A Numerical Simulation of the Airflow and Aerosol Particle Deposition in a Realistic Airway Model of a Healthy Adult

Modeling Realistic Geometries in Human Intrathoracic Airways

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

Air pollution is associated with increased incidence-rate of head and neck cancers: A nationally representative ecological study

INTRODUCTION

Early studies show conflicting findings regarding particulate matter ≤ 2.5 μm in diameter (PM2.5) exposure and development of head and neck cancers (HNC). We analyzed the relationship between PM2.5 exposure and various types of HNC in a nationally representative ecological sample.

METHODS

We determined HNC incidence in 608 US counties from 2011 to 2019 using the Surveillance, Epidemiology and End Results (SEER) Program from the National Cancer Institute. We also collected information on sociodemographic factors from SEER and data on smoking and alcohol intake from CDC data frames (county level). PM2.5 exposure levels were estimated using satellite and meteorological data via previously validated general additive models. Flexible semi-nonparametric regression models were used to test the relationship between PM2.5 exposure levels and HNC incidence, adjusting for demographics, socioeconomic factors, and comorbidity.

RESULTS

Increased PM2.5 exposure levels were associated with higher incidence-rates of oral cavity and pharyngeal cancers controlling for confounders in our primary analyses (IRR = 1.04, 95 % CI 1.01, 1.07, p = 0.02 per 1 μg/m3 increase in PM2.5). This relationship was maintained after adjusting for multiple testing (Holm s method, p = 0.04) and in ordinary least squares (OLS) regression (β = 0.17, 95 % CI 0.01, 0.57, p = 0.01). Increased exposure was also associated with other HNC: esophagus (IRR = 1.06, 95 % CI 1.01, 1.11, p = 0.02), lip (IRR = 1.16, 95 % CI 1.03, 1.31, p = 0.01), tonsil (IRR = 1.10, 95 % CI 1.03, 1.16, p < 0.01). However, these relationships were not maintained in secondary analyses.

CONCLUSIONS

This nationally representative ecological study shows that increased levels of air pollution are associated with increased incidence of overall oral cavity and pharyngeal cancers in the US.

Study on the coal dust deposition fraction and site in the upper respiratory tract under different particle sizes and labor intensities

In order to study the dust exposure amount and coal dust deposition rule of coal miners under different labor intensity in coal mine environment, an airflow-particle two-phase coupling calculation model of human upper respiratory tract was established based on Euler-Lagrange framework, and the airflow field in the upper respiratory tract and the characteristics of coal dust deposition were simulated and studied. By comparing the experimental data, the relative error of simulation is in the range of 1.5 %-11.2 %. The results showed that the total deposition fraction of 1 μm dust was the smallest (0.61-1.20 %), and was relatively less affected by respiratory intensity, and the overall distribution was uniform. When the dust particle size increased to 7.07 μm, the total dust deposition fraction in the nasal cavity, pharynx and larynx was in the range of 11.10 %-20.91 %, and increased with the respiratory intensity. When the dust particle size was large, the dust particles of 20 μm and 80 μm were mostly concentrated in the front of the nasal cavity, and the deposition amount of 80 μm dust was about 99.52 %. It was found that with the increase of dust particle size or the increase of labor intensity, the possibility of dust being transported into lungs became smaller. The fitting function of 7.07 μm dust escape rate and labor intensity was obtained, for example, Y_7.07μm = 91.73-0.22n (n is labor intensity), and the escape rate of dust with 7.07 μm particle size was up to 88.90 %. Most of them escape from the upper respiratory tract and enter the lungs, which provides theoretical guidance for quantifying the accumulated dust exposure amount in the lungs and monitoring respiratory dust concentration.