• Imaging Science in Dentistry
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• v.52 no.3
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• pp.267-273
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• 2022

Purpose: The aim of this study was to assess the influence of kilovoltage- peak (kVp) and the metal artifact reduction (MAR) tool on the detection of buccal and lingual peri-implant dehiscence in the presence of titanium-zirconia (Ti-Zr) and zirconia (Zr) implants in cone-beam computed tomography (CBCT) images. Materials and Methods: Twenty implant sites were created in the posterior region of human mandibles, including control sites (without dehiscence) and experimental sites (with dehiscence). Individually, a Ti-Zr or Zr implant was placed in each implant site. CBCT scans were performed using a Picasso Trio device, with variation in the kVp setting (70 or 90 kVp) and whether the MAR tool was used. Three oral radiologists scored the detection of dehiscence using a 5-point scale. The area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity were calculated and compared by multi-way analysis of variance (α=0.05). Results: The kVp, cortical plate involved (buccal or lingual cortices), and MAR did not influence any diagnostic values (P>0.05). The material of the implant did not influence the ROC curve values(P>0.05). In contrast, the sensitivity and specificity were statistically significantly influenced by the implant material (P<0.05) with Zr implants showing higher sensitivity values and lower specificity values than Ti-Zr implants. Conclusion: The detection of peri-implant dehiscence was not influenced by kVp, use of the MAR tool, or the cortical plate. Greater sensitivity and lower specificity were shown for the detection of peri-implant dehiscence in the presence of a Zr implant.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.236-243
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• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.

• Journal of the Korean Society of Food Culture
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• v.24 no.6
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• pp.613-630
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• 2009

This thesis, which involves honest life stories of members of the ìtraditionalî Korean generation that lived through the turbulent times of the first half of the twentieth century, assesses the meaning and import of Korean cuisine during an individual Korean's lifetime, as well as the relevant properties of the culinary culture of the traditional generation and how those properties continue to influence the present generation of Koreans. Thus, traditional Korean culinary culture was subdivided into the following four aspects, each of which were exemplified by representative examples. The first of these is slow-food dietary life, which is exemplified by fermented foods. The development of side dishes (panchan) based on fermentation - kimchi, different types of soy and bean paste, salted seafoods, dishes of dried radish or cucumber slices seasoned with soy sauce, and so on - made the quantitative and qualitative supplementation of food possible for traditional Koreans. The second of these aspects, referred to as friendly dietary life, is exemplified by self-sufficiently produced foods. The system of many species and small production suitable with the season made it possible to produce food from sustainable ecological systems and to maintain locally grown food-cultures, each of which was distinguished from others by a local specialty product. The third aspect of the traditional Korean culinary culture involves the same use of medicinal roots and plant materials for foodstuff, and this is exemplified by the use of foods to cure and prevent diseases. The notion, for example, that 'boiled rice is an invigorant' is characteristic of the notion that diet can function in a preventative medical context, and other similar Korean notions illustrate the importance, also, of the curative properties of food. The fourth and final aspect of traditional Korean culinary culture identified herein is creative dietary life, which can be viewed essentially as a Korean adaptation to the turbulence of life during the early $20^{th}$ century in Korea. This trend is exemplified by many Korean foods that were created in response to foreign influences, such as onions, cabbages, curry, etc. which found their place in overall Korean culture through the age of Japanese settlement, as well as the Korean war.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1369-1378
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• 2021

Objective: Few attempts have been made to investigate the prognostic value of dynamic contrast-enhanced (DCE) MRI or dynamic susceptibility contrast (DSC) MRI of non-enhancing, T2-high-signal-intensity (T2-HSI) lesions of glioblastoma multiforme (GBM) in newly diagnosed patients. This study aimed to investigate the prognostic values of DCE MRI and DSC MRI parameters from non-enhancing, T2-HSI lesions of GBM. Materials and Methods: A total of 76 patients with GBM who underwent preoperative DCE MRI and DSC MRI and standard treatment were retrospectively included. Six months after surgery, the patients were categorized into early progression (n = 15) and non-early progression (n = 61) groups. We extracted and analyzed the permeability and perfusion parameters of both modalities for the non-enhancing, T2-HSI lesions of the tumors. The optimal percentiles of the respective parameters obtained from cumulative histograms were determined using receiver operating characteristic (ROC) curve and univariable Cox regression analyses. The results were compared using multivariable Cox proportional hazards regression analysis of progression-free survival. Results: The 95th percentile value (PV) of Ktrans, mean Ktrans, and median Ve were significant predictors of early progression as identified by the ROC curve analysis (area under the ROC curve [AUC] = 0.704, p = 0.005; AUC = 0.684, p = 0.021; and AUC = 0.670, p = 0.0325, respectively). Univariable Cox regression analysis of the above three parametric values showed that the 95th PV of Ktrans and the mean Ktrans were significant predictors of early progression (hazard ratio [HR] = 1.06, p = 0.009; HR = 1.25, p = 0.017, respectively). Multivariable Cox regression analysis, which also incorporated clinical parameters, revealed that the 95th PV of Ktrans was the sole significant independent predictor of early progression (HR = 1.062, p < 0.009). Conclusion: The 95th PV of Ktrans from the non-enhancing, T2-HSI lesions of GBM is a potential prognostic marker for disease progression.

