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

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

Genetic determinants of SARS‐CoV‐2 and the clinical outcome of COVID‐19 in Southern Bangladesh

Abstract Background The coronavirus disease 2019 (COVID‐19) pandemic has had a severe impact on population health. The genetic determinants of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in southern Bangladesh are not well understood. Methods This study aimed to determine the genomic variation in SARS‐CoV‐2 genomes that have evolved over 2 years of the pandemic in southern Bangladesh and their association with disease outcomes and virulence of this virus. We investigated demographic variables, disease outcomes of COVID‐19 patients and genomic features of SARS‐CoV‐2. Results We observed that the disease severity was significantly higher in adults (85.3%) than in children (14.7%), because the expression of angiotensin‐converting enzyme‐2 (ACE‐2) diminishes with ageing that causes differences in innate and adaptive immunity. The clade GK (n = 66) was remarkable between June 2021 and January 2022. Because of the mutation burden, another clade, GRA started a newly separated clustering in December 2021. The burden was significantly higher in GRA (1.5‐fold) highlighted in mild symptoms of COVID‐19 patients than in other clades (GH, GK, and GR). Mutations were accumulated mainly in S (22.15 mutations per segment) and ORF1ab segments. Missense (67.5%) and synonymous (18.31%) mutations were highly noticed in adult patients with mild cases rather than severe cases, especially in ORF1ab segments. Moreover, we observed many unique mutations in S protein in mild cases compared to severe, and homology modeling revealed that those might cause more folding in the protein's alpha helix and beta sheets. Conclusion Our study identifies some risk factors such as age comorbidities (diabetes, hypertension, and renal disease) that are associated with severe COVID‐19, providing valuable insight regarding prioritizing vaccination for high‐risk individuals and allocating health care and resources. The findings of this work outlined the knowledge and mutational basis of SARS‐CoV‐2 for the next treatment steps. Further studies are needed to confirm the effects of structural and functional proteins of SARS‐CoV‐2 in detail for monitoring the emergence of new variants in future