• Korean Journal of Radiology
• /
• v.22 no.7
• /
• pp.1021-1033
• /
• 2021

Objective: To assess the expression of vascular normalization genes in different molecular subtypes of breast cancer and to determine whether molecular subtypes with a higher vascular normalization gene expression can be identified using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). Materials and Methods: This prospective study evaluated 306 female (mean age ± standard deviation, 50 ± 10 years), recruited between January 2014 and August 2017, who had de novo breast cancer larger than 1 cm in diameter (308 tumors). DCE MRI followed by IVIM DWI studies using 11 different b-values (0 to 1200 s/mm²) were performed on a 1.5T MRI system. The Tofts model and segmented biexponential IVIM analysis were used. For each tumor, the molecular subtype (according to six [I-VI] subtypes and PAM50 subtypes), expression profile of genes for vascular normalization, pericytes, and normal vascular signatures were determined using freshly frozen tissue. Statistical associations between imaging parameters and molecular subtypes were examined using logistic regression or linear regression with a significance level of p = 0.05. Results: Breast cancer subtypes III and VI and PAM50 subtypes luminal A and normal-like exhibited a higher expression of genes for vascular normalization, pericyte markers, and normal vessel function signature (p < 0.001 for all) compared to other subtypes. Subtypes III and VI and PAM50 subtypes luminal A and normal-like, versus the remaining subtypes, showed significant associations with K^trans, k_ep, v_p, and IAUGC_BN90 on DEC MRI, with relatively smaller values in the former. The subtype grouping was significantly associated with D, with relatively less restricted diffusion in subtypes III and VI and PAM50 subtypes luminal A and normal-like. Conclusion: DCE MRI and IVIM parameters may identify molecular subtypes of breast cancers with a different vascular normalization gene expression.

• Journal of The Korean Society of Clinical Toxicology
• /
• v.21 no.2
• /
• pp.128-134
• /
• 2023

Purpose: The Prescott nomogram has been utilized to forecast hepatotoxicity from acute acetaminophen poisoning. In developing countries, emergency medical centers lack the resources to report acetaminophen concentrations; thus, the commencement and cessation of treatment are based on the reported dose. This study investigated risk factors that can predict acetaminophen detection after 15 hours for safe treatment termination. Methods: Data were collected from an urban emergency medical center from 2010 to 2020. The study included patients ≥14 years of age with acute acetaminophen poisoning within 15 hours. The correlation between risk factors and detection of acetaminophen 15 hours after ingestion was evaluated using logistic regression, and the area under the curve (AUC) was calculated. Results: In total, 181 patients were included in the primary analysis; the median dose was 150.9 mg/kg and 35 patients (19.3%) had acetaminophen detected 15 hours after ingestion. The dose per weight and the time to visit were significant predictors for acetaminophen detection after 15 hours (odds ratio, 1.020 and 1.030, respectively). The AUCs were 0.628 for a 135 mg/kg cut-off value and 0.658 for a cut-off 450 minutes, and that of the combined model was 0.714 (sensitivity: 45.7%, specificity: 91.8%). Conclusion: Where acetaminophen concentrations are not reported during treatment following the UK guidelines, it is safe to start N-acetylcysteine immediately for patients who are ≥14 years old, visit within 15 hours after acute poisoning, and report having ingested ≥135 mg/kg. Additional N-acetylcysteine doses should be considered for patients visiting after 8 hours.

• Annals of Occupational and Environmental Medicine
• /
• v.35
• /
• pp.14.1-14.10
• /
• 2023

Background: Shift work is known to cause changes in the circadian rhythm of the human body and adversely affect not only physical health but also mental health. Some studies have demonstrated the correlation between shift work and thyroid stimulating hormone (TSH), a hormone that changes according to the diurnal rhythm, but few studies have reported the different TSH levels according to the shift work type. This study aimed to investigate changes in TSH according to the shift work type. Methods: This study included 1,318 female workers who had a medical checkup at a university hospital in Changwon from 2015 to 2019. Shift work types were classified as non-shift work, regular 2 shifts, and irregular three shifts, and a TSH ≥ 4.2 mIU/L was defined as abnormal. A general linear model (GLM) was used to compare the TSH levels and the risk of subclinical hypothyroidism in each year, and a binary logistic analysis was performed using a generalized estimation equation (GEE) to compare the risk of subclinical hypothyroidism over the 5-year period. Results: Of the 1,318 participants included in this study, 363, 711, and 244 were non-shift, two-shift, and irregular three-shift workers, respectively. In the GEE analysis, after adjusting for age, body mass index, smoking, and alcohol consumption, the odds ratios (ORs) were 1.81 (95% confidence interval [CI]: 1.15-2.86; p = 0.011) in 2 shifts and 2.02 (95% CI: 1.23-3.32; p = 0.006) in irregular three shifts, compared to non-shift. Conclusions: Our results showed that shift work had a higher risk of subclinical hypothyroidism than non-shift work and that there was a significant difference in the risk of subclinical hypothyroidism according to the shift work type. These findings suggest that the shift work type can be considered in future thyroid function tests and evaluations.

