Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

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: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Objectives: ：This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods: ：The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the ‘Agricultural Cohort’ registered at the Centers for Farmers` Safety and Health Center. Trainedinterviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those withscores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results：Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6%in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included maritalstatus (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions ：The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.

Objectives: ：This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods: ：The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the ‘Agricultural Cohort’ registered at the Centers for Farmers` Safety and Health Center. Trainedinterviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those withscores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results：Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6%in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included maritalstatus (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions ：The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.

Objectives: ：This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods: ：The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the ‘Agricultural Cohort’ registered at the Centers for Farmers` Safety and Health Center. Trainedinterviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those withscores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results：Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6%in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included maritalstatus (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions ：The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.

The incidence of gonorrhea has increased significantly in recent years in the United States, especially among sexually active twenty-year-olds. Although the incidence of gonorrhea has decreased in Korea since the early 2000s, it is still common among people in their twenties. Nucleic acid amplification test (NAAT) is the most sensitive diagnostic test for detecting gonococcal infection. Gram-staining is a simple and useful laboratory test for diagnosing symptomatic male gonococcal urethritis. Although bacterial culture can be used to detect antimicrobial susceptibility, its sensitivity is lower than that of NAAT. Treatment for uncomplicated gonorrhea infection is a single intramuscular injection of ceftriaxone 500 mg. Doxycycline (100 mg twice daily for 7 days) is added if there is a possibility of co-infection with chlamydia. If ceftriaxone is difficult to use, spectinomycin 2 g can be injected intramuscularly in Korea. Patients with gonorrhea should have repeated examinations within three months at the exposure site because of a high risk of re-infection. A person diagnosed with gonorrhea should discuss the nature of the infection, the importance of informing partners, when sexual activity can resume, and how to reduce the risk of sexually transmitted infections.

Objectives: ：This study aims to explore the prevalence of depressive symptoms among elderly residents in the relatively stable rural areas of Jeju and to examine the relationships between levels of depression, sociodemographic factors, and health habits. Methods: ：The study site was within rural Jeju, where elderly individuals aged 65 and older were randomly selected from the ‘Agricultural Cohort’ registered at the Centers for Farmers` Safety and Health Center. Trainedinterviewers conducted surveys using the Short Form Geriatric Depression Scale (sGDS-K), defining those withscores of 6 or above as experiencing depressive symptoms for the analysis. Other variables such as sex, age, educational level, marital status, annual income, subjective health status, underlying disease, perceived stress levels, smoking, and drinking status were also recorded Results：Out of 533 subjects, the prevalence of depressive symptoms was 35.3%, with 28.5% in male and 45.6%in female (p<0.001). Factors significantly associated with the prevalence of depressive symptoms included maritalstatus (p=0.014), educational level (p<0.001), annual income (p=0.034), subjective health status (p<0.001), perceived stress level (p<0.001), feeling of despair (p<0.001) and suicidal ideas (p<0.001). Multivariate logistic regression analysis revealed that subjective health status, perceived stress level, and feelings of despair were associated with the prevalence of depressive symptoms. Conclusions ：The high prevalence of depressive symptoms among the rural elderly in Jeju highlights the need for targeted mental health interventions. Addressing sociocultural factors and improving early detection and intervention strategies can help reduce the socioeconomic impact of depression in this population.