Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: Objective of this study is to evaluate the association between high risk of depression and metabolic diseases such as hypertension, diabetes, and dyslipidemia in Korean cervical cancer patients. Methods: A total of 330 women with cervical cancer were included in this study, using data from the Korea National Health and Nutrition Examination Survey from 2010 to 2021. Participants were categorized into two groups—high risk of depression and non-depression—based on their answers to survey items related to depression. A multivariate logistic regression analysis was used to evaluate the influence of metabolic diseases on high risk of depression in patients with cervical cancer. Results: A total of 78 (23.64%) and 252 (76.36%) women were classified into the high risk of depression and non-depression groups, respectively. In multivariate logistic regression analysis adjusting for age, menopausal status, and smoking status, diabetes was associated with an odds ratio of 2.47 (95% confidence interval: 1.205, 5.071) for high risk of depression in cervical cancer patients. However, among the metabolic diseases, hypertension, and dyslipidemia were not associated with high risk of depression in patients with cervical cancer. Conclusion This study suggests that diabetes may be associated with a increased risk of high risk of depression in cervical cancer patients. Therefore, appropriate treatment of diabetes in cervical cancer patients may contribute to lowering the risk of depression in the future.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Background: Primary Merkel cell carcinoma of the salivary gland is currently not listed in the World Health Organization classification. However, cases of Merkel cell type neuroendocrine carcinomas of the salivary gland with perinuclear cytokeratin 20 positivity have been intermittently reported. We here investigated the clinicopathologic features of additional cases. Methods: Data of four cases of Merkel cell type small cell neuroendocrine carcinoma of the salivary gland were retrieved. To confirm the tumors’ primary nature, clinical records and pathologic materials were reviewed. Optimal immunohistochemical staining was performed to support the diagnosis. Results: All tumors were located in the parotid gland. Possibilities of metastasis were excluded in all cases through a meticulous clinicopathological review. Tumor histology was consistent with the diagnosis of small cell neuroendocrine carcinoma. Tumors’ immunohistochemical phenotypes were consistent with Merkel cell carcinoma, including Merkel cell polyomavirus large T antigen positivity in two of the four cases. Conclusions Merkel cell carcinomas can originate in salivary glands and are partly associated with Merkel cell polyomavirus infection as in cutaneous Merkel cell carcinomas.

Emerging evidence suggests that systemic inflammation may play a critical role in neurological disorders. Recent studies have shown the connection between inflammatory bowel diseases (IBD) and neurological disorders, revealing a bidirectional relationship through the gut-brain axis.Immunotherapies, such as Treg cells infusion, have been proposed for IBD. However, the role of adaptive immune cells in IBD-induced neuroinflammation remains unclear. In this study, we established an animal model for IBD in mice with severe combined immune-deficient (SCID), an adaptive immune deficiency, to investigate the role of adaptive immune cells in IBD-induced neuroinflammation. Mice were fed 1%, 3%, or 5% dextran sulfate sodium (DSS) for 5 days. We measured body weight, colon length, disease activity index (DAI), and crypt damage. Pro-inflammatory cytokines were measured in the colon, while microglial morphology, neuronal count, and inflammatory cytokines were analyzed in the brain. In the 3% DSS group, colitis symptoms appeared at day 7, with reduced colon length and increased crypt damage showing colitis-like symptoms. By day 21, colon length and crypt damage persisted, while DAI showed recovery. Although colonic inflammation peaked at day 7, no significant increase in inflammatory cytokines or microglial hyperactivation was observed in the brain. By day 21, neuroinflammation was detected, albeit with a slight delay, in the absence of adaptive immune cells. The colitis-induced neuroinflammation model provides insights into the fundamental immune mechanisms of the gut-brain axis and may contribute to developing immune cell therapies for IBD-induced neuroinflammation.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

Background/Aims: The Korean guidelines for Helicobacter pylori treatment were revised in 2020, however, the extent of adherence to these guidelines in clinical practice remains unclear. Herein, we initiated a prospective, nationwide, multicenter registry study in 2021 to evaluate the current management of H.pylori infection in Korea. Methods: This interim report describes the adherence to the revised guidelines and their impact on firstline eradication rates. Data on patient demographics, diagnoses, treatments, and eradication outcomes were collected using a web-based electronic case report form. Results: A total of 7,261 patients from 66 hospitals who received first-line treatment were analyzed.The modified intention-to-treat eradication rate for first-line treatment was 81.0%, with 80.4% of the prescriptions adhering to the revised guidelines. The most commonly prescribed regimen was the 14-day clarithromycin-based triple therapy (CTT; 42.0%), followed by tailored therapy (TT; 21.2%), 7-day CTT (14.1%), and 10-day concomitant therapy (CT; 10.1%). Time-trend analysis demonstrated significant increases in guideline adherence and the use of 10-day CT and TT, along with a decrease in the use of 7-day CTT (all p<0.001). Multivariate logistic regression analysis revealed that guideline adherence was significantly associated with first-line eradication success (odds ratio, 2.03; 95% confidence interval, 1.61 to 2.56; p<0.001). Conclusions The revised guidelines for the treatment of H. pylori infection have been increasingly adopted in routine clinical practice in Korea, which may have contributed to improved first-line eradication rates. Notably, the 14-day CTT, 10-day CT, and TT regimens are emerging as the preferred first-line treatment options among Korean physicians.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: Objective of this study is to evaluate the association between high risk of depression and metabolic diseases such as hypertension, diabetes, and dyslipidemia in Korean cervical cancer patients. Methods: A total of 330 women with cervical cancer were included in this study, using data from the Korea National Health and Nutrition Examination Survey from 2010 to 2021. Participants were categorized into two groups—high risk of depression and non-depression—based on their answers to survey items related to depression. A multivariate logistic regression analysis was used to evaluate the influence of metabolic diseases on high risk of depression in patients with cervical cancer. Results: A total of 78 (23.64%) and 252 (76.36%) women were classified into the high risk of depression and non-depression groups, respectively. In multivariate logistic regression analysis adjusting for age, menopausal status, and smoking status, diabetes was associated with an odds ratio of 2.47 (95% confidence interval: 1.205, 5.071) for high risk of depression in cervical cancer patients. However, among the metabolic diseases, hypertension, and dyslipidemia were not associated with high risk of depression in patients with cervical cancer. Conclusion This study suggests that diabetes may be associated with a increased risk of high risk of depression in cervical cancer patients. Therefore, appropriate treatment of diabetes in cervical cancer patients may contribute to lowering the risk of depression in the future.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.