation, and ozone generation of plasma jets for the operator of plasma sources were evaluated. Microbial tests confirmed the presence of S. aureus on the lesions of the groups before treatment. The groups exposed to plasma treatment showed a notable reduction in bacterial population compared to the model group (p<0.05). Furthermore, our investigation indicated that plasma treatment reduced pruritus behavior. The findings suggest that cold atmospheric plasma treatment may potentially target skin infections caused by S. aureus in addition to conventional therapies.

Background: Automatic tube compensation (ATC) was designed to compensate for the additional resistive load imposed by the endotracheal tube during spontaneous breathing. In ATC mode, the ventilator adds or subtracts the flow-dependent pressure drop across the tube during both inspiration and expiration so that tracheal pressure remains close to PEEP. Early prototype ventilators achieved true tracheal-pressure control and showed physiological and clinical benefits, but clinical studies with commercial systems have failed to confirm these earlier findings. A 2003 bench study found that commercial ventilators provided, at best, only partial tube compensation, unlikely to result in meaningful clinical benefit. We therefore tested whether this limitation has been remedied in contemporary ICU ventilators. Methods: We performed a bench comparison of five commercial ICU ventilators and an ATC prototype ventilator designed to accurately compensate for the flow-dependent resistance over a wide range of flow rates. An active lung simulator generated spontaneous breathing patterns with weak, moderate, and strong inspiratory efforts at different PEEP levels. We tested each breathing pattern through endotracheal tubes with inner diameters of 7 and 8 mm, and measured airway pressure, tracheal pressure, and flow during CPAP with and without ATC. Breathing through the tube against open atmosphere served as a zero-PEEP/T-piece reference. Results: In CPAP mode, the commercial ventilators showed flow-dependent airway-pressure deviations, amounting to a substantial added resistance of 1.5 - 6.5 mbar/(L/s), whereas the ATC prototype ventilator imposed an added resistance of only 0.6 mbar/(L/s). In ATC mode, the commercial ventilators reduced the resistive load by no more than by 25%, and large tracheal-pressure deviations remained, especially at higher inspiratory effort and during expiration. In some cases, the residual load during ATC was even greater than the load during unsupported breathing through the tube. By contrast, the ATC prototype ventilator maintained tracheal pressure close to PEEP throughout the breathing cycle and eliminated on average 79% of the tube-related resistive load. Conclusions: In the commercial ventilators evaluated in this study, the defining physiological objective of ATC was only partially achieved. Therefore, clinical benefits previously reported for tracheal-pressure control support should be interpreted with caution when applied to commercial ATC implementations, unless effective tube compensation has been demonstrated under relevant conditions. These findings suggest that more advanced control approaches, such as those implemented in the ATC prototype ventilator, may be required to achieve consistent and physiologically accurate tube compensation.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Tissue phantoms that mimic microvasculature and perfusion are essential for modelling vascular function, guiding interventions, and calibrating imaging systems, which require faithful replication of vascular geometry and flow. Conventional fabrication strategies, including wire-based molding, lithographic micromachining, and additive manufacturing, offer useful capabilities but remain constrained by predefined designs, rectangular channel cross-sections, limited scalability, and high production costs. Reliance on predefined digital vascular models restricts design flexibility and limits the ability to capture the natural variability and complexity of real vascular systems. Here, we present a lithography-free, fractal-generating approach based on a modified Lifted Hele-Shaw Cell (LHSC) technique, in which vascular networks emerge spontaneously via interfacial fluid instabilities. Unlike pre-designed methods, these structures are governed by fluid properties and flow conditions, enabling adaptive, physiologically relevant geometries with smooth Gaussian cross-sections and natural diameter tapering. We demonstrate four phantom designs: a planar vascular tree, an anatomically guided cerebral network, a retinal vascular model, and a conformable curved substrate phantom. Validation using Laser Speckle Contrast Imaging confirms structural fidelity and physiologically relevant flow consistent with Murrays law. This platform uniquely integrates realistic vascular architecture with emergent, fractal driven formation, highlighting its potential as a reproducible and biologically relevant alternative to conventional vascular phantom fabrication. Furthermore, the availability of such realistic in vitro vascular models can reduce reliance on animal experiments and contribute towards more ethical and sustainable preclinical research.

