Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: s/Aims: Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related deaths in South Korea. This study evaluated the characteristics of Korean patients newly diagnosed with HCC in 2016-2018. Methods: Data from the Korean Primary Liver Cancer Registry (KPLCR), a representative database of patients newly diagnosed with HCC in South Korea, were analyzed. This study investigated 4,462 patients with HCC registered in the KPLCR in 2016-2018. Results: The median patient age was 63 years (interquartile range, 55-72). 79.7% of patients were male. Hepatitis B infection was the most common underlying liver disease (54.5%). The Barcelona Clinic Liver Cancer (BCLC) staging system classified patients as follows: stage 0 (14.9%), A (28.8%), B (7.5%), C (39.0%), and D (9.8%). The median overall survival was 3.72 years (95% confidence interval, 3.47-4.14), with 1-, 3-, and 5-year overall survival rates of 71.3%, 54.1%, and 44.3%, respectively. In 2016-2018, there was a significant shift toward BCLC stage 0-A and Child-Turcotte-Pugh liver function class A (P<0.05), although survival rates did not differ by diagnosis year. In the treatment group (n=4,389), the most common initial treatments were transarterial therapy (31.7%), surgical resection (24.9%), best supportive care (18.9%), and local ablation therapy (10.5%). Conclusions Between 2016 and 2018, HCC tended to be diagnosed at earlier stages, with better liver function in later years. However, since approximately half of the patients remained diagnosed at an advanced stage, more rigorous and optimized HCC screening strategies should be implemented.

The Korean Enhanced Recovery After Surgery (ERAS) Committee within the Korean Society of Surgical Metabolism and Nutrition was established to develop ERAS guidelines tailored to the Korean context. This guideline focuses on creating the most current evidence-based practice guidelines for ERAS purposes, based on systematic reviews. All key questions targeted randomized controlled trials exclusively, and if fewer than 2 were available, studies employing propensity score matching were also included. Recommendations for each key question were marked with strength of recommendation and level of evidence following internal and external review processes by the committee.

The Korean Enhanced Recovery After Surgery (ERAS) Committee within the Korean Society of Surgical Metabolism and Nutrition was established to develop ERAS guidelines tailored to the Korean context. This guideline focuses on creating the most current evidence-based practice guidelines for ERAS purposes, based on systematic reviews. All key questions targeted randomized controlled trials exclusively, and if fewer than 2 were available, studies employing propensity score matching were also included. Recommendations for each key question were marked with strength of recommendation and level of evidence following internal and external review processes by the committee.

Purpose: This study aimed to investigate the relationship between body composition and glaucoma by analyzing the associations between anthropometric and ocular parameters. Methods: A total of 494 eyes from 247 patients were reviewed from a general health examination database at a tertiary hospital. Anthropometric parameters were assessed using a multifrequency bioelectrical impedance device. Mean ocular perfusion pressure (MOPP) was calculated based on systolic and diastolic blood pressures and intraocular pressure (IOP). Retinal thickness and other ocular parameters were analyzed for their association with body composition. Results: A total of 221 eyes from 221 patients, including 104 with glaucoma, were enrolled in the final analysis. The prevalence of sarcopenia was significantly higher in patients with glaucomatous damage than in those without (p = 0.025). Higher IOP showed significant associations with lower MOPP (p < 0.001), higher body mass index (BMI; p = 0.001), and higher waist to hip ratio (p = 0.001). Retinal thickness was not significantly associated with body composition parameters, including BMI and appendicular lean mass adjusted with squared height. Higher MOPP was significantly correlated with lower IOP (p < 0.001), higher BMI (p < 0.001), higher waist to hip ratio (p < 0.001), and higher appendicular lean mass divided by squared height (p = 0.009). Conclusions Skeletal muscle mass and BMI were significantly associated with MOPP. Since low MOPP is a known risk factor for glaucoma, its association with skeletal muscle mass may indicate a relationship between systemic muscle health, ocular blood perfusion, and glaucomatous damage. Further large-scale studies are needed to validate these associations between skeletal muscle mass and glaucoma and explore their clinical implications.

Background: This study aimed to investigate the prevalence, management, and comorbidities of diabetes mellitus among Korean adults. Methods: Data from the Korea National Health and Nutrition Examination Survey (2019–2022) were analyzed to assess the prevalence, treatment, risk factors, and comorbidities of diabetes. Comparisons between young and older adults with diabetes were emphasized. Results: Among Korean adults aged ≥30 years, the prevalence of diabetes is 15.5% during 2021–2022. Of these, 74.7% were aware of their condition, 70.9% received antidiabetic treatment, and only 32.4% achieved glycosylated hemoglobin (HbA1c) <6.5%. Moreover, 15.9% met the integrated management targets, which included HbA1c <6.5%, blood pressure <140/85 mm Hg, and low-density lipoprotein cholesterol <100 mg/dL. In young adults aged 19 to 39 years, the prevalence of diabetes was 2.2%. Among them, 43.3% were aware of their condition, 34.6% received treatment, and 29.6% achieved HbA1c <6.5%. Obesity affected 87.1%, and 26.9% had both hypertension and hypercholesterolemia. Among adults aged ≥65 years, the prevalence of diabetes was 29.3%, with awareness, treatment, and control rates of 78.8%, 75.7%, and 31.2%, respectively. Integrated management targets (HbA1c <7.5%, hypertension, and lipids) were achieved by 40.1%. Conclusion Diabetes mellitus remains highly prevalent among Korean adults, with significant gaps in integrated glycemic, blood pressure, and lipid control. Older adults with diabetes show higher awareness and treatment rates but limited integrated management outcomes. Young adults with diabetes bear a significant burden of obesity and comorbidities, alongside low awareness and treatment rates. Therefore, early intervention programs, education, and strategies tailored to younger populations are urgently required.

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.