Modeling Fluid Flow in Complex Biological Systems: Implications for Dense Tumor Microenvironments, Metastasis, and Bone Cancer

The transport of biofluids in physiological systems plays a critical role in understanding and optimizing therapeutic interventions for diseases such as cancer and respiratory infections. This dissertation focuses on developing and validating a comprehensive computational framework for studying multiphase transport in complex biological environments. The research emphasizes the use of Computational Fluid Dynamics (CFD) to simulate and analyze plasma perfusion in solid tumors, particle deposition in respiratory airways, and fluid transport in cancer metastasis and bone cancer systems. By addressing the challenges of modeling biological transport phenomena, this work provides valuable insights into therapeutic planning and experimental validation. The study begins with the development of a biomimetic model for tumor perfusion, incorporating electrohydrodynamic (EHD) effects to enhance the realism and accuracy of blood flow simulations. These models simulate the transport of plasma and cellular components through the complex tumor microenvironment, characterized by abnormal vasculature and heterogeneous interstitial flow resistance. Validation is achieved by comparing numerical predictions with experimental data derived from microfluidic tumor spheroids, demonstrating the reliability of the computational approach for predicting perfusion trends and drug delivery outcomes. The research extends to modeling cancer metastasis in bioreactor systems and bone cancer progression, where multiphase CFD simulations provide insights into fluid and particle xxi transport dynamics in diverse biological conditions. These applications highlight the versatility of the computational framework in addressing different aspects of cancer modeling, including metastasis trends and drug delivery pathways. The integration of anatomical accuracy into the models enables clinically relevant predictions that bridge computational simulations with experimental observations. In addition, this dissertation investigates particle deposition and escape in the nasopharyngeal region, focusing on droplet transport under varying flow conditions. Using anatomically accurate airway reconstructions, CFD simulations reveal the influence of particle size, flow rates, and airway geometry on deposition efficiency and escape probabilities. The findings provide a detailed understanding of biofluid transport in the upper respiratory system, with implications for drug delivery and respiratory disease modeling. Overall, this dissertation advances the field of biofluid mechanics by integrating CFD-based modeling with experimental validation to address challenges in cancer research and respiratory diagnostics. The research outcomes contribute to the development of predictive tools for tumor perfusion analysis, metastasis modeling, and particle transport in airways, offering pathways for improved therapeutic interventions and experimental designs

Modeling of Transport in Anatomic Respiratory Airways: Applications in Targeted Drug Delivery and Airborne Pathogenic Transmissions

This thesis aims to explore the potential of improving the efficacy of drugs for treatment of viral infections by targeting the nasopharynx, which is commonly the first site of infection for many viral pathogens. Currently, intranasal sprays are used, but the standard protocol (“Current Use” or CU) results in suboptimal drug deposition at the nasopharynx. To address this issue, an “Improved Use” or, IU protocol has been proposed, which involves pointing the spray bottle at a shallower angle and aiming slightly towards the cheeks. The IU delivery is also robust to perturbations in spray direction, which highlights the practical utility of this new drug administration protocol. The results of the simulation are experimentally verified using a 3D-printed airway cavity of a different subject. Next with the smallpox virus as an example pathogen, a numerical modeling framework for airborne respiratory diseases has been made. This modeling framework shows that the regional deposition of virus-laden inhaled droplets at the initial infection site (for smallpox, this is the oropharynx and the lungs) peaks for the droplet size range (8–27 μm for oropharyngeal deposition, and ≤ 14 μm for lungs) and can be used to determine the number of virions required to launch the infection in a subject. Subsequently, to explore the mechanics of lower airway disease progression, we have considered SARS-CoV-2. We have investigated the spread of SARS-CoV-2 from the nasopharynx to the lower airway. Using computational models, the inhalation process has been tracked with quantification for the volume of nasopharyngeal liquid transmitted to the lower airspace during each aspiration. The results suggest that a significant amount of liquid may be aspirated each day, which could lead to an increased risk of aggressive and accelerated lung infections in individuals with conditions like dysphagia. Finally, in view of the high cost and time required for conducting numerous numerical simulations, we have checked Machine Learning platforms as an alternative method for predicting regional deposition at various anatomical regions based on the geometric features of the anatomic flow domains in respiratory physiology. As an ancillary topic, the thesis also explores the morphological characteristics of the nose and their influence on airflow patterns and heat transfer dynamics inside the nasal cavity of a pig’s nose. The findings indicate that tortuosity has a crucial role in particle capture efficiency, particularly in high-olfactory mammalian species such as pigs and opossums. Understanding the fluid-particle interactions in nasal cavities could lead to the development of nature-inspired designs for various engineering processes, such as the creation of novel filtration devices. Therefore, it is essential to continue investigating the significance of heat management and particle screening in nasal structures to reveal their mechanistic functions and translate this information into practical applications

