Particle deposition, clearance and interaction with lung surfactant

Numerical moelling of three areas of human lung: (a) Particle deposition, (b) Mucociliary Clearance and (c) particle lung interaction

Breathing Life into Research: How Computers Help Us See Where Particles Go in the Lungs

Our lungs are incredibly complex — a delicate network of branching airways that delivers life-giving oxygen to every cell in the body. But along with oxygen, we also breathe in countless particles every day — from helpful medicines delivered through inhalers to harmful pollutants like dust, smoke, and even microplastics. Understanding exactly where these particles travel and where they end up in the lungs is essential for protecting health and improving treatments.

Dr. Suvash C. Saha’s research brings cutting-edge computer technology into this challenge. Using detailed 3D models of the lungs reconstructed from CT scans, he and his team run Computational Fluid Dynamics (CFD) simulations — advanced computer calculations that can predict how air moves and how particles behave as we breathe. These models are not generic; they are based on actual lung scans, meaning they capture the unique shapes, twists, and turns of each airway.

By simulating different breathing patterns, particle sizes, and airflow rates, the research can pinpoint “hotspots” where particles are most likely to settle. This is vital for two reasons. First, for medicine delivery, it helps improve inhalers so drugs reach exactly the parts of the lungs where they are needed. Second, for environmental health, it reveals how harmful particles from pollution or microplastics accumulate in sensitive lung regions, potentially leading to disease.

This virtual approach offers a safe, ethical, and cost-effective way to study the lungs without invasive procedures. It also opens the door to personalised healthcare — tailoring inhaler designs or treatment strategies to match a patient’s unique lung shape and breathing style.

Dr. Saha’s work doesn’t just stay in the lab. Its findings help doctors, engineers, and policymakers make informed decisions, whether that’s designing cleaner cities, developing better medical devices, or assessing the risks of emerging pollutants. By combining medical imaging with powerful computer simulations, this research provides a clear view of an invisible process, helping us all breathe a little easier.

Clearing the Airways: How Advanced Computer Models Help Us Understand Mucociliary Transport in Chronic Respiratory Diseases

Our lungs have a natural cleaning system that works quietly, day and night. Tiny hair-like structures called cilia beat in a coordinated rhythm, moving a thin layer of mucus that traps dust, bacteria, and other unwanted particles out of the airways. This process, known as mucociliary transport, is one of the body’s first lines of defence for keeping the lungs healthy and free from infection.

In people with chronic respiratory diseases such as asthma, chronic bronchitis, or chronic obstructive pulmonary disease (COPD), this cleaning system often doesn’t work as well. The mucus can become too thick or sticky, cilia can slow down, and harmful particles may linger in the lungs, leading to inflammation, infections, and breathing difficulties. Understanding exactly how and why this process changes in disease is key to developing better treatments.

Dr. Suvash C. Saha’s research uses advanced numerical methods — powerful computer-based simulations — to explore mucociliary transport in unprecedented detail. By creating 3D, physics-based models of the airways and incorporating realistic biological data, these simulations can mimic how mucus and cilia interact under healthy and diseased conditions. The models can test “what-if” scenarios — for example, how changes in mucus viscosity, cilia beat frequency, or airflow patterns might help or hinder the clearance of particles.

This approach allows researchers to explore situations that would be difficult or even impossible to study directly in patients. It provides a safe and cost-effective way to predict how different therapies — such as inhaled medicines, hydration treatments, or mechanical interventions — could improve airway clearance.

Ultimately, the goal is personalised medicine: tailoring treatments to the specific characteristics of each patient’s lungs. By combining insights from medical imaging, biological studies, and advanced computer modelling, Dr. Saha’s work aims to give doctors a clearer picture of how to restore and protect this vital self-cleaning system, helping patients with chronic respiratory diseases breathe easier and live healthier lives.

At the Molecular Frontier: How Computer Simulations Reveal the Secrets of Breathing

Every breath we take seems simple — air goes in, air comes out — yet at the microscopic level, it’s a marvel of physics, chemistry, and biology working together. One of the unsung heroes of breathing is the lung surfactant — a thin, soapy layer of molecules that coats the inside of our air sacs (alveoli). This layer dramatically reduces surface tension, stopping the tiny air sacs from collapsing and making breathing effortless.

But breathing isn’t just about air meeting lung tissue — it’s about how air and water molecules interact at this surfactant layer. These interactions are happening at nanometre scales and at speeds far too fast for the naked eye to see. Understanding this invisible process is crucial for tackling breathing difficulties in premature babies, people with acute respiratory distress syndrome (ARDS), or those exposed to pollutants that damage the surfactant.

Dr. Suvash C. Saha’s research uses molecular dynamics simulations — a powerful type of computer modelling — to zoom in on these interactions with incredible detail. Imagine a virtual microscope that doesn’t just see molecules but watches how they move, vibrate, and interact over time. By building a computer model of the lung surfactant monolayer and surrounding water and air molecules, these simulations reveal how temperature, pressure, or pollutants might change its behaviour.

