Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Probiotics exert various effects on the body and provide different health benefits. Previous reports have demonstrated that the P8 protein (P8), isolated from Lactobacillus rhamnosus, has anticancer properties. However, its efficacy in stem cells and normal cells has not been reported. In this study, the effect of P8 on cell proliferation and wound healing was evaluated, investigating its underlying mechanism. Based on scratch assay results, we demonstrated that P8 treatment significantly increases wound healing by activating the cell cycle and promoting stem cell stemness.Cellular mechanisms were further investigated by culturing stem cells in a medium containing Lactobacillus-derived P8 protein, revealing its promotion of cell proliferation and migration. Also, it is found that P8 enhances the expression of stemness markers, such as OCT4 and SOX2, along with activation of the mitogen-activated protein kinase (MAPK) signaling and Hippo pathways. These results indicate that P8 can promote cell growth by increasing stem cell proliferation, migration, and stemness in a manner associated with MAPK and Hippo signaling, which could contribute to the increased wound healing after P8 treatment. Furthermore, P8 could promote wound healing in keratinocytes by activating the MAPK signaling pathways. These results suggest that P8 might be a promising candidate to enhance stem cell culture efficiency by activating cell proliferation, and enhance therapeutic effects in skin diseases.

Four soluble factors—putrescine, glucosamine, nicotinamide, and signal transducer and activator of transcription 3 (STAT3) inhibitor BP-1-102—were shown to differentiate bone marrow mononucleated cells (BMNCs) into functional insulin-producing cells (IPCs) in vitro. Transplantation of these IPCs improved hyperglycemia in diabetic mice. However, the role of endogenous BMNC regeneration in this effect was unclear. This study aimed to evaluate the effect of these factors on in vivo BMNC differentiation into IPCs in diabetic mice. Mice were orally administered the factors for 5 days, twice at 2-week intervals, and monitored for 45–55 days. Glucose tolerance, glucose-stimulated insulin secretion, and pancreatic insulin content were measured. Chimeric mice harboring BMNCs from insulin promoter luciferase/green fluorescent protein (GFP) transgenic mice were used to track endogenous BMNC fate. These factors lowered blood glucose levels, improved glucose tolerance, and enhanced insulin secretion. Immunostaining confirmed IPCs in the pancreas, showing the potential of these factors to induce β-cell regeneration and improve diabetes treatment.

Background/Aims: Gastric neuroendocrine tumors (GNETs), once rare, have become more prevalent due to the increased use of endoscopy and increased physician awareness. The clinical characteristics and long-term outcomes of GNET management were explored in this study. Methods: The clinical data of 69 patients who treated at Seoul National University Hospital between January 2013 and October 2023 were retrospectively studied. Baseline characteristics, recurrence rates, associated factors, and overall survival rates were analyzed. Results: Of the tumors, 71.0% were grade 1, 24.6% were grade 2, 1.4% were grade 3, and 2.9% were poorly differentiated. In terms of tumor type, 69.6% were type I, 1.4% were type II, and 29.0% were type III. A significant proportion of patients with grade 1 tumors received more endoscopic treatment, whereas a significant proportion of patients with grade 2 tumors underwent surgery or chemotherapy (p=0.015). The overall 5-year survival and recurrence rates were 93.8% and 7.25% (5/69), respectively. Among five patients who experienced recurrence, three had metachronous recurrence, all of which were type I; the remaining two patients exhibited distant hepatic metastasis, encompassing types I and III. The time to recurrence was 1 to 9.8 years.Margin positivity (p=0.002) and invasion deeper than the submucosal layer (p=0.007) were associated with higher recurrence rates. However, there was no significant association between recurrence and intestinal metaplasia, atrophic gastritis, or Helicobacter pylori infection. Conclusions Most patients with GNETs in this study had grade I and type I tumors, and the overall prognosis was favorable. Patients with risk factors for recurrence warrant further investigation. Those presenting margin positivity or deep invasion after resection should be closely monitored and undergo follow-up examinations, as necessary.

Allogeneic stem cell transplantation (allo-SCT) is a salvage treatment option for patients with relapsed or refractory lymphoid malignancies. However, the clinical variables impacting outcomes in these patients remain unclear. We analyzed 58 patients who underwent allo-SCT for lymphoid malignancies, including B-cell lymphoma (BCL, n = 20), Hodgkin’s disease (n = 3), multiple myeloma (n = 9), natural killer/T-cell lymphoma (NK/TCL, n = 4), and TCL (n = 22).The median progression-free survival (PFS) was 27.4 months, while the median overall survival (OS) was 30.6 months.In univariate analysis, human leukocyte antigen (HLA) matching and complete remission status post-transplantation were associated with improved PFS and OS. However, only post-transplant response remained significant for both sur‑ vival outcomes in the multivariate analysis. Moreover, HLA matching was associated with a significantly improved PFS in patients with BCL and NK/TCL, but with better OS only in those with BCL. Complete remission after transplantation was associated with better PFS and OS in patients with BCL, NK/TCL, and TCL. Our results indicate that post-transplant response is an important prognostic indicator in allo-SCT for lymphoid malignancies and may guide clinical decisions and additional treatment.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)