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.

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.

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: Providing optimal nutrition to patients with acute respiratory failure is difficult because nutritional requirements vary according to disease severity and comorbidities. In 2021, the National Medical Center initiated a protocol for screening upon admission and regular monitoring by a multidisciplinary nutritional support team (NST), for all patients in the medical intensive care unit (ICU). This study aimed to evaluate the effects of routine NST monitoring and active intervention on the clinical outcomes of patients with acute respiratory failure. Methods: Patients with acute respiratory failure requiring high-flow nasal cannula, non-invasive ventilation, or mechanical ventilation were included. The primary outcome was 28-day mortality after ICU admission. Secondary outcomes included the supplied/target calorie ratio, supplied/target protein ratio on day 7, and complications. Results: In total, 152 patients were included in the analysis. The patients were divided into a pre-monitoring (n=96) and post-monitoring groups (n=56). More patients in the post-monitoring group received NST intervention and had earlier initiation of enteral feeding. In survival analysis, 28-day mortality was significantly lower in post-monitoring group (adjusted hazard ratio, 0.42; 95% CI, 0.24–0.74). The ratio of achievement for required calories and protein on day 7 was higher, but not significantly, in the post-monitoring group. No significant differences were observed in the incidence of complications. Conclusions Regular NST monitoring in the ICU could have contributed to a reduced risk of 28-day mortality in critically ill patients with acute respiratory failure.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Purpose: This study investigated the incidence of secondary malignancy in multiple myeloma (MM) patients compared with that in the general population using a population-based database covering all residents in Korea. Materials and Methods: Based on the national health insurance system in Korea, all people primarily diagnosed with MM between January 1, 2010 to December 31, 2018 were identified. A total of 9,985 MM patients aged ≥ 20 years in Korea were included. Results: Among them, 237 (2.4%) developed secondary malignancies by 2018. The standardized incidence rates (SIRs) of all secondary malignancies in MM patients were 0.87 (95% confidence interval [CI], 0.76 to 0.98), with a higher incidence of hematologic malignancies than in the general population with an SIR of 3.80 (95% CI, 2.61 to 5.00). The incidence rates of both lymphoid malignancy (SIR, 3.56; 95% CI, 2.31 to 4.82) and myeloid malignancy (SIR, 3.78; 95% CI, 1.16 to 6.39) were higher in MM patients than in the general population. In contrast, a lower incidence of solid cancer was observed in MM patients than in the general population (SIR, 0.76, 95% CI, 0.65 to 0.86). There was no significant difference in survival in MM patients without secondary malignancies, with hematologic malignancy, and with solid cancer (p=0.413). Conclusion MM patients had a greater risk of secondary malignancies, especially hematologic malignancies, than the general population. Future studies with a focus on analyzing patients’ history, treatment details, and genetic information in various stages of MM patients are needed to better understand the mechanism behind this increased risk.