Through this approach, the research can answer vital questions:

    • How does the surfactant layer adapt during inhalation and exhalation?

    • What happens when harmful particles or chemicals disrupt it?

    • How might engineered surfactants be designed to restore function when the natural system fails?

The beauty of molecular dynamics is that it allows scientists to run “what-if” scenarios in a safe, controlled virtual environment — something that would be impossible to do inside a living lung. This not only advances fundamental science but also helps in designing better medical treatments, inhalation therapies, and protective measures against airborne hazards.

By blending high-performance computing with molecular-scale biology, Dr. Saha’s work opens a new window into one of the most critical — and delicate — interfaces in the human body, bringing us closer to breathing breakthroughs that can save lives.

Tiny Threats: How Microplastics and Nanoplastics Affect Health in a Mice Model

We’ve all heard about plastic waste polluting our oceans, but the problem doesn’t end there. Over time, larger plastic items break down into tiny fragments — some small enough to see (microplastics) and others so small they’re invisible to the naked eye (nanoplastics). These particles are now found everywhere — in the air we breathe, the water we drink, and even in the food we eat. But what happens when they enter living organisms?

Dr. Suvash C. Saha’s research tackles this pressing question by studying how microplastics and nanoplastics interact inside the body, using a carefully controlled mice model. Mice are often used in biomedical research because their biological systems share many similarities with humans, allowing scientists to explore health impacts before translating findings to human medicine.

This work doesn’t just look at single types of particles — it focuses on the interactional effects. That means examining what happens when both microplastics and nanoplastics are present together. Could they amplify each other’s harmful effects? Could one type change how the other moves through the body? These are critical questions for understanding the real-world risks, because in nature, we’re rarely exposed to just one pollutant at a time.

Using advanced imaging and biochemical analysis, the research tracks where these particles go inside the body, whether they build up in certain organs, and how they influence inflammation, immune responses, and tissue health. Early findings suggest that combined exposure could pose more complex risks than either type of plastic alone, potentially affecting respiratory, digestive, and even reproductive systems.

The ultimate goal of this research is to provide scientific evidence for public health policy, guiding regulations to limit environmental plastic pollution and protect human health. It also offers a deeper understanding of how these particles might contribute to chronic diseases over time.

By uncovering how microplastics and nanoplastics interact inside a living organism, Dr. Saha’s work shines a light on a hidden threat — and brings us one step closer to solutions that keep our environment, and ourselves, healthier.

Computational Fluid Dynamics study of particle deposition in the CT-Scan based lung surface

Introduction

Computational Fluid Dynamics (CFD) is a branch of fluid mechanics that utilizes numerical analysis and algorithms to solve and analyze problems involving fluid flows. The application of CFD in biomedical engineering has significantly advanced the understanding of complex biological processes, one of which is the study of particle deposition in the human respiratory system. This essay explores the use of CFD to study particle deposition on CT-scan-based lung surfaces, an area critical for assessing the inhalation of pollutants and the efficacy of inhalable medical treatments.

Background

The human lung is a highly intricate structure, consisting of branching airways that culminate in alveoli, where gas exchange occurs. The deposition of inhaled particles in these airways depends on various factors, including particle size, airflow patterns, and the anatomical features of the lung. With the rise in air pollution and respiratory diseases, understanding how particles behave once they enter the respiratory system is essential for developing better therapeutic strategies and protective measures.

Methodology

CT-Scan Based Lung Model

The first step in conducting a CFD study of particle deposition involves creating an accurate model of the lung’s anatomy. This is typically achieved using CT (computed tomography) scans, which provide detailed cross-sectional images of the lung. These images are then processed to create a three-dimensional model of the lung’s airways. Software tools such as Mimics or 3D Slicer can be employed to convert CT images into 3D models, capturing the intricate details of the bronchial tree.

Meshing

Once the 3D model is created, it is then converted into a computational mesh. Meshing involves breaking down the 3D model into smaller elements or cells, which are used by CFD software to perform calculations. The quality and resolution of the mesh significantly influence the accuracy of the simulation. Fine meshes are used in regions where high accuracy is required, such as around bifurcations of the airways, whereas coarser meshes may be used in more straightforward sections.

Boundary Conditions

In CFD simulations, it is essential to define boundary conditions, which specify the behavior of the fluid at the boundaries of the computational domain. For a lung model, this includes specifying the airflow rate at the trachea (inlet) and the pressure or flow rate at the terminal bronchioles (outlets). Typically, inhalation flow rates are based on physiological data, and different breathing patterns can be simulated to study their effects on particle deposition.

Particle Tracking

Particles of various sizes can be introduced into the airflow to study their deposition patterns. These particles can represent pollutants, aerosols, or medication droplets. The behavior of particles in the airflow is governed by factors such as gravity, Brownian motion, and interactions with the airway walls. CFD software, such as ANSYS Fluent or OpenFOAM, allows for the simulation of these particles’ trajectories and their eventual deposition locations.

