• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.2
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• pp.113-124
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• 2004

A computer software program, called BIDAS (BIoassay Data Analysis Software) is developed to interpret the bioassay measurement data in terms of intakes and the committed effective dose using the human respiratory tract model (HRTM), gastrointestinal tract (GI-tract) model and biokinetic models currently recommended by the International Commission on Radiological Protection (ICRP) to describe the behavior of the radioactive materials within the body. The program consists of three modules; first, a database module to manage the bioassay data, second, another databasee module to store the predicted bioassay quantities of each radionuclide and finally, a computational module to estimate the intake and committed effective dose calculated with the bioassay quantity measurement values from either an acute or chronic exposure of the radionuclies within the body. This paper describes the features of the program as well as the quality assurance check results of the BIDAS software program.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.551-560
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• 2003

Particle deposition in human lungs was investigated theoretically by using asymmetric five-lobe lung model. The volumes of each of the five lobes were different, thereby forming an asymmetric lung structure. The tidal volume and flow rate of each lobe were scaled according to lobar volume. The total and regional deposition with various breathing patterns were calculated by means of tracking volume segments and accounting for particle loss during inhalation and exhalation. The deposition fractions were obtained for each airway generation and lung lobe, and dominant deposition mechanisms were investigated for different size particles. Results show that the tidal volume and flow rate have a characteristic influence on particle deposition. The total deposition fraction increases with an increase in tidal volume for all particle sizes. However, flow rate has dichotomous effects: a higher flow rate results in a sharp increase in deposition for large size particles, but decreases deposition for small size particles. Deposition distribution within the lung shifts proximally with higher flow rate whereas deposition peak shifts to the deeper lung region with larger tidal volume. Deposition fraction in each lobe was proportional to its volume. Among the three main deposition mechanisms, diffusion was dominant for particles < 0.5 ${\mu}{\textrm}{m}$ whereas sedimentation and impaction were most influential for larger size particles. Impaction was particularly dominant for particles> 8 ${\mu}{\textrm}{m}$. The results may prove to be useful for estimating deposition dose of inhaled pollutant particles at various breathing conditions.

• Biomolecules & Therapeutics
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• v.26 no.6
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• pp.576-583
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• 2018

Human rhinoviruses (HRV) are one of the major causes of common cold in humans and are also associated with acute asthma and bronchial illness. Heat-shock protein 90 (Hsp90), a molecular chaperone, is an important host factor for the replication of single-strand RNA viruses. In the current study, we examined the effect of the Hsp90 inhibitor pochonin D, in vitro and in vivo, using a murine model of human rhinovirus type 1B (HRV1B) infection. Our data suggested that Hsp90 inhibition significantly reduced the inflammatory cytokine production and lung damage caused by HRV1B infection. The viral titer was significantly lowered in HRV1B-infected lungs and in Hela cells upon treatment with pochonin D. Infiltration of innate immune cells including granulocytes and monocytes was also reduced in the bronchoalveolar lavage (BAL) by pochonin D treatment after HRV1B infection. Histological analysis of the lung and respiratory tract showed that pochonin D protected the mice from HRV1B infection. Collectively, our results suggest that the Hsp90 inhibitor, pochonin D, could be an attractive antiviral therapeutic for treating HRV infection.

• Journal of Radiation Protection and Research
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• v.40 no.4
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• pp.216-222
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• 2015

Facilities processing raw materials containing naturally occurring radioactive materials (NORM) may give rise to enhanced radiation dose to workers due to chronic inhalation of airborne particulates. Internal radiation dose due to particulate inhalation varies depending on particulate properties, including size, shape, density, and absorption type. The objective of the present study was to assess inhalation dose sensitivity to physicochemical properties of airborne particulates. Committed effective doses to workers resulting from inhalation of airborne particulates were calculated based on International Commission on Radiological Protection 66 human respiratory tract model. Inhalation dose generally increased with decreasing particulate size. Committed effective doses due to inhalation of $0.01{\mu}m$ sized particulates were higher than doses due to $100{\mu}m$ sized particulates by factors of about 100 and 50 for $^{238}U$ and $^{230}Th$, respectively. Inhalation dose increased with decreasing shape factor. Shape factors of 1 and 2 resulted in dose difference by about 18 %. Inhalation dose increased with particulate mass density. Particulate mass densities of $11g{\cdot}cm^{-3}$ and $0.7g{\cdot}cm^{-3}$ resulted in dose difference by about 60 %. For $^{238}U$, inhalation doses were higher for absorption type of S, M, and F in that sequence. Committed effective dose for absorption type S of $^{238}U$ was about 9 times higher than dose for absorption F. For $^{230}Th$, inhalation doses were higher for absorption type of F, M, and S in that sequence. Committed effective dose for absorption type F of $^{230}Th$ was about 16 times higher than dose for absorption S. Consequently, use of default values for particulate properties without consideration of site specific physiochemical properties may potentially skew radiation dose estimates to unrealistic values up to 1-2 orders of magnitude. For this reason, it is highly recommended to consider site specific working materials and conditions and use the site specific particulate properties to accurately access radiation dose to workers at NORM processing facilities.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.