• Journal of Chest Surgery
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• v.57 no.1
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• pp.44-52
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• 2024

Background: Visceral pleural invasion (VPI) is a poor prognostic factor that contributes to the upstaging of early lung cancers. However, the preoperative assessment of VPI presents challenges. This study was conducted to examine intraoperative pleural carcinoembryonic antigen (pCEA) level and maximum standardized uptake value (SUVmax) as predictive markers of VPI in patients with clinical T1N0M0 lung adenocarcinoma. Methods: A retrospective review was conducted of the medical records of 613 patients who underwent intraoperative pCEA sampling and lung resection for non-small cell lung cancer. Of these, 390 individuals with clinical stage I adenocarcinoma and tumors ≤30 mm were included. Based on computed tomography findings, these patients were divided into pleural contact (n=186) and non-pleural contact (n=204) groups. A receiver operating characteristic (ROC) curve was constructed to analyze the association between pCEA and SUVmax in relation to VPI. Additionally, logistic regression analysis was performed to evaluate risk factors for VPI in each group. Results: ROC curve analysis revealed that pCEA level greater than 2.565 ng/mL (area under the curve [AUC]=0.751) and SUVmax above 4.25 (AUC=0.801) were highly predictive of VPI in patients exhibiting pleural contact. Based on multivariable analysis, pCEA (odds ratio [OR], 3.00; 95% confidence interval [CI], 1.14-7.87; p=0.026) and SUVmax (OR, 5.25; 95% CI, 1.90-14.50; p=0.001) were significant risk factors for VPI in the pleural contact group. Conclusion: In patients with clinical stage I lung adenocarcinoma exhibiting pleural contact, pCEA and SUVmax are potential predictive indicators of VPI. These markers may be helpful in planning for lung cancer surgery.

• IMMUNE NETWORK
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• v.24 no.2
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• pp.3.1-3.23
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• 2024

Cigarette smoke extract (CSE)-treated mouse airway epithelial cells (MAECs)-derived exosomes accelerate the progression of chronic obstructive pulmonary disease (COPD) by upregulating triggering receptor expressed on myeloid cells 1 (TREM-1); however, the specific mechanism remains unclear. We aimed to explore the potential mechanisms of CSE-treated MAECs-derived exosomes on M1 macrophage polarization and pyroptosis in COPD. In vitro, exosomes were extracted from CSE-treated MAECs, followed by co-culture with macrophages. In vivo, mice exposed to cigarette smoke (CS) to induce COPD, followed by injection or/and intranasal instillation with oe-TREM-1 lentivirus. Lung function and pathological changes were evaluated. CD68⁺ cell number and the levels of iNOS, TNF-α, IL-1β (M1 macrophage marker), and pyroptosis-related proteins (NOD-like receptor family pyrin domain containing 3, apoptosis-associated speck-like protein containing a caspase-1 recruitment domain, caspase-1, cleaved-caspase-1, gasdermin D [GSDMD], and GSDMD-N) were examined. The expression of maternally expressed gene 3 (MEG3), spleen focus forming virus proviral integration oncogene (SPI1), methyltransferase 3 (METTL3), and TREM-1 was detected and the binding relationships among them were verified. MEG3 increased N6-methyladenosine methylation of TREM-1 by recruiting SPI1 to activate METTL3. Overexpression of TREM-1 or METTL3 negated the alleviative effects of MEG3 inhibition on M1 polarization and pyroptosis. In mice exposed to CS, EXO^-CSE further aggravated lung injury, M1 polarization, and pyroptosis, which were reversed by MEG3 inhibition. TREM-1 overexpression negated the palliative effects of MEG3 inhibition on COPD mouse lung injury. Collectively, CSE-treated MAECs-derived exosomal long non-coding RNA MEG3 may expedite M1 macrophage polarization and pyroptosis in COPD via the SPI1/METTL3/TREM-1 axis.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.

• Korean Journal of Radiology
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• v.23 no.2
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• pp.180-188
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• 2022

Objective: To validate the performance of 3T spin-echo echo-planar imaging (SE-EPI) magnetic resonance elastography (MRE) for staging hepatic fibrosis in a large population, using surgical specimens as the reference standard. Materials and Methods: This retrospective study initially included 310 adults (155 undergoing hepatic resection and 155 undergoing donor hepatectomy) with histopathologic results from surgical liver specimens. They underwent 3T SE-EPI MRE ≤ 3 months prior to surgery. Demographic findings, underlying liver disease, and hepatic fibrosis pathologic stage according to METAVIR were recorded. Liver stiffness (LS) was measured by two radiologists, and inter-reader reproducibility was evaluated using the intraclass correlation coefficient (ICC). The mean LS of each fibrosis stage (F0-F4) was calculated in total and for each etiologic subgroup. Comparisons among subgroups were performed using the Kruskal-Wallis test and Conover post-hoc test. The cutoff values for fibrosis staging were estimated using receiver operating characteristic (ROC) curve analysis. Results: Inter-reader reproducibility was excellent (ICC, 0.98; 95% confidence interval, 0.97-0.99). The mean LS values were 1.91, 2.41, 3.24, and 5.41 kPa in F0-F1 (n = 171), F2 (n = 26), F3 (n = 38), and F4 (n = 72), respectively. The discriminating cutoff values for diagnosing ≥ F2, ≥ F3, and F4 were 2.18, 2.71, and 3.15 kPa, respectively, with the ROC curve areas of 0.97-0.98 (sensitivity 91.2%-95.9%, specificity 90.7%-99.0%). The mean LS was significantly higher in patients with cirrhosis (F4) of nonviral causes, such as primary biliary cirrhosis (9.56 kPa) and alcoholic liver disease (7.17 kPa) than in those with hepatitis B or C cirrhosis (4.28 and 4.92 kPa, respectively). There were no statistically significant differences in LS among the different etiologic subgroups in the F0-F3 stages. Conclusion: The 3T SE-EPI MRE demonstrated high interobserver reproducibility, and our criteria for staging hepatic fibrosis showed high diagnostic performance. LS was significantly higher in patients with non-viral cirrhosis than in those with viral cirrhosis.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1241-1250
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• 2022

Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and Methods: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Results: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). Conclusion: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.