Purpose: Accumulating evidence has clarified that gut dysbiosis is involved in lung cancer development and progression. Although the relationship between tumors and gut microbiota has been extensively studied using clinical samples, no studies have examined the association between mutant epidermal growth factor receptor (EGFR)–induced lung carcinogenesis and dysbiosis in gut microbiota. Therefore, we investigated the gut microbiota profiles in stool samples from human lung-specific conditional EGFR-mutant transgenic mice during lung tumor carcinogenesis. Materials and Methods: Stool samples were collected before tamoxifen treatment (V1) and at each time point following mutant EGFR expression in lung tissue (V2) and lung tumor appearance (V3). Fecal 16S rRNA taxonomy was analyzed to assess microbial diversity, composition, and dynamic changes at each time point. Results: We found that microbiota richness and diversity were significantly elevated when tumors developed and grew in the lung. Phylogenetic analysis of the microbial community revealed that Lachnospiraceae, Ruminococcaceae, Porphyromonadaceae, Rhodospirillaceae, Odoribacteraceae, and Desulfovibrionaceae showed a significant increase at the V3 stage compared to the V1 stage at the family level. In contrast, Lactobacillaceae, Bacteroidaceae, Muribaculaceae, Coriobacteriaceae, and Rikenellaceae significantly decreased at the V3 stage compared to the V1 stage. Furthermore, Lactobacillus species, also known as short chain fatty acid-producing bacteria, were relatively abundant at the V1 stage but were depleted with the occurrence of lung tumors at the V3 stage. Conclusion Changes in gut microbiota, such as Lactobacillus species, may be a predictive factor for the emergence and progression of tumors in an animal model of lung adenocarcinoma induced by mutant EGFR.

This case report highlights the significance of pre-operative screening for congenital anomalies such as uterine horn aplasia and renal agenesis in cats undergoing elective ovariohysterectomy (OHE), which may induce or exacerbate postoperative acute kidney injury (AKI). An eight-month-old female mixed-breed cat was presented for an elective ovariohysterectomy. The absence of a left kidney with left-sided segmental uterine aplasia was observed in the cat during the OHE procedure. AKI was diagnosed on postoperative day (POD) 1, characterized by renal enlargement and abnormal levels of renal function parameters on blood tests. After treatment renal function blood tests normalised on POD 12.This case underscores the necessity of pre-operative screening for renal agenesis in cats undergoing OHE to prevent and manage potential postoperative AKI.Identifying such congenital anomalies can improve surgical planning and postoperative care, and ultimately improving patient outcomes.

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.

Background: With the increasing demand for surgical procedures in dermatology, resident education in surgical dermatology has become important for delivering high-quality treatment.However, it remains unclear if a sufficient number of residency programs with quality standards exist, as there has been little research on this subject in South Korea. Objective: To identify the status of surgical dermatology education among residents and assess dermatologists’ perceptions of the subject. Methods: A 35-question survey was developed and distributed to all resident training hospitals and local clinics listed by the Korean Society of Dermatologic Surgery. Only third- and fourthyear residents were included and board-certified specialists from training hospitals and local clinics responded to the surveys. Results: Survey participants included 88 residents and 120 specialists of whom one-quarter of the residents attended regular monthly educational sessions. Most residents (93%) participated in cosmetic procedures, and many performed laser therapy. However, the opportunity for toxin or filler injection was rare, with only 12% of the residents having experience with filler injections. In response, 49% of residents and 32% of specialists said that more cosmetic training was required, whereas 28% of residents and 50% of specialists said that more training for both cosmetic and conventional surgeries was necessary. Conclusion The survey demonstrated a need for more training programs in surgical dermatology during residency and a perception gap between residents and specialists. Therefore, developing educational residency programs that focus on basic dermatologic surgery principles and their applications in cosmetic procedures is essential.

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.