Results

Airflow Patterns

CFD simulations reveal complex airflow patterns within the lung’s airways. During inhalation, the airflow is generally laminar in the upper airways and becomes more turbulent as it moves deeper into the bronchial tree. The bifurcations and branching nature of the airways cause significant changes in velocity and pressure, influencing where particles are likely to deposit.

Particle Deposition

Particle deposition patterns vary significantly based on particle size. Larger particles tend to deposit in the upper airways due to inertial impaction, while smaller particles can penetrate deeper into the lung, depositing via sedimentation and diffusion. For example, particles larger than 10 micrometers are mostly captured in the nasal cavity or the trachea, whereas particles in the range of 1-5 micrometers can reach the bronchi and bronchioles. Ultrafine particles (less than 0.1 micrometers) have the potential to reach the alveoli.

Effect of Breathing Patterns

Different breathing patterns, such as shallow versus deep breathing, affect particle deposition. Shallow breathing primarily deposits particles in the upper airways, whereas deep breathing allows particles to travel deeper into the lungs. Breath-hold techniques, where a person holds their breath after inhaling, can also increase the deposition of particles in the alveoli due to prolonged residence time.

Discussion

Implications for Health

Understanding particle deposition is critical for assessing the health risks associated with inhaling pollutants. For instance, particles that deposit in the upper airways may cause conditions such as asthma or bronchitis, while those that reach the alveoli can lead to more severe issues like lung cancer or systemic inflammation due to their potential to enter the bloodstream. CFD studies provide insights into which regions of the lung are most at risk and help in designing protective measures such as masks or air purification systems.

Drug Delivery

CFD simulations are also invaluable in optimizing the delivery of inhalable drugs. By understanding how particles of different sizes behave within the lung, pharmaceutical companies can design aerosol treatments that target specific regions of the lung. For example, medications intended to treat alveolar infections need to ensure that a significant portion of the drug reaches the alveoli, which can be achieved by formulating the drug particles to the appropriate size and using breath-hold techniques during inhalation.

Personalized Medicine

The use of patient-specific lung models derived from CT scans allows for personalized treatment plans. Each individual’s lung anatomy is unique, and factors such as airway obstructions or variations in branching patterns can significantly influence particle deposition. CFD allows for the customization of inhalable treatments to maximize efficacy and minimize side effects based on individual anatomical features.

Conclusion

The application of Computational Fluid Dynamics in studying particle deposition on CT-scan-based lung surfaces offers profound insights into respiratory health and the efficacy of inhalable therapies. By leveraging detailed anatomical models and sophisticated simulation techniques, researchers can predict how particles behave within the lung, paving the way for improved treatments and protective measures against airborne pollutants. As CFD technology and medical imaging continue to advance, the potential for even more precise and personalized healthcare solutions becomes increasingly attainable. This interdisciplinary approach not only enhances our understanding of respiratory mechanics but also contributes significantly to the fields of environmental health and pharmaceutical sciences.

Muco-ciliary Transport in patients with chronic respiratory diseases using an advanced numerical method

Introduction

Muco-ciliary transport is a crucial defense mechanism of the respiratory system, responsible for clearing mucus and trapped particles from the airways. This process involves the coordinated action of cilia, hair-like structures on the epithelial cells lining the airways, and the mucus layer, which traps inhaled pathogens and particulates. In patients with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), cystic fibrosis, and chronic bronchitis, muco-ciliary transport is often impaired, leading to mucus accumulation and increased susceptibility to infections. This essay explores the use of advanced numerical methods to study and improve muco-ciliary transport in patients with chronic respiratory diseases.

Background

Chronic respiratory diseases are characterized by persistent inflammation, mucus hypersecretion, and structural changes in the airways. These conditions hinder the normal functioning of the muco-ciliary escalator, resulting in reduced mucus clearance. The efficiency of muco-ciliary transport depends on several factors, including ciliary beat frequency, mucus rheology, and airway surface liquid composition. Numerical methods, particularly computational fluid dynamics (CFD) and particle tracking models, offer powerful tools to simulate and analyze these complex processes.

Methodology

Advanced Numerical Methods

The study of muco-ciliary transport in diseased lungs requires sophisticated numerical techniques to capture the intricate interactions between mucus, cilia, and airway structures. The following advanced numerical methods are commonly used:

  1. Computational Fluid Dynamics (CFD): CFD is used to model airflow within the airways and the movement of mucus. This involves solving the Navier-Stokes equations for fluid flow and incorporating models for mucus viscosity and elasticity.
  2. Particle Tracking Models: These models simulate the movement of particles (representing mucus and trapped pathogens) within the airway. They account for forces such as gravity, drag, and interactions with the airway walls and cilia.
  3. Multi-Scale Modeling: Multi-scale models integrate detailed simulations of ciliary motion at the cellular level with larger-scale airflow and mucus transport models. This approach captures the effects of individual cilia on overall mucus movement.
  4. Immersed Boundary Method: This method is used to model the interaction between moving cilia and the surrounding fluid. It allows for the simulation of flexible, moving boundaries within a fluid domain, accurately representing the ciliary beat pattern.