Mathematical descriptions of drug release from polymeric nanocapsules are commonly based on first-order kinetics derived from the Noyes-Whitney equation. However, previous formulations implicitly predict that increasing drug solubility in the oily core accelerates release, which contradicts experimental evidence. In this work, we revisit the modeling framework and derive a physically consistent equation based on diffusion through the polymeric shell coupled with partition equilibrium at the oil-water interface. The resulting model shows that the effective release rate is inversely proportional to solubility. This formulation resolves the apparent paradox, preserves the experimentally observed exponential saturation behavior, and collapses kinetic data into a single intrinsic parameter. We further demonstrate that the only prior model proposed specifically for nanocapsular systems [1] also embeds the solubility paradox, and show that its own published validation data in fact confirm the inverse-solubility scaling derived here, with a deviation of only 9% between two independent formulations. Dimensional consistency, comparison with classical and nanoscale-specific models, and validation using published data support the robustness of the proposed approach.

A wide spectrum of microparticles is inhaled with each breath, deposited on airway surfaces, entrapped in the mucus, and removed by mucociliary transport (MCT). However, the influence of particle size on MCT remains largely unknown. Here, we investigated the MCT of microparticles with a trachea-on-a-chip method that integrates a micro-machined device with a trachea explant from newborn pigs. This method preserves airway structures for mucus secretion and cilia beating (e.g., airway surface epithelia and submucosal glands), maintains physiological air-liquid-interface on the airway surface, and allows tracks motion of microparticles with high resolution. Using this method, we found that, in normal airways, 6 um polystyrene particles clear rapidly, whereas 102 um particles clear slower and require mucus strands for motion. In cystic fibrosis (CF) airways, MCT of microparticles reduces, but particle size-dependence persists. Methacholine increases particle motion in normal airways, but not in CF airways. These findings suggest two distinct MCT processes, in which large particles rely on mucus strands for clearance, small particles can be cleared independent of mucus strands, and CF disrupts both.

The endothelial cells (ECs) that line blood vessels continuously sense and respond to the physical forces exerted by blood flow. In vivo, pulsatile arterial flow interacts with vessel curvature, branching and other anatomical features to generate complex local hemodynamic environments that dictate the magnitude, direction, pulsatility, and oscillatory nature of wall shear stress experienced by ECs. Currently, accessible and reproducible in vitro models of complex pulsatile flow that recapitulate in vivo vascular anatomy remain limited. Here, we combine a novel rotational-flow endothelial culture platform with detailed computational fluid dynamics (CFD) modeling to characterize four well geometries designed to generate distinct hemodynamic environments. CFD analyses demonstrate that these geometries intrinsically generate pulsatile flow and produce reproducible spatially distinct regions of wall shear stress magnitude, pulsatility, and oscillatory shear within a single culture well. Endothelial alignment mapping and functional assays reveal region-specific cellular responses to the predicted local flow conditions that closely corresponded to the predicted local hemodynamic environment, linking complex flow patterns to endothelial adaptation. The technical advancements of our modeling efforts should support a faster, cheaper, simpler, and--importantly--validated framework for future investigation into EC mechanobiology under complex flow conditions. HIGHLIGHTSO_LISimple engineered well geometries generate distinct hemodynamic microenvironments, mimicking in vivo vascular structures, using a conventional orbital shaker. C_LIO_LIComputational fluid dynamics (CFD) reveals spatially distinct patterns of wall shear stress, pulsatility, and oscillatory shear applied to ECs within individual culture wells. C_LIO_LIHigh average wall shear stress and elevated oscillatory shear index induces a unique perpendicular alignment of ECs to the dominant flow vector. C_LI

Conventional air filtration relies on passive mechanical capture without pathogen inactivation, where viral reduction must be balanced with airflow and energy performance. We developed an Ablative Polymer Coated (APC) filtration system that converts passive filters into active pathogen-reducing surfaces while maintaining low airflow resistance. Unlike conventional approaches requiring denser, higher-resistance media, this strategy enhances biological performance at the filter surface without equivalent aerodynamic penalties. The coating incorporates benzalkonium chloride within a polyvinyl acetate/acrylate matrix for controlled ablative exposure. Performance was evaluated using transmission electron microscopy (TEM), aerosol challenge testing, and HVAC-scale filtration. Ablative exposure caused progressive structural disruption of MS2 bacteriophage, the SARS-CoV-2 simulant. In aerosol challenge testing, coated media achieved up to 99.997% viral filtration efficiency under respiratory airflow conditions. In HVAC (Heating, Ventilation, and Air Conditioning)-scale testing, coated filters achieved >85% viral filtration efficiency with minimal pressure-drop increase. Computational fluid dynamics modeling confirmed uniform airflow distribution without significant turbulence generation. Energy analysis suggested coated filters may reduce energy demand relative to conventional higher-resistance configurations while improving biological performance. These findings support ablative polymer-coated media as a strategy for reducing airborne viral burden without aerodynamic penalties of higher-efficiency passive filtration, suggesting an approach that complements rather than depends solely on tighter filter design.

