Heart failure (HF) is a major cause of mortality and morbidity in South Korea, imposing substantial physical, emotional, and financial burdens on patients and society. Despite the high burden of symptom and complex care needs of HF patients, palliative care and hospice services remain underutilized in South Korea due to cultural, institutional, and knowledge-related barriers. This position statement from the Korean Society of Heart Failure emphasizes the need for integrating palliative and hospice care into HF management to improve quality of life and support holistic care for patients and their families. By clarifying the role of palliative care in HF and proposing practical referral criteria, this position statement aims to bridge the gap between HF and palliative care services in South Korea, ultimately improving patient-centered outcomes and aligning treatment with the goals and values of HF patients.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Mangiferin is a kind of natural xanthone glycosides and is known to have various pharmacological activities. However, since the beneficial efficacy of this compound has not been reported in retinal pigment epithelial (RPE) cells, this study aimed to evaluate whether mangiferin could protect human RPE ARPE-19 cells from oxidative injury mimicked by hydrogen peroxide (H 2O 2). The results showed that mangiferin attenuated H 2O 2-induced cell viability reduction and DNA damage, while inhibiting reactive oxygen species (ROS) production and preserving diminished glutathione (GSH). Mangiferin also antagonized H 2O 2-induced inhibition of the expression and activity of antioxidant enzymes such as manganese superoxide dismutase and GSH peroxidase, which was associated with inhibition of mitochondrial ROS production. In addition, mangiferin protected ARPE-19 cells from H 2O 2-induced apoptosis by increasing the Bcl-2/Bax ratio, decreasing caspase-3 activation, and blocking poly(ADP-ribose) polymerase cleavage. Moreover, mangiferin suppressed the release of cytochrome c into the cytosol, which was achieved by interfering with mitochondrial membrane disruption. Furthermore, mangiferin increased the expression and activity of heme oxygenase-1 (HO-1) and nuclear factor-erythroid-2 related factor 2 (Nrf2). However, the inhibition of ROS production, cytoprotective and anti-apoptotic effects of mangiferin were significantly attenuated by the HO-1 inhibitor, indicating that mangiferin promoted Nrf2-mediated HO-1 activity to prevent ARPE-19 cells from oxidative injury. The results of this study suggest that mangiferin, as an Nrf2 activator, has potent ROS scavenging activity and may have the potential to protect oxidative stress-mediated ocular diseases.

Background/Aims: Serum hepatitis B virus (HBV) DNA levels and non-invasive liver fibrosis scores are significantly associated with hepatocellular carcinoma (HCC) risk in chronic hepatitis B (CHB) patients. Nonetheless, the relationship between HBV DNA levels and liver fibrosis scores is unclear. Methods: A historical cohort comprising 6,949 non-cirrhotic Korean CHB patients without significant alanine aminotransferase elevation was investigated. The association of HBV DNA levels with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis (FIB)-4 score at baseline was analyzed using general linear models. Results: In HBeAg-negative patients (n=4,868), HBV DNA levels correlated linearly with both APRI and FIB-4 scores. In contrast, in HBeAg-positive patients (n=2,081), HBV DNA levels correlated inversely with both APRI and FIB-4 scores. Across the entire cohort, a significant non-linear parabolic relationship was identified between HBV DNA levels and fibrosis scores, independent of age and other covariates. Notably, moderate viral loads (6–7 log10 IU/mL) corresponded to the highest APRI and FIB-4 scores (p<0.001). Over a median 10-year follow-up, 435 patients (6.3%) developed HCC. Higher APRI scores ≥0.5 and FIB-4 scores ≥1.45 were significantly associated with elevated HCC risk (p<0.001 for both). HBV DNA level remained a significant predictive factor for HCC development, even after adjusting for APRI or FIB-4 scores. Conclusions HBV viral load is significantly correlated with APRI and FIB-4 scores, and is also associated with HCC risk independent of those scores in CHB patients. These findings suggest that HBV DNA level is associated with hepatocarcinogenesis through both direct and indirect pathways.

Background: We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis). Methods: A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination. Results: A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH. Conclusion Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.