A mechanistic model for smallpox transmission via inhaled aerosols inside respiratory pathways

Investigations on airborne transmission of pathogens constitute a rapidly expanding field, primarily focused on understanding the expulsion patterns of respiratory particulates from infected hosts and their dispersion in confined spaces. Largely overlooked has been the crucial role of fluid dynamics in guiding inhaled virus-laden particulates within the respiratory cavity, thereby directing the pathogens to the infection-prone upper airway sites. Here, we discuss a multi-scale approach for modeling the onset parameters of airway infection based on flow physics. The findings are backed by Large Eddy Simulations of inhaled airflow and computed trajectories of pathogen-bearing aerosols/droplets within two clinically healthy and anatomically realistic airway geometries reconstructed from computed tomography imaging. As a representative anisotropic pathogen that can transmit aerially, we have picked smallpox from the Poxviridae family to demonstrate the approach. The fluid dynamics findings on inhaled transmission trends are integrated with virological and epidemiological parameters for smallpox (e.g., viral concentration in host ejecta, physical properties of virions, and typical exposure durations) to establish the corresponding infectious dose (i.e., the number of virions potent enough to launch infection in an exposed subject) to be, at maximum, of the order of O(2), or more precisely 1 to 180. The projection agrees remarkably well with the known virological parameters for smallpox.Comment: 6 pages, 3 figure

Session 11: \u3cem\u3eCan machine learning predict particle deposition at specific intranasal regions based on computational fluid dynamics inputs/outputs and nasal geometry measurements?\u3c/em\u3e

Along with machine learning modeling, numerical simulations of respiratory airflow and particle transport can be used to improve targeted deposition at the upper respiratory infection site of numerous airborne diseases. Given the need for more patient data from varied demographics, we propose a machine learning-enabled protocol for determining optimal formulation design parameters that may match nasal spray device settings for successful drug delivery. We measured 11 anatomical parameters (including nasopharyngeal volume, nostril heights, and mid-nasal cavity volume) for 10 CT-based nasal geometries representative of the population for this aim. We also ran 160 computational fluid dynamics simulations of drug delivery on the same geometries for various breathing situations, using varied pressure gradients to drive inhaled air transport to evaluate drug deposition at the various upper airway areas for nasal inhalers. Using this test data, we constructed 18 machine-learning models to estimate the targeted deposition at the different regions of the upper airway. This study contributes to developing a customized, efficient intranasal delivery system for prophylactics, treatments, and immunizations; the findings will apply to a broad spectrum of respiratory disorders

On a model-based approach to improve intranasal spray targeting for respiratory viral infections

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17 μm are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude

On a model-based approach to improve intranasal spray targeting for respiratory viral infections

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17 μm are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude

Grey, blue, and green hydrogen: A comprehensive review of production methods and prospects for zero-emission energy