Respiratory virus transmission occurs in indoor settings where ventilation, occupancy, and dwell time determine exposure levels. Improving indoor air quality (IAQ) therefore could help reduce disease burden associated with respiratory viruses, yet its population-level impact remains poorly quantified. Here, we develop an individual-based transmission modelling framework that links within-location airborne dynamics to individual infection risk and population-level spread, whilst explicitly incorporating heterogeneity in ventilation and baseline indoor air quality across locations. We use this modelling approach to evaluate IAQ-improving interventions (air-quality interventions or AQIs), using hypothetical endemic and pandemic pathogen archetypes with properties similar to SARS-CoV-2 and influenza, and evaluate how effects on key epidemiological metrics (such as annualized incidence and epidemic final size) depend on AQI coverage, efficacy and allocation strategy. At 20% AQI intervention coverage and 80% efficacy, annualized incidence was reduced by approximately 7.2% for an endemic 'SARS-CoV-2-like' respiratory virus, and 17.0% for an endemic 'influenza-like' virus; at 60% coverage (80% efficacy) the reductions were 26.3% and 56.4%, respectively. Targeting AQI installation to the highest-risk locations outperformed random allocation: for SARS-CoV-2-like transmission, 20% coverage at 80% efficacy cut absolute incidence by 10.8% when targeted versus 7.2% when random; for influenza-like transmission, this comparison was 28.9% versus 17.0%. In epidemic scenarios, random installation at 40% coverage and 60% efficacy reduced final size by 23.7% (influenza-like) versus 6.3% (SARS-CoV-2-like). These results support treating clean indoor air as core public-health infrastructure and prioritising risk-based deployment of IAQ-improving interventions to maximise population-level benefit within budgetary and operational constraints.

With the increasing prevalence of subcutaneous administration of peptides, understanding their release and absorption is key to designing formulations with desired pharmacokinetics. Though the absorption of monoclonal anti-bodies (mAbs) has been widely explored through computational modeling, that of peptides remains poorly understood, as key features of peptide absorption, including concentration-dependent oligomerization and reversible binding with serum albumin and extracellular matrix, have not been captured. In this work, we present a first-of-its-kind approach to simulating subcutaneous administration of peptides that couples a high-fidelity tissue-level poroelastic model with a systemic compartment pharmacokinetic model. While accounting for competing binding and oligomerization tendencies of peptides, the model not only captures the process of injection but also tracks the absorption over subsequent days. We demonstrate the model using a single-dose administration of semaglutide and validate it against experimentally observed pharmacokinetic parameters. The results show the distribution of the different forms of the injected peptide throughout the body and describe the role of binding in sustaining its release. The model also reveals novel mechanisms, such as albumin-bound monomers enveloping the plume and the balance of oligomerization and binding in early stages of peptide absorption.

Computational analysis of physiological and biomedical systems necessitate efficient geometry representations for high fidelity model predictions, including patient or device specificity. Particle-based Lagrangian computational approaches comprise a valuable approach to gain insights from quantitative velocity and pressure data from computational models. Examples include particle dynamics and transport in human vasculature for diseases such as stroke, thrombosis, and embolisms; and modern targeted drug delivery systems in the vascular network and respiratory airways. However, current particle simulation approaches can bear significant computational expense that scales with both number of particles and background fluid mesh resolution. A significant determinant of this computational expense is the contact resolution between particles and anatomically realistic vessel wall. Here, we develop an efficient particle dynamics model that leverages an implicit representation of real anatomical features using a signed distance field to efficiently resolve particle-wall contact. We outline the underlying algorithmic details, followed by a systematic illustration of performance and accuracy using simplified and analytically defined geometries and flow fields. Subsequently, we present a representative simulation of embolic particles along a human vascular segment where we compare our distance field-based approach against classical wall-contact checks based on assessing particle boundary intersection with triangulated surface mesh. Our approach transforms the underlying Lagrangian contact detection operation into an equivalent Eulerian operation, significantly speeding up bulk particle dynamics computations without significantly impacting accuracy or geometric fidelity.