Particle deposition in the human lung: Health implications of particulate matter from different sources

Deng, Qihong; Deng, Linjing; Miao, Yufeng; Guo, Xilong; Li, Yuguo

doi:10.1016/j.envres.2018.11.014

Cited by 220 publications

(101 citation statements)

References 78 publications

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“…Additionally, they found some differences between the deposition of soil-dust particles and traffic particles. Whereas traffic particles were deposited mainly via diffusion and sedimentation, soil-dust particle deposition was driven by sedimentation only [57]. Deng et al (2019) also indicated, as a limitation of their study, the fact that particle dynamics during transport in the RT, such as collision, growth, and aggregation, are not considered in their model.…”

Section: Applicationsmentioning

confidence: 99%

“…Deng et al (2019) recently used a one-dimensional lumped "trumpet" model with a variable cross-sectional area to predict PM deposition inside the human lung [57]. The following equation described the particles transport and deposition:…”

Section: Applicationsmentioning

confidence: 99%

“…In all generations, the initial particle concentration was set to zero with the establishment of steady-state after several cycles of breathing. After this numerical modeling, the deposition fraction (DF) in different airways of the human lungs could be achieved [57].…”

Section: Applicationsmentioning

confidence: 99%

“…Additionally, because the lumped "trumpet" model was used, they could not consider the heterogeneity of the lung's structure. One further limitation is that they only evaluated the effect of deposition location on health, and they did not consider the toxicity of chemical compositions [57]. It is a common limitation that is seen in many studies that have applied the 1-D model for the prediction of aerosol particle deposition.…”

Section: Applicationsmentioning

confidence: 99%

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Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

et al. 2020

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The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches' many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.Atmosphere 2020, 11, 137 2 of 27 PM Cd , and PM Pb ), the annual limit values are 6, 5, and 500 ng/m 3 , respectively (Directives 1999/30/EC and 2008/50/EC) [9]. Inhaled drug therapy can be a useful option against many lung diseases such as asthma or COPD. The efficiency of pharmaceutical aerosols depends on their deposition in the airways, especially the upper generations [10]. The estimation of aerosol transportation and deposition in the human lung can therefore indicate strategies for the prevention or alleviation of health effects from inhaled toxic particles or new drug-aerosol delivery approaches that can overcome the limitations of inhalers [11]. Atmospheric-aerosol deposition in the human respiratory tract (RT) can be directly measured by monitoring and comparing inhaled and exhaled particle concentrations. However, due to experimental limitations, the regional dose in the respiratory system is hard to measure experimentally but can be predicted by the application of different mathematical models. Suitable mathematical models are those that show a reasonable correlation between results obtained from modeling and empirical measurements [12]. Computational models generally consist of three elements: (1) a mathematical formulation that describes the relevant physical and chemical processes, (2) the setting of specific initial and boundary conditions, and (3) the solving of equations for the specified geometry. The accuracy of a computational model depends on the extent to which these elements are realistic. Additionally, regardless of the computational model that is applied, four types of input data are required: (1) lung morphometry, (2) breathing pattern, (3) particle properties, and (4) gas/vapor properties (Figure 1) [4]. The effects of these input data to the computational models were introduced in the review of Rostami (2009) and Hussain et al. (2011) [4,13]. In this review, we only present the advances of lung geometry models due to their strong relations to the accuracies of the computational models [4,13].

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

et al. 2020

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“…Airborne PM were considered the hazardous causative determinants of several diseases such as respiratory, cardiovascular and neurological disorders. These particles are divided into three main categories on the basis of their diameter: coarse particles, or PM 10 , (with an aerodynamic diameter between 10 and 2.5 µm); fine particles, or PM 2.5 , (with diameters < 2.5 µm); and ultrafine particles, or PM 0.1, (with diameters < 0.1 µm) [7]. Numerous studies revealed that particulate matter collected from different locations all over the world is strongly associated with the elevated morbidity and mortality and various diseases [8-13].…”

Section: Introductionmentioning

confidence: 99%

Imperative role of particulate matter in innate immunity during RNA virus infection