Model Development

To develop a comprehensive numerical model of muco-ciliary transport, the following steps are undertaken:

  1. Anatomical Model Creation: High-resolution imaging techniques such as CT or MRI scans are used to create detailed 3D models of the patient’s airway anatomy. These models serve as the geometric foundation for simulations.
  2. Ciliary Beat Pattern Simulation: The ciliary beat pattern is modeled using data from experimental studies. This includes the frequency, amplitude, and coordination of ciliary motion. Advanced techniques such as the immersed boundary method are used to simulate ciliary motion within the mucus layer.
  3. Mucus Properties Characterization: The rheological properties of mucus, including viscosity and elasticity, are characterized using experimental data. These properties are incorporated into the CFD model to simulate mucus behavior accurately.
  4. Boundary and Initial Conditions: Appropriate boundary and initial conditions are set for the simulations, including airflow rates, mucus secretion rates, and initial distribution of mucus within the airways.

Results

Airflow and Mucus Dynamics

Numerical simulations provide detailed insights into airflow patterns and mucus dynamics within the airways of patients with chronic respiratory diseases. The results highlight regions of airflow obstruction, areas of mucus accumulation, and the impact of altered ciliary function on mucus transport. In particular, the simulations reveal how changes in airway geometry and mucus properties due to chronic diseases affect muco-ciliary clearance.

Impact of Ciliary Dysfunction

In patients with chronic respiratory diseases, ciliary dysfunction is a common issue. Simulations show that reduced ciliary beat frequency and coordination lead to decreased mucus transport velocity. This results in mucus stasis and increased risk of infections. Advanced numerical methods allow for the quantification of these effects, providing a basis for assessing the severity of ciliary dysfunction and its impact on mucus clearance.

Therapeutic Interventions

Numerical models can be used to evaluate the effectiveness of therapeutic interventions aimed at improving muco-ciliary transport. For example, simulations can assess the impact of treatments such as mucolytics (which thin mucus), ciliary stimulants (which enhance ciliary beat frequency), and airway clearance techniques (such as chest physiotherapy). The results help in optimizing treatment strategies for individual patients based on their specific airway characteristics and disease severity.

Discussion

Implications for Treatment

The use of advanced numerical methods to study muco-ciliary transport has significant implications for the treatment of chronic respiratory diseases. By providing detailed insights into the factors affecting mucus clearance, these models enable personalized treatment approaches. For instance, patients with severe ciliary dysfunction may benefit more from ciliary stimulants, while those with highly viscous mucus may respond better to mucolytics.

Personalized Medicine

Numerical simulations based on patient-specific airway models allow for the development of personalized treatment plans. This approach takes into account individual variations in airway anatomy, mucus properties, and ciliary function. Personalized models can predict the outcomes of different therapeutic interventions, guiding clinicians in selecting the most effective treatments for each patient.

Future Research

Future research in this area could focus on further refining numerical models to capture the complex interactions between mucus, cilia, and the airway surface liquid. Additionally, the integration of multi-scale models with experimental data from in vitro and in vivo studies can enhance the accuracy and predictive capability of simulations. Advances in imaging techniques and computational power will also play a crucial role in improving the resolution and detail of numerical models.

Conclusion

Advanced numerical methods provide a powerful tool for studying muco-ciliary transport in patients with chronic respiratory diseases. By accurately simulating the complex dynamics of mucus and ciliary motion, these models offer valuable insights into the factors affecting mucus clearance. This knowledge can inform the development of personalized treatment strategies, improving the management of chronic respiratory diseases and enhancing patient outcomes. As numerical methods and computational technologies continue to advance, their application in biomedical research holds great promise for addressing the challenges of chronic respiratory diseases and improving respiratory health.

Modelling of Gold nanoparticle with or without drug coated interaction into lung surfactant at the molecular scale

Introduction

Nanotechnology has revolutionized the field of medicine, offering innovative approaches to drug delivery, diagnostic imaging, and treatment of various diseases. Among the numerous applications of nanotechnology, gold nanoparticles (AuNPs) have garnered significant attention due to their unique physical and chemical properties. This essay explores the molecular-scale modeling of gold nanoparticles with or without drug-coated interactions in lung surfactant, aiming to understand their behavior and potential therapeutic implications.

Background

Gold Nanoparticles

Gold nanoparticles are small gold particles with a diameter in the range of 1 to 100 nanometers. Their large surface area-to-volume ratio, ease of functionalization, and biocompatibility make them ideal candidates for biomedical applications. AuNPs can be synthesized in various shapes, including spheres, rods, and stars, and can be functionalized with drugs, proteins, or other molecules to enhance their therapeutic efficacy.

Lung Surfactant

Lung surfactant is a complex mixture of lipids and proteins that reduces surface tension in the alveoli, preventing lung collapse and facilitating gas exchange. It consists primarily of phospholipids, with dipalmitoylphosphatidylcholine (DPPC) being the most abundant component. Surfactant proteins, such as SP-A, SP-B, SP-C, and SP-D, play crucial roles in surfactant function and host defense. Understanding the interaction between nanoparticles and lung surfactant at the molecular level is essential for assessing their safety and efficacy in respiratory therapies.

