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.

Background: Atopic dermatitis (AD) is a common skin disease with a wide range of symptoms. Due to the rapidly changing treatment landscape, regular updates to clinical guidelines are needed. Objective: This study aimed to update the guidelines for the treatment of AD to reflect recent therapeutic advances and evidence-based recommendations. Methods: The Patient characteristics, type of Intervention, Control, and Outcome framework was used to determine 48 questions related to AD management. Evidence was graded, recommendations were determined, and, after 2 voting rounds among the Korean Atopic Dermatitis Association (KADA) council members, consensus was achieved. Results: This guideline provides treatment guidance on advanced systemic treatment modalities for AD. In particular, the guideline offers up-to-date treatment recommendations for biologics and Janus-kinase inhibitors used in the treatment of patients with moderate to severe AD.It also provides guidance on other therapies for AD, along with tailored recommendations for children, adolescents, the elderly, and pregnant or breastfeeding women. Conclusion KADA’s updated AD treatment guidelines incorporate the latest evidence and expert opinion to provide a comprehensive approach to AD treatment. The guidelines will help clinicians optimize patient-specific therapies.

Background: Atopic dermatitis (AD) is a common skin disease with a wide range of symptoms. Due to the rapidly changing treatment landscape, regular updates to clinical guidelines are needed. Objective: This study aimed to update the guidelines for the treatment of AD to reflect recent therapeutic advances and evidence-based practices. Methods: The Patient characteristics, type of Intervention, Control, and Outcome framework was used to determine 48 questions related to AD management. Evidence was graded, recommendations were determined, and, after 2 voting rounds among the Korean Atopic Dermatitis Association (KADA) council