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Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications. [ABSTRACT FROM AUTHOR]

We aim to investigate common diagnoses and risk factors for emergency department (ED) visits as well as those for hospitalization and death after ED visits. This study describes the clinical course of ED visits by using the 2014–2015 population data retrieved from the National Health Insurance Service. Sociodemographic, medical, and behavioral factors were analyzed through multiple logistic regression. Older people were more likely to be hospitalized or to die after an ED visit, but younger people showed a higher risk for ED visits. Females were at a higher risk for ED visits, but males were at a higher risk for ED-associated hospitalization and death. Individuals in the highest quartile of income had a lower risk of ED death relative to lowest income level individuals. Disabilities, comorbidities, and medical issues, including previous ED visits or prior hospitalizations, were risk factors for all ED-related outcomes. Unhealthy behaviors, including current smoking, heavy alcohol consumption, and not engaging in regular exercise, were also significantly associated with ED visits, hospitalization, and death. Common diagnoses and risk factors for ED visits and post-visit hospitalization and death found in this study provide a perspective from which to establish health polices for the emergency medical care system. [ABSTRACT FROM AUTHOR]

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time. [ABSTRACT FROM AUTHOR]

To date, an optimal working model which predicts biological age (BA) with a set of working biomarkers has not been devised to represent the Korean female population. Accuracy of prediction and applicability are required of an optimal set of commonly assessed biomarkers to provide information on the health status. The goal of this study was to identify a set of biomarkers that represent the aging process and to develop and compare different BA prediction models to elucidate the most fitting and applicable model for providing information on health status in the Korean female population. Using a series of selection processes, eight clinically assessable variables were selected by analyzing relations between 31 clinical variables and chronologic age in 912 normal, healthy individuals among 3642 female participants with ages ranging from 30 to 80 years. The multiple linear regression (MLR), principal component analysis (PCA), and the Klemera-Doubal (KDM) statistical methods were applied to obtain three different sets of BA prediction models. These three models were assessed by calculating and performing the coefficient determinations (r 2 ), regression slopes, effect sizes, pairwise t -tests, and Bland-Altman plots. The BA models were further compared for the applicability by calculating the BAs of clinical risk groups. MLR showed the narrowing effects at the either ends of the age spectrum with greatest effect sizes. PCA showed the greatest degree of dispersion and deviation from the regression center. These MLR and PCA trends were also exhibited by clinically risk groups. In conclusion, the KDM BA prediction model based on the selected biomarkers was found to provide the most reliable and stable results for the practical assessment of BA. [ABSTRACT FROM AUTHOR]

The term user experience (UX) encompasses the concepts of usability and affective engineering. However, UX has not been defined clearly. In this study, a literature survey, user interview and indirect observation were conducted to develop definitions of UX and its elements. A literature survey investigated 127 articles that were considered to be helpful to define the concept of UX. An in-depth interview targeted 14 hands-on workers in the Korean mobile phone industry. An indirect observation captured daily experiences of eight end-users with mobile phones. This study collected various views on UX from academia, industry, and end-users using these three approaches. As a result, this article proposes definitions of UX and its elements: usability, affect, and user value. These results are expected to help design products or services with greater levels of UX. © 2011 Wiley Periodicals, Inc. This article was published online on 20 October 2011. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected 13 June 2013. [ABSTRACT FROM AUTHOR]

Abstract: Recently, many countries have been making an effort to reduce their carbon dioxide (CO2) emission, and as part of such effort, the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Kyoto Protocol in 1997. South Korea is very likely to be included in the second batch of countries that must reduce their greenhouse gas emission after the end of the implementation of the Kyoto Protocol in 2012. Reducing the country’s CO2 emission, however, can have an impact on the economy. Therefore, in this study, the correlations between South Korea’s economic growth, CO2 emission, and energy consumption were analyzed. The analysis period was from Q1 1991 to Q4 2011, and the analysis methods that were used were regression analysis for the relational analysis among the various overall indices, and the Markov switching model for a more detailed analysis. The results of the analyses showed that South Korea’s economic growth and CO2 emission were coincidental. The correlation analysis of the country’s economic growth and energy consumption showed a significant correlation between economic growth and fossil fuels, which emit CO2, such as coal in the industrial sector, petroleum products in the industrial and transportation sectors, and liquefied natural gas (LNG) in the residential/ commercial and industrial sectors. It is expected that the results of this study will pave the way for the conduct of various researches on controlling the country’s CO2 emission management, and for suggestions for such to be given, such as policies for reducing the energy consumption in each sector, using the methodology proposed in this study. [Copyright &y& Elsevier]