Methodology

Molecular Dynamics (MD) Simulations

Molecular dynamics (MD) simulations are a powerful computational tool for studying the interactions between nanoparticles and biological molecules at the atomic level. These simulations involve solving Newton’s equations of motion for a system of interacting particles, allowing for the observation of dynamic processes over time.

  1. Model Preparation: The first step in MD simulations is to create an initial model of the system. This includes constructing a model of the gold nanoparticle, with or without drug coating, and the lung surfactant components. For the surfactant, a bilayer or monolayer model of DPPC can be used, along with relevant surfactant proteins.
  2. Force Field Selection: A suitable force field, such as CHARMM, AMBER, or GROMOS, is selected to describe the interactions between atoms in the system. Force fields include parameters for bond lengths, angles, dihedrals, and non-bonded interactions (van der Waals and electrostatic forces).
  3. Solvation and Ion Placement: The system is solvated with water molecules, and appropriate ions are added to neutralize the charge and mimic physiological conditions.
  4. Equilibration and Production Runs: The system undergoes energy minimization to remove any steric clashes, followed by equilibration to stabilize the temperature and pressure. After equilibration, production runs are performed to collect data on the interactions and dynamics of the system.

Analysis of Interactions

  1. Binding Affinity: The binding affinity of the gold nanoparticle to the lung surfactant components is calculated using techniques such as molecular docking or free energy perturbation (FEP). This provides insights into the strength and stability of the interaction.
  2. Conformational Changes: The conformational changes in the surfactant molecules upon interaction with the nanoparticle are analyzed using metrics such as root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF).
  3. Interaction Energies: The interaction energies between the nanoparticle and surfactant components are calculated to understand the contributions of different types of interactions (hydrophobic, electrostatic, hydrogen bonding).
  4. Structural Analysis: The structural integrity of the surfactant in the presence of the nanoparticle is assessed to determine any potential disruptions to its function.

Results

Interaction with Lung Surfactant

  1. Uncoated Gold Nanoparticles: MD simulations reveal that uncoated gold nanoparticles primarily interact with the hydrophobic tails of the DPPC molecules. The interaction is driven by van der Waals forces, leading to minimal disruption of the surfactant structure. The nanoparticles tend to embed partially within the surfactant layer, maintaining the overall integrity of the surfactant.
  2. Drug-Coated Gold Nanoparticles: When coated with drugs, the interaction profile of the nanoparticles changes significantly. The drug molecules can form hydrogen bonds and electrostatic interactions with the polar head groups of DPPC and surfactant proteins. This can lead to increased binding affinity and potential conformational changes in the surfactant molecules. The extent of these changes depends on the nature of the drug coating and its affinity for the surfactant components.

Conformational Changes and Stability

The presence of gold nanoparticles, especially when drug-coated, can induce conformational changes in the surfactant molecules. These changes are quantified using RMSD and RMSF analysis. In some cases, the drug-coated nanoparticles can cause partial unfolding or reorientation of surfactant proteins, which may affect their functionality. However, the overall stability of the surfactant layer is generally maintained, indicating that the system can accommodate the nanoparticles without significant disruption.

Discussion

Therapeutic Implications

The interaction of gold nanoparticles with lung surfactant has important implications for respiratory therapies. Uncoated AuNPs show minimal disruption to the surfactant layer, suggesting their potential use as carriers for drug delivery without compromising lung function. Drug-coated AuNPs, on the other hand, offer a dual benefit of targeted drug delivery and enhanced interaction with surfactant components, potentially improving therapeutic efficacy.

Safety Considerations

While the simulations indicate that gold nanoparticles can interact with lung surfactant without causing significant disruption, it is essential to consider potential long-term effects and toxicity. In vivo studies and clinical trials are necessary to validate the safety and efficacy of these nanoparticles in respiratory therapies. Additionally, understanding the impact of nanoparticle size, shape, and surface chemistry on their interactions with lung surfactant can guide the design of safer and more effective nanomedicines.

Conclusion

The molecular-scale modeling of gold nanoparticle interactions with lung surfactant provides valuable insights into their potential as therapeutic agents for respiratory diseases. Advanced numerical methods, such as molecular dynamics simulations, offer a detailed understanding of the dynamic processes and interactions at play. While uncoated AuNPs exhibit minimal disruption to the surfactant layer, drug-coated AuNPs demonstrate enhanced binding and potential therapeutic benefits. Further research and experimental validation are necessary to fully harness the potential of gold nanoparticles in respiratory medicine, ensuring their safety and efficacy for clinical applications.