• Journal of Korean Medicine for Obesity Research
• /
• v.24 no.1
• /
• pp.41-53
• /
• 2024

Objectives: We used the Korea Health Panel Annual Data 2019 to analyze factors related to visits to both Korean medicine and Western medicine (WM) outpatient clinics among patients with overweight and obesity. Methods: The inclusion criteria for this study are as follows: 1) adults over 18 years of age, 2) overweight or obese with a body mass index of 25.0 or more, 3) visited WM outpatient clinics at least once during 2019. Total 2,963 individuals were included in WM group or integrative medicine (IM) group. Using the Andersen healthcare utilization model, factors related to healthcare utilization of the participants were classified. Binomial logistic regression analysis was used to analyze factors associated with IM use. Results: Among the participants, 80.49% (n=2,385) were assigned to WM group and 19.51% (n=578) to IM group. As a result of the regression analysis, factors significantly related to the use of IM included the elderly over 65 years of age, sex (men), college or higher education level, residential area (Gwangju/Jeolla/Jeju), presence of cancer, and presence of musculoskeletal disease. The main diagnosis associated with both WM and IM use was most frequently musculoskeletal conditions. Also, IM group received WM treatment for musculoskeletal conditions more frequently compared to WM group. Conclusions: This study is the first to analyze healthcare utilization patterns among overweight or obese patients in Korea. The current findings suggest that the presence of musculoskeletal conditions, especially in this population, may be strongly associated with concurrent use of IM services.

• Annals of Hepato-Biliary-Pancreatic Surgery
• /
• v.28 no.1
• /
• pp.14-24
• /
• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.14 no.1
• /
• pp.1-10
• /
• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Weed & Turfgrass Science
• /
• v.7 no.3
• /
• pp.275-282
• /
• 2018

Miscanthus species are known as a genus of eco-friendly and low-maintenance cost ornamental grasses. Plug and cutting methods were tested for multi-propagation of most promising ornamental Miscanthus species in greenhouse and field plot. The plug formation period with three different cell sizes with four cultivars (M. sinensis 'Andersson', 'Strictus', 'Gracillimus', 'Variegatus') were evaluated the seedling development stages with two irrigation types of the over-head and the bottom watering in greenhouse and field plot afterward during 2015-2016 season. In seedling plug test, the size of tray cell affected the plug formation. Bottom irrigation resulted positively on plant height, weight, root and tiller development compared with the over-head irrigation. Plug cell size affected the plant growth in the field after transplanting. All of the 3 Miscanthus species showed higher rates of successful propagation at the lower nodes before inflorescence formation (vegetative growth stage). To analyze the survival factors of M. xgiganteus cutting, the cutting time, node part, and culm diameter were tested as independent variables with the binary logistic model. The survival probability was influenced by node part and culm diameter significantly. The third and fifth node parts showed 0.12 (8X higher failure probability) and 0.02 (50X higher failure probability) times less survival probability. It means the survival probability will be increased by using older and lower part of cuttings during a vegetative growth stage before inflorescences of M. xgiganteus.

• Journal of Hospice and Palliative Care
• /
• v.18 no.2
• /
• pp.128-135
• /
• 2015

Purpose: As the National Health Insurance Service (NHIS) began to cover home oxygen therapy (HOT) services from 2006, it is expected that the new services have contributed to overall positive outcome of patients with chronic obstructive pulmonary disease (COPD). We examined whether the usage of HOT has helped slow down the progression of COPD. Methods: We examined hospital claim data (N=10,798) of COPD inpatients who were treated in 2007~2012. We performed ${\chi}^2$ tests to analyze the differences in the changes to respiratory impairment grades. Multiple logistic regression analysis was used to identify factors that are associated with the use of HOT. Finally, a generalized linear mixed model was used to examine association between the HOT treatment and changes to respiratory impairment grades. Results: A total of 2,490 patients had grade 1 respiratory impairment, and patients with grades 2 or 3 totaled 8,308. The OR for use of HOT was lower in grade 3 patients than others (OR: 0.33, 95% CI: 0.30~0.37). The maintenance/mitigation in all grades, those who used HOT had a higher OR than non-users (OR: 1.41, 95% CI: 1.23~1.61). Conclusion: HOT was effective in maintaining or mitigating the respiratory impairment in COPD patients.

• Clinical and Experimental Pediatrics
• /
• v.50 no.10
• /
• pp.987-994
• /
• 2007

Purpose : This study investigated the clinical significance of AN in children and adolescents with obesity induced metabolic complications. Methods : Forty-nine patients who had obesity induced metabolic complications were participated in this cross-sectional study. Obesity induced metabolic complications are as follows: hypertension, dyslipidemia, impaired fasting glucose (IFG), impaired glucose tolerance (IGT), nonalcoholic steatohepatitis (NASH), homeostasis model assessment of insulin resistance (HOMA-IR)>3.16. Clinical characteristics, such as, age, percentage-weight-for-height (PWH), pubertal status, blood pressure (BP), fasting plasma insulin level, fasting and post-oral glucose tolerance test 2-hour glucose levels, liver function test, lipid profile, HOMA-IR were compared according to the presence of AN. Results : Sixty-five percent of patients had AN, 57.1% NASH, 57.1% dyslipidemia, 55.1% hypertension, 46.9% IFG, 24.5% HOMA-IR>3.16 and 16.2% IGT. The patients who were moderately to severely obese with AN had higher incidence of IGT and HOMA-IR>3.16. The patients with AN had significantly higher diastolic BP ($79.4{\pm}6.9$ vs $75.4{\pm}5.6mmHg$), fasting levels of plasma insulin ($10.6{\pm}6.0$ vs $6.2{\pm}5.4{\mu}IU/mL$), HOMA-IR index ($2.6{\pm}1.4$ vs $1.4{\pm}1.3$) and PWH ($42.4{\pm}13.0$ vs $34.3{\pm}1.8%$). The increasing tendency for the presence of AN was significantly related to the cumulative number of obesity induced metabolic complications. Binary logistic regression analysis revealed that the presence of AN was significantly associated with fasting plasma insulin level, PWH and IFG. Conclusion : AN could be useful as a clinical surrogate of obesity induced metabolic complications.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_3
• /
• pp.1109-1123
• /
• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.