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.

Objective: Temporary anchorage devices (TADs) have considerably reduced the need for anterior segmental osteotomy (ASO) in patients with Class I malocclusion. Most previous studies have been published before the widespread use of TADs, thus warranting new guidelines for determining the optimal approach for surgery and orthodontic treatment. This study aimed to establish guidelines on the choice between ASO and non-ASO (NASO) based on soft tissue considerations. Methods: Sixty-seven patients diagnosed with skeletal Class II malocclusion were divided into the ASO (n = 31) and NASO (n = 36) groups. Cephalometric analyses were used to compare the initial and final records to assess the effect of treatment on soft tissues. The interlabial gap, upper lip anterior to the E-line, lower lip anterior to the E-line, H-angle, upper lip to the nasion-perpendicular line, and nasolabial angle were evaluated. In particular, a proportional difference indicator between the upper and lower lips relative to the pogonion angle between the facial plane and CK line was presented, followed by statistics analyses. Statistical significance was set at P < 0.05. Results: Both groups demonstrated normal proportions of the upper and lower lips; however, significant differences favoring ASO over NASO in terms of soft tissue changes were observed for several variables. Conclusions ASO is advised if the required adjustment for the upper and lower lips is –4.0 mm and –5.0 mm, respectively. For modifications of –2.0 mm, NASO is preferred. This study provides clinical guidelines on the choice between ASO and NASO based on the required lip movement measurements.

Connective tissue disease (CTD), comprising a range of autoimmune disorders, is often accompanied by lung involvement, which can lead to life-threatening complications. The primary types of CTDs that manifest as interstitial lung disease (ILD) include rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, mixed CTD, idiopathic inflammatory myopathies, and systemic lupus erythematosus. CTD-ILD presents a significant challenge in clinical diagnosis and management due to its heterogeneous nature and variable prognosis. Early diagnosis through clinical, serological, and radiographic assessments is crucial for distinguishing CTD-ILD from idiopathic forms and for implementing appropriate therapeutic strategies. Hence, we have reviewed the multiple clinical manifestations and diagnostic approaches for each type of CTD-ILD, acknowledging the diversity and complexity of the disease. The importance of a multidisciplinary approach in optimizing the management of CTD-ILD is emphasized by recent therapeutic advancements, which include immunosuppressive agents, antifibrotic therapies, and newer biological agents targeting specific pathways involved in the pathogenesis. Therapeutic strategies should be customized according to the type of CTD, the extent of lung involvement, and the presence of extrapulmonary manifestations. Additionally, we aimed to provide clinical guidance, including therapeutic recommendations, for the effective management of CTD-ILD, based on patient, intervention, comparison, outcome (PICO) analysis.

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.

Hangovers from alcohol consumption cause symptoms like headaches, nausea, and fatigue, disrupting daily activities and overall well-being. Over time, they can also lead to inflammation and oxidative stress. Effective hangover relief alleviates symptoms, prevents dehydration, and replenishes energy needed for daily tasks. Natural foods considered high in antioxidants and antiinflammatory properties may aid in the hepatic breakdown of alcohol. The study aims to investigate the impact of glutathione or its enriched yeast extract, which is recognized for its antioxidant characteristics, on alcohol metabolism and alleviating hangovers in a rat model exposed to binge drinking. In this study, glutathione and its enriched yeast extract controlled hangover behaviour patterns, including locomotor activity. Additionally, it enhanced the activities of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) following ethanol ingestion (3 g/kg). Further, the incorporation of glutathione led to an increase in the expression of antioxidant enzymes, such as SOD and catalase, by activating the nuclear erythroid 2-related factor 2 (Nrf2) signaling pathway.This activation reduced the excessive production of reactive oxygen species (ROS) and malondialdehyde. Next, glutathione modulated the activity of cytochrome P450 2E1 (CYP2E1) and the protein expressions of Bax and Bcl2. Besides, in vitro and in vivo investigations with glutathione demonstrated a regulating effect on the pan-s-glutathionylation and its associated protein expression, glutaredoxin 1 (Grx1), glutathione-S-transferase Pi (GST-π), and glutathione reductase (GR). Together, these findings suggest that glutathione or its enriched yeast extract as a beneficial dietary supplement for alleviating hangover symptoms by enhancing alcohol metabolism and its associated Nrf2/Keap1 signalings.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Pneumocystis jirovecii pneumonia (PJP) is a life-threatening infection commonly observed in immunocompromised patients, necessitating prompt diagnosis and treatment. This review evaluates the diagnostic performance of various tests used for PJP diagnosis through a comprehensive literature review. Additionally, we propose a diagnostic algorithm tailored to non-human immunodeficiency virus immunocompromised patients, considering the specific characteristics of current medical resources in Korea.