Related publications

Journal Publications

  1. X. Huang, I. Francis1, G. Saha, M.M. Rahman, S. C. Saha, “Large Eddy Simulation-Based Modeling of Cold-Air Inhalation from Nasal Cavities to the Distal Lung: Insights for Athlete Health and Performance”, Results in Engineering, Accepted on 25/06/2024, pp. … [IF: 5.0]. Q1.
  2. S. C. Saha, X. Huang, I. Francis1, G. Saha, “Airway Stability in Sleep Apnea: Assessing Continuous Positive Airway Pressure Efficiency”, Respiratory Physiology & Neurobiology, 325, pp. 104265. [IF: 2.3]. Q2.
  3. X. Huang, S. C. Saha, G. Saha, I. Francis, Z. Luo, “Transport and deposition of microplastics and nanoplastics in the human respiratory tract”, Environmental Advances, 16, (2024), pp. 100525 [CS: 3.6]. Q1
  4.  S. C. Saha, G. Saha “Effect of Microplastics Deposition on Human Lung Airways: A Review with Computational Benefits and Challenges”, Heliyon, 10, (2024) pp. e24355 [IF: 4.0]. Q1.
  5. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “A numerical study on sedimentation effect of dust, smoke and traffic particle deposition in a realistic human lung”, International Journal of Multiphase Flow, 27, (2024) pp. 104685 [IF: 3.8]. Q1.
  6. A. R. Paul, A. Jain, S. C. Saha, “Exposure Assessment of Air Pollution in Lungs”, Atmosphere, 13, (2022), pp. 1767 [IF: 3.110]. Q2
  7. I. Francis, S. C. Saha, “Surface tension effects on flow dynamics and alveolar mechanics in the acinar region of human lung”, Heliyon, 8, (2022), pp. e11026 [IF: 3.776]. Q1.
  8. I. Francis, S. C. Saha, “Computational Fluid Dynamics and Machine Learning Algorithms Analysis of Striking Particle Velocity Magnitude, Particle Diameter, and Impact Time Inside an Acinar Region of Human Lung”, Physics of Fluid, 34, (2022), pp. 101904 [IF: 3.521]. Q1.
  9. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “Nanoparticle transport and deposition in a heterogeneous human lung airway tree: An efficient one path model for CFD simulations’ European Journal of Pharmaceutical Sciences, 177, (2022), pp. 106279 [IF: 5.112]. Q1.
  10. M. Z. Islam, S. I. Hossain, E. Deplazes, S. C. Saha, “Concentration-dependent cortisone adsorption and interaction with model lung surfactant monolayer”, Molecular Simulation, 48 (2022) pp. 1627–1638 [IF: 2.346]. Q2.
  11. F. Jiao, S. I. Hossain, J. Sang, S. C. Saha, Y. T. Gu, Z. E Hughes, N. S. Gandhi, “Molecular Basis of Transport of Surface Functionalised Gold Nanoparticles to Pulmonary Surfactant”, RSC Advances, 12, (2022), pp. 18012 – 18021 [IF: 3.361]. Q1.
  12. S. C. Saha, I. Francis, X. Huang, A. R. Paul, “Heat transfer and fluid flow analysis of realistic 16-generation lung”, Physics of Fluid, 34, (2022), pp. 061906 [IF: 3.521]. Q1.
  13. I. Francis, J. Shrestha, K. R. Paudel, P. M. Hansbro, M. E. Warkiani, S. C. Saha, “Recent advances in Lung-on-a-chip models”, Drug Discovery Today, 27, (2022), pp. 2593-2602[IF: 7.851]. Q1.
  14. T. Gemci, V. Ponyavin, R. Collins, T. E. Corcoran, S. C. Saha, M. S. Islam, “CFD study of dry pulmonary surfactant aerosols deposition in upper 17 generations of human respiratory tract”, Atmosphere, 13, (2022), pp. 726. [IF: 2.686]. Q2.
  15. B. V. Duong, P. Larpruenrudee, T. Fang, S. I. Hossain, S. C. Saha, Y.T. Gu, M.S. Islam, ”Is SARS CoV-2 Omicron variant deadlier and highly transmissible than Delta variant?”, International Journal of Environmental Research and Public Health, 19, (2022), pp.4586. [IF: 3.390]. Q1.
  16. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “Numerical study of nano and micro pollutant particle transport and deposition in realistic human lung airways’ Powder Technology, 402, (2022), pp. 117364 [IF: 5.134]. Q1.
  17. M.Z. Islam, M. Krajewska, S.I. Hossain, K. Prochaska, A. Anwar, E. Deplazes, S. C. Saha, ”The concentration-dependent effect of the steroid drug prednisolone on a lung surfactant monolayer”, Langmuir., 38, (2022), pp. 4188 – 4199, [IF.: 3.882]. Q1.
  18. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “Aerosol particle transport and deposition in upper and lower airways of infant, child and adult human lungs”, Atmosphere, 12, (2021), pp. 1402. [IF: 2.686]. Q2.
  19. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “Numerical study of nanoscale and microscale particle transport in realistic lung models with and without stenosis”, International Journal of Multiphase Flow, 145, (2021) pp. 103842 [IF: 3.186]. Q1.
  20. S. I. Hossain, S. C. Saha, E. Deplazes, “Phenolic compounds alter the ion permeability of phospholipid bilayers via specific lipid interactions”, Physical Chemistry Chemical Physics (PCCP), 23, (2021) pp. 22352 – 22366. [IF: 3.430]. Q1.
  21. S. I. Hossain, Z. Luo, E. Deplazes, S. C. Saha, “Shape matters – The interaction of gold nanoparticles with model lung surfactant monolayers”, Journal of the Royal Society Interface, 18, (2021) pp. 20210402, [IF: 4.118]. Q1.
  22. M.M. Rahman, M. Zhao, M.S. Islam, K. Dong, S. C. Saha, “Aging effects on airflow distribution and micron-particle transport and deposition in a human lung using CFD-DPM approach”, Advanced Powder Technology, 32, (2021), pp. 3506 – 3516. [IF: 4.833]. Q1.
  23. M. S. Islam, P. Larpruenrudee, S. C. Saha, O. Pourmehran, A. R. Paul, T. Gemci, R. Collins, G. Paul, Y. T. Gu, “How severe acute respiratory syndrome coronavirus-2 aerosol propagate through the age-specific upper airways?”, Physics of Fluids, 33, (2021) pp. 081911. [IF: 3.521]. Q1.
  24. A.R. Paul, F. Khan, A. Jain, S.C. Saha, ”Deposition of smoke particles in human airways with realistic waveform, Atmosphere, 12, (2021), pp. 912. [IF: 2.686]. Q2.
  25. A. Tiwari, A. Jain, A.R. Paul, S.C. Saha, ”Computational evaluation of drug delivery in human respiratory tract under realistic inhalation, Physics of Fluids, 33, (2021), pp. 083311. [IF: 3.521]. Q1.
  26. M. S. Islam, P. Larpruenrudee, A. R. Paul, G. Paul, T. Gemci, Y. T. Gu, S. C. Saha, ”Polydisperse aerosol transport and deposition in upper airways of age-specific lung”, International Journal of Environmental Research and Public Health, 18, (2021), pp. 6239. [IF: 3.390]. Q2.
  27. M. S. Islam, P. Larpruenrudee, A. R. Paul, G. Paul, T. Gemci, Y. T. Gu, S. C. Saha, ”SARS CoV-2 Aerosol: How far it can travel to the lower airways?”, Physics of Fluids, 33, (2021), pp. 061903. [IF: 3.521. Q1.
  28. Z. Fan, D. Holmes, E. Sauret, M. S. Islam, S. C. Saha, Z. Ristovski, Y. T. Gu, “A multi-scale modelling method incorporating spatial coupling and temporal coupling into transient simulations of the human airways”, International Journal for Numerical Methods in Fluids, 93, (2021) pp. 2905 – 2920, [IF: 2.107]. Q1.
  29. S. I. Hossain, N. S. Gandhi, Z. E. Hughes, S. C. Saha, “Computational studies of lipid wrapped gold nanoparticle transport through model lung surfactant monolayers”, The Journal of Physical Chemistry B, 125, (2021) pp.1392 – 1401, [IF: 2.991]. Q1
  30. M.S. Islam, Y.T. Gu, A. Farkas, G. Paul, S. C. Saha, “Helium-Oxygen Mixture Model for Particle Transport in CT-Based Human Lung”, International Journal of Environmental Research and Public Health, 17 (2020) pp. 3574. [IF: 2.468] Q2.
  31. P. Singh , V. Raghav, V. Padhmashali, G. Paul, M.S. Islam, S. C. Saha, “Airow and particle transport prediction through stenosis airway”, International Journal of Environmental Research and Public Health, 17 (2020) pp. 1119. [IF: 2.468] Q2.
  32. M.S. Islam, G. Paul, H.X. Ong, P.M. Young, Y.T. Gu, S. C. Saha, “A Review of respiratory anatomical development, air ow characterization and particle deposition”, International Journal of Environmental Research and Public Health, 17 (2020) pp. 380. [IF: 2.468] Q2
  33. S. M. Vanaki, D. Holmes, S. C. Saha, J. Chen, R. J. Brown, P. G. Jayathilake, “Muco-ciliary clearance: A review of modelling techniques”, Journal of Biomechanics, 99 (2020) pp. 109578 [IF: 2.576]. Q1.
  34. M. S. Islam, S. C. Saha, E. Sauret, H. Ong, P. Young, Y. T. Gu, “Euler-Lagrange approach to investigate respiratory anatomical shape eects on aerosol particle transport and deposition” Toxicology Research and Application, 3 (2019) pp. 1 – 15.
  35. Q. Gu, S. Qi, Y. Yue, J. Shen, B. Zhang, W. Sun, W. Qian, M. S. Islam, S. C. Saha, J. Wu, “Structural and functional alternations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer”, BioMedical Engineering OnLine, 18 (2019) Article number: 108 [I.F. 1.676]. Q2.
  36. M. S. Islam, S. C. Saha, T. Gemci, E. Sauret, I. A. Yang, Z. Ristovski, Y. T. Gu, “Euler-Lagrange prediction of diesel-exhaust polydisperse particle transport and deposition: Anatomy and turbulence eects” Nature Scientic Reports, 9 (2019) Article number: 12423 (16 pages). [IF: 4.525]. Q1.
  37. S. I. Hossain, N. S. Gandhi, Z. E. Hughes, Y. T. Gu, S. C. Saha, “Molecular insights on the interference of simplied lung surfactant models by gold nanoparticle pollutants”, BBA Biomembranes, 1861 (2019) pp. 1458 – 1467. [IF: 3.790]. Q1
  38. M. S. Islam, S. C. Saha, E. Sauret, T. Gemci, I. A. Yang, Y. T. Gu, “Polydisperse microparticle transport and deposition to the terminal bronchioles in a heterogeneous vasculature tree” Nature Scientic Reports, 8 (2018) Article number: 16387 (9 pages). [IF: 4.525. Q1.
  39. M. S. Islam, S. C. Saha, E. Sauret, T. Gemci, I. A. Yang, Y. T. Gu, “Ultrane particle transport and deposition in a large scale 17-generation lung model” Journal of Biomechanics, 64 (2017) pp. 9 { 18, [IF: 2.576]. Q1.
  40. M. S. Islam, S.C. Saha, E. Sauret, I. Yang, Y. Gu, “Pulmonary aerosol transport and deposition analysis in upper 17 generations of the human respiratory tract.” Journal of Aerosol Science, 108, (2017), pp. 29 – 43 [IF: 2.240] Q2.

Conference Proceedings

  1. M. Z. Islam, S. I. Hossain, E. Deplazes, S. Bhowmick S. C. Saha, “Molecular Dynamics Study of Prednisolone Concentration on Cholesterol Based Lung Surfactant Monolayer” Proceeding of the 13th International Conference on Mechanical Engineering (ICME2019), 18 – 20 December 2019, BUET, Dhaka, Bangladesh.
  2. M. S. Islam, Y. T. Gu, S. C. Saha, “Helium-Oxygen Mixture Model for Particle Transport in CT-Based MouthThroat and Upper airways ” Proceeding of the Australasian Conference on Computational Mechanics, Tasmania, Australia, 27 – 29 November 2019.
  3. M. S. Islam, Y. T. Gu, S. C. Saha, “Helium-Oxygen Mixture Model for Particle Transport in CT-Based MouthThroat and Upper airways ” Proceeding of the Australasian Conference on Computational Mechanics, Tasmania, Australia, 27 – 29 November 2019.
  4. S. C. Saha, M. S. Islam, Z. Luo, “Ultrane Particle Transport and Deposition in the Upper Airways of a CT-Based Realistic Lung” Proceeding of the 21st Australasian Fluid Mechanics Conference, Adelaide, Australia, 10 – 13 December 2018.
  5. S. C. Saha, M. S. Islam, M. M. Molla, “Aerosol Particle Transport and Deposition in a CT-Scan Based Mouth-Throat Model”, Proceedings of the 8th BSME International Conference on Thermal Engineering, 19 – 21 December, 2018, Dhaka, Bangladesh.
  6. M. S. Islam, S. C. Saha, P. M. Young, “Aerosol Particle Transport and Deposition in a CT- Based Lung Airway for Helium-Oxygen Mixture” Proceeding of the 21st Australasian Fluid Mechanics Conference, Adelaide, Australia, 10 – 13 December 2018. 2017
  7. M.S. Islam, S. C. Saha, E. Sauret, Y.T. Gu, “Transport and Deposition in the Terminal Bronchioles of Large Scale 17-Generation Model” Proceedings of the 8th International Conference on Computational Methods (ICCM2017), 25 { 29 July 2017, Guilin, Guangxi, China.
  8. S.I. Hossain, S. C. Saha, N.S. Gandhi, E. Sauret, Y.T. Gu, “Molecular dynamics simulation study of a pulmonary surfactant monolayer with Gold nanoparticles”, MM2017 Conference, 27 – 29 September 2017, Curtin University, Western Australia.
  9. M.S. Islam, S. C. Saha, E. Sauret, Y.T. Gu, “Diesel Exhaust Particle Transport and Deposition in Upper 4 Generations of the CT-Scan based Lung Airways”, Proceedings of the 20th Australasian Fluid Mechanics Conference (AFMC2016), 05 { 08 December, 2016, Perth, Australia.
  10. M.S. Islam, S. C. Saha, E. Sauret, Y.T. Gu, “Eects of Velocity on Diesel Exhaust Particle Transport and Deposition in the Central Airways of the Human Lung”, Proceedings of the 2nd Australasian Conference on Computational Mechanics (ACCM2015), 30 November – 01 December 2015, Brisbane, Australia.
  11. M. S. Islam, S. C. Saha, E. Sauret, Y. T. Gu, “Numerical investigation of aerosol particle transport and deposition in realistic lung airway”, Proceedings of the 6th International Conference on Computational Methods (ICCM2015), 14 { 17 July, 2015, Auckland, New Zealand.