Energy is the linchpin for economic development despite its generation deficit worldwide. Hydrogen can be used as an alternative energy source to meet the requirement that it emits zero to near-zero impurities and is safe for the environment and humans. Because of growing greenhouse gas emissions and the fast-expanding usage of renewable energy sources in power production in recent years, interest in hydrogen is resurging. Hydrogen may be utilized as a renewable energy storage, stabilizing the entire power system and assisting in the decarbonization of the power system, particularly in the industrial and transportation sectors. The main goal of this study is to describe several methods of producing hydrogen based on the principal energy sources utilized. Moreover, the financial and ecological outcomes of three key hydrogen colors (gray, blue, and green) are discussed. Hydrogen’s future prosperity is heavily reliant on technology advancement and cost reductions, along with future objectives and related legislation. This research might be improved by developing new hydrogen production methods, novel hydrogen storage systems, infrastructure, and carbon-free hydrogen generation

Computational multiphase characterization of perfusion trends inside biomimetic reduced-order dense tumors

AbstractDense fibrous extracellular constitution of solid tumors exerts high resistance to diffusive transport into it; additionally, the scarcity of blood and lymphatic flows hinders convection. The complexity of fluidic transport mechanisms in such tumor environments still presents open questions with translational end goals. For example, clinical diagnosis and targeted drug delivery platforms for such dense tumors can ideally benefit from a quantitative framework on plasma uptake into the tumor. In this study, we present a computational model for physical parameters that may influence blood percolation and penetration into a simple biomimetic solid tumor geometry. The model implements 3-phase viscous laminar transient simulation to mimic the transport physics inside a tumor-adhering blood vessel and measures the constituent volume fractions of the three considered phases, viz. plasma, RBCs (Red Blood Cells, also known as “erythrocytes”), and WBCs (White Blood Cells, also known as “leukocytes”) at three different flow times, while simultaneously recording the plasma pressure and velocity at the entry point to the tumor’s extracellular space. Subsequently, to quantify plasma perfusion within the tumor zone, we have proposed a reduced-order 2D transport model for the tumor entry zone and its extracellular space for three different fenestra diameters: 0.1, 0.3, and 0.5 μm; the simulations were 2-phase viscous laminar transient. The findings support the hypothesis that plasma percolation into the tumor is proportional to the leakiness modulated by the fenestra openings, quantifiable through the opening sizes.</jats:p

Experimental and Computational Multiphase Flow

Dense fibrous extracellular constitution of solid tumors exerts high resistance to diffusive transport into it; additionally, the scarcity of blood and lymphatic flows hinders convection. The complexity of fluidic transport mechanisms in such tumor environments still presents open questions with translational end goals. For example, clinical diagnosis and targeted drug delivery platforms for such dense tumors can ideally benefit from a quantitative framework on plasma uptake into the tumor. In this study, we present a computational model for physical parameters that may influence blood percolation and penetration into simple biomimetic solid tumor geometry. The model implements three-phase viscous-laminar transient simulation to mimic the transport physics inside a tumor-adhering blood vessel and measures the constituent volume fractions of the three considered phases, viz. plasma, RBCs (red blood cells, also known as "erythrocytes "), and WBCs (white blood cells, also known as "leukocytes ") at three different flow times, while simultaneously recording the plasma pressure and velocity at the entry point to the tumor's extracellular space. Subsequently, to quantify plasma perfusion within the tumor zone, we proposed a reduced-order two-dimensional transport model for the tumor entry zone and its extracellular space for three different fenestra diameters: 0.1, 0.3, and 0.5 mu m; the simulations were two-phase viscous-laminar transient. The findings support the hypothesis that plasma percolation into the tumor is proportional to the leakiness modulated by the size of fenestra openings, and the rate of percolation decays with the diffusion distance.National Institutes of Health (NIH) Center of Biomedical Research Excellence (COBRE); North Dakota State University Center for Diagnostic and Therapeutic Strategies in Pancreatic Cancer [5P20GM109024]Published versionThe reported work has been supported by a National Institutes of Health (NIH) Center of Biomedical Research Excellence (COBRE) Pilot Grant from the North Dakota State University Center for Diagnostic and Therapeutic Strategies in Pancreatic Cancer (Project No. 5P20GM109024). Any opinions, findings, and conclusions or recommendations expressed here are, however, those of the authors, and do not necessarily reflect views of the NIH