Purpose: The incidence of breast cancer in older females is increasing with increased life expectancy. This study analyzed tumor characteristics and oncological outcomes in patients aged ≥ 70 years compared to patients in a younger postmenopausal group, in conjunction with their adherence to treatment guidelines. Methods: Patients aged ≥ 50 years, newly diagnosed with breast cancer, were divided into two age categories: ≥ 70 years and 50–69 years. All patients underwent curative surgery at Korea University Guro Hospital between January 2009 and December 2019. Clinical data on tumor subtype, histopathological grade, and clinical stage, along with treatment details were collected. Disease-free survival, distant recurrence-free survival, and breast cancer-specific survival rates were determined. Results: Of 1,199 patients, 166 (13.8%) were ≥ 70 years at the time of surgery. The diseasefree, distant recurrence-free, and breast cancer-specific survival rates were significantly lower in patients aged ≥ 70 years (p < 0.05). In a subgroup analysis, human epidermal growth factor receptor 2-positive tumors were the only subtype with a statistically significant difference in survival outcomes, and adherence to the guidelines was strongly linked to a better prognosis. Conclusion Patients aged ≥ 70 years had lower disease-free, distant recurrence-free, and breast cancer-specific survival rates compared to younger postmenopausal patients aged 50–69 years. With the continuous increase in life expectancy and advances in healthcare, it is critical to optimize treatment strategies for older patients with breast cancer to improve survival outcomes and enhance their quality of life.

Purpose: This study analyzed the pathological complete response (pCR) rates, long-term outcomes, and biological features of human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer patients undergoing neoadjuvant treatment. Methods: This single-center study included 1,667 patients who underwent neoadjuvant chemotherapy from 2008 to 2014. Patients were categorized by HER2 status, and their clinicopathological characteristics, chemotherapy responses, and recurrence-free survival (RFS) rates were analyzed. Results: Patients with HER2-low tumors were more likely to be older (p = 0.081), have a lower histological grade (p < 0.001), and have hormone receptor (HorR)-positive tumors (p < 0.001). The HER2-positive group exhibited the highest pCR rate (23.3%), followed by the HER2-zero (15.5%) and HER2-low (10.9%) groups. However, the pCR rate did not differ between HER2-low and HER2-zero tumors in the HorR-positive or HorR-negative subgroups.The 5-year RFS rates increased in the following order: HER2-low, HER2-positive, and HER2-zero (80.0%, 77.5%, and 74.5%, respectively) (log-rank test p = 0.017). A significant survival difference between patients with HER2-low and HER2-zero tumors was only identified in HorR-negative tumors (5-year RFS for HER2-low, 74.5% vs. HER2-zero, 66.0%; log-rank test p-value = 0.04). Multivariate survival analysis revealed that achieving a pCR was the most significant factor associated with improved survival (hazard ratio [HR], 4.279; p < 0.001).Compared with HER2-zero, the HRs for HER2-low and HER2-positive tumors were 0.787 (p = 0.042) and 0.728 (p = 0.005), respectively. After excluding patients who received HER2-targeted therapy, patients with HER2-low tumors exhibited better RFS than those with HER2-zero (HR 0.784, p = 0.04), whereas those with HER2-positive tumors exhibited no significant difference compared with those with HER2-low tumors (HR, 0.975; p = 0.953). Conclusion Patients with HER2-low tumors had no significant difference in pCR rate compared to HER2-zero but showed better survival, especially in HorR-negative tumors.Further investigation into biological differences is warranted.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.