This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Purpose: The purpose of the study was to analyze the priorities for educational content regarding precautions to be taken to prevent safety accidents for employees in school foodservice using the Borich priority formula and the Locus for Focus model. Methods: A survey was conducted in February 2019 on 194 employees in elementary school and 122 employees in middle- and high school foodservice in the Jeonbuk area. Demographic characteristics, status of safety accidents, safety education, and their importance and performance levels were assessed using a self-administered questionnaire. The priorities for the educational content on precautions to prevent safety accidents were based on a 3-step analysis method, including the paired sample t-test, Borich priority formula, and the Locus for Focus Model. Results: The average perceived importance of the precautions to be taken against safety accidents of employees in elementary-, middle-, and high schools was higher compared to the average performance of the employees (p < 0.001). The top priority for elementary school employees was caution against falls during the cleaning of the gas hood and the trench in the kitchen. In addition, ‘awareness of chemical signs’ was added as one of the top priorities of middle- and high school employees. The second highest priority items were ‘do stretching’, ‘safely adjusting workbench height’, ‘keeping the right attitude’, ‘using assistive devices when moving heavy things’, and ‘checking the material safety data sheet’, which were the same for all elementary, middle- and high school employees. Conclusion Thus, to improve the educational preparedness of employees in the area of safety precautions, eight safety/accident prevention items should be included in the safety education program.

Background and Objectives: This study investigated whether an artificial intelligence computer-assisted diagnosis (AI-CAD) software recently developed in our institution named the Severance Artificial intelligence program (SERA) could show similar diagnostic performance for thyroid cancers using ultrasonographic (US) images from a mobile phone (SERA_M) compared to using images directly downloaded from the pictures archive and communication system (PACS) (SERA_P). Materials and Methods: From October 2019 to December 2019, 259 thyroid nodules from 259 patients were included. SERA was run on original and mobile images to evaluate SERA_P and SERA_M. Nodules were categorized according to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). To compare diagnostic performance, a logistic regression analysis was conducted using the Generalized Estimating Equation. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve, and compared using the Delong Method. Results: There were 40 cancers (15.4%) and 219 benign lesions (84.6%). The AUC and sensitivity of SERA_M (0.82 and 85%, respectively) were not statistically different from SERA_P (0.8 and 75%, respectively) (p=0.526 and p=0.091, respectively). The AUC of radiologists (0.856) was not significantly different compared to SERA_P and SERA_M (p=0.163 and p=0.414, respectively). The sensitivity of radiologists (77.5%) was not statistically different compared to SERA_P and SERA_M (p=0.739 and p=0.361, respectively). Conclusion AI-CAD software using pictures taken by a mobile phone showed comparable diagnostic performance with the same software using images directly from PACS.

Purpose: The aim of this study is to evaluate the safety and efficacy of ex vivo activated and expanded natural killer (NK) cell therapy (SNK01) plus pembrolizumab in a randomized phase I/IIa clinical trial. Materials and Methods: Overall, 18 patients with advanced non–small cell lung cancer (NSCLC) and a programmed death ligand 1 tumor proportion score of 1% or greater who had a history of failed frontline platinum-based therapy were randomized (2:1) to receive pembrolizumab every 3 weeks +/– 6 weekly infusions of SNK01 at either 2×109 or 4×109 cells per infusion (pembrolizumab monotherapy vs. SNK01 combination). The primary endpoint was safety, whereas the secondary endpoints were the objective response rate (ORR), progression-free survival (PFS), overall survival, and quality of life. Results: Since no dose-limiting toxicity was observed, the maximum tolerated dose was determined as SNK01 4×109 cells/dose. The safety data did not show any new safety signals when SNK01 was combined with pembrolizumab. The ORR and the 1-year survival rate in the NK combination group were higher than those in patients who underwent pembrolizumab monotherapy (ORR, 41.7% vs. 0%; 1-year survival rate, 66.7% vs. 50.0%). Furthermore, the median PFS was higher in the SNK01 combination group (6.2 months vs. 1.6 months, p=0.001). Conclusion Based on the findings of this study, the NK cell combination therapy may consider as a safe treatment method for stage IV NSCLC patients who had a history of failed platinum-based therapy without an increase in adverse events.

Purpose: We investigated the possible effects of diabetic ketoacidosis (DKA) at the initial diagnosis of type 1 diabetes mellitus (T1DM) on the clinical outcomes of pediatric patients. Methods: Medical records of children and adolescents with newly diagnosed T1DM seen in the Ajou University Hospital from January 2008 to August 2020 were reviewed and analyzed. Results: Among 129 diagnosed T1DM patients, 40.3% presented with DKA. Although demographic and basic characteristics did not differ between DKA and non-DKA patients, DKA patients needed a significantly higher insulin dosage than non-DKA patients for 2 years after diagnosis. However, control of glycated hemoglobin was not different between the DKA and non-DKA groups during the observation period. In the biochemical analysis, C-peptide, insulin-like growth factor-1, and insulin-like growth factor binding protein 3, high-density lipoprotein cholesterol, free T4, and T3 values were lower, but thyroid-stimulating hormone, initial serum glucose, uric acid, total cholesterol, triglyceride, and low-density lipoprotein cholesterol values were higher in DKA patients than non-DKA patients at the diagnosis of T1DM; however, these differences were temporarily present and disappeared with insulin treatment. Other clinical outcomes, such as height, thyroid function, and urine microalbumin level, did not vary significantly between the DKA and non-DKA groups during 5 years of follow-up. Conclusion DKA at initial presentation reflects the severity of disease progression, and the deleterious effects of DKA seem to impact insulin secretion. Although no difference in long-term prognosis was found, early detection of T1DM should help to reduce DKA-related islet damage and the socioeconomic burden of T1DM.

Advanced or metastatic breast cancer affects multiple organs and is a leading cause of cancer-related death. Cancer metastasis is associated with epithelial-mesenchymal metastasis (EMT). However, the specific signals that induce and regulate EMT in carcinoma cells remain unclear. PRR16/Largen is a cell size regulator that is independent of mTOR and Hippo signalling pathways. However, little is known about the role PRR16 plays in the EMT process. We found that the expression of PRR16 was increased in mesenchymal breast cancer cell lines. PRR16 overexpression induced EMT in MCF7 breast cancer cells and enhances migration and invasion. To determine how PRR16 induces EMT, the binding proteins for PRR16 were screened, revealing that PRR16 binds to Abl interactor 2 (ABI2). We then investigated whether ABI2 is involved in EMT. Gene silencing of ABI2 induces EMT, leading to enhanced migration and invasion. ABI2 is a gene that codes for a protein that interacts with ABL proto-oncogene 1 (ABL1) kinase. Therefore, we investigated whether the change in ABI2 expression affected the activation of ABL1 kinase. The knockdown of ABI2 and PRR16 overexpression increased the phosphorylation of Y412 in ABL1 kinase. Our results suggest that PRR16 may be involved in EMT by binding to ABI2 and interfering with its inhibition of ABL1 kinase. This indicates that ABL1 kinase inhibitors may be potential therapeutic agents for the treatment of PRR16-related breast cancer.