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.

Anticancer activities of Licochalcone D (LCD) in human colorectal cancer (CRC) cells HCT116 and oxaliplatin-resistant HCT116 (HCT116-OxR) were determined. Cell viability assay and soft agar assay were used to analyze antiproliferative activity of LCD.Flow cytometry was performed to determine effects of LCD on apoptosis, cell cycle distribution, reactive oxygen species (ROS), mitochondrial membrane potential (MMP) dysfunction, and multi-caspase activity in CRC cells. Western blot analysis was used to monitor levels of proteins involved in cell cycle and apoptosis signaling pathways. LCD suppressed the growth and anchorageindependent colony formation of both HCT116 and HCT116-OxR cells. Cell cycle analysis by flow cytometry indicated that LCD induced cell cycle arrest and increased cells in sub-G1 phase. In parallel with the antiproliferative effect of LCD, LCD up-regulated levels of p21 and p27 while downregulating cyclin B1 and cdc2. In addition, phosphorylation levels of JNK and p38 mitogen-activated protein kinase (MAPK) were increased by LCD. Inhibition of these kinases somehow prevented the antiproliferative effect of LCD. Moreover, LCD increased ROS and deregulated mitochondrial membrane potential, leading to the activation of multiple caspases. An ROS scavenger N-acetyl-cysteine (NAC) or pan-caspase inhibitor Z-VAD-FMK prevented the antiproliferative effect of LCD, supporting that ROS generation and caspase activation mediated LCD-induced apoptosis in CRC cells. In conclusion, LCD exerted antitumor activity in CRC cells by inducing ROS generation and phosphorylation of JNK and p38 MAPK. These results support that LCD could be further developed as a chemotherapeutic agent for treating CRC.

Background: Post-transplantation cyclophosphamide (PTCy) and anti-thymocyte globulin (ATG) are common pro‑ phylactic strategies for graft-versus-host disease (GVHD) after haploidentical hematopoietic stem cell transplantation (haplo-HSCT). Interleukin (IL)-6 is a surrogate marker for cytokine release syndrome (CRS) and acute GVHD.Method The clinical outcomes and complications of haplo-HSCT with PTCy plus ATG versus PTCy monotherapy were compared according to serum IL-6 levels at Chungnam National University Hospital (Daejeon, South Korea) from Jan‑ uary 2019 to February 2023. Results: Forty patients who underwent haplo-HSCT were analyzed. A significant difference in IL-6 levels was observed between the PTCy plus ATG and PTCy alone groups (7.47 ± 10.55 vs. 117.65 ± 127.67; p = 0.003). More patients in the PTCy plus ATG group had a CRS grade of 0 than in the PTCy alone group (p < 0.001). Serum IL-6 levels were associated with grades II–IV acute GVHD (r = 0.547, p < 0.001). The cumulative incidence (CI) of grades II–IV acute GVHD was significantly higher in the PTCy alone group (67.9% vs. 4.8%; p < 0.001). No significant difference in the CI for chronic GVHD was detected between the PTCy plus ATG and PTCy alone groups (72.1% vs. 82.0%; p = 0.730). The CI of 1-year non-relapse mortality was significantly higher in the PTCy alone group than in the PTCy plus ATG group (42.2% vs. 15.9%; p = 0.022). The 1-year overall survival (OS) was significantly better in the PTCy plus ATG group (75.9% vs. 35.3%; p = 0.011). The 1-year GVHD-free, relapse-free survival rate was 29.4% in the PTCy alone group and 54.0% in the PTCy plus ATG group (p = 0.038). Conclusion Serum IL-6 levels were higher in the PTCy alone group than in the PTCy plus ATG group. The addition of ATG before stem cell infusion affected IL-6 levels and reduced the incidences of CRS and grade II–IV acute GVHD in haplo-HSCT patients. This study suggests that PTCy plus ATG as GVHD prophylaxis in haplo-HSCT is beneficial in terms of clinical outcomes and complications of HSCT.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.