Mishra

Pandikannan

Gangamma

et al. 2020

Preprint

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25Sensing of pathogens by specialized receptors is the hallmark of the innate immune response. 26Innate immune response also mounts a defense response against various allergens and pollutants 27 including particulate matter present in the atmosphere. Air pollution has been included as the top 28 threat to global health declared by WHO which aims to cover more than three billion people against 29 health emergencies from 2019-2023. Particulate matter (PM), one of the major components of air 30 pollution, is a significant risk factor for many human diseases and its adverse effects include 31 morbidity and premature deaths throughout the world. Several clinical and epidemiological studies 32 have identified a key link between the PM composition and the prevalence of respiratory and 33 inflammatory disorders. However, the underlying molecular mechanism is not well understood. 34Here, we investigated the influence of air pollutant, PM10 during RNA virus infections using highly 35 pathogenic avian influenza (HPAI). We thus characterized the transcriptomic profile of lung 36 epithelial cell line, A549 treated with PM10 prior to infection with (HPAI) H5N1 influenza virus, 37 which is known to severely affect the lung and cause respiratory damage. We found that PM10 38 regulates virus infectivity and enhances overall pathogenic burden in the lung cells. Additionally, 39 the transcriptomic profile highlights the connection of host factors related to various metabolic 40 pathways and immune responses which were dysregulated during virus infection. Overall our 41 findings suggest a strong link between the prevalence of respiratory illness and the air quality. 42 43 44 45 Keywords: Particulate Matter (PM10), Virus Infection, Infectious Disease, Innate Immunity and 46 Metabolic Pathways-Related Genes. 47India and associated health concerns in term of occurrence of disease [14][15][16][17][18][19]. Although, most of 65 the studies were based on the epidemiological data and cross-sectional studies, there were few 66 studies about involvement of PM in respiratory diseases [20][21][22], asthma [23], cancer [24-27], 67 tuberculosis [28, 29] . It has been known that PM can induce innate immunity and can change the 68 level of cytokines, upon its exposure to the airways of humans [30][31][32][33]. PM were readily associated 69 with respiratory infections such as chronic obstructive pulmonary disease (COPD) [34][35][36][37] and it 70 is also reported to be associated with the respiratory syncytial virus (RSV) and influenza virus 71infections. [38][39][40][41]. Yet these studies are limited to epidemiological, cross -sectional studies [22, 72 42-44]. 73Here, we isolated and characterized PM10 from a heavily industrialized city Bengaluru, India and 74 checked its effect on RNA virus infection. We observed and concluded that PM10 hijacks the innate 75 immune system upon viral infection and significantly enhanced the viral replication of the RNA 76 viruses like new-castle disease virus (NDV), influenza virus -H1N1 (PR8) and H5N1. By 77...

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E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow

Park

Young

2021

Adv Materials Technologies

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Microfluidic lung‐on‐a‐chip systems are increasingly attractive tools for studying lung physiology and function because of their ability to accurately recapitulate spatiotemporal features of the airway tissue microenvironment including cellular organization, tissue architecture, and mechanical cues such as cyclic stretching and airflow. However, most lung‐on‐a‐chip devices to date rely on integrated design elements like membranes for airway cell culture, and focus mainly on enabling on‐chip monitoring and analysis while neglecting the need for off‐chip analysis. Here, an extractable floating liquid‐gel‐based organ‐on‐a‐chip for airway tissue modeling referred to as “E‐FLOAT” is described that is arrayable, scalable, and uniquely amenable to withstand physiologic airflow by microanchors. It is shown that E‐FLOAT can be combined with a custom airflow system that permits controlled injection of particulate matter for air pollution studies. Results show that airflow is critical to efficiently achieving physiologic mimicry of airway epithelium composition, tight junction expression, mucus production, and cilia formation on epithelial cells. It is also shown that E‐FLOAT allows standard on‐chip analysis while permitting complete sample extraction and off‐chip analysis via immunocytochemistry, microscopy, and histological sectioning and staining, thereby expanding the number and types of biological assays that can be used and questions that can be tackled.

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Particle deposition in the human lung: Health implications of particulate matter from different sources

Cited by 220 publications

References 78 publications

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

Imperative role of particulate matter in innate immunity during RNA virus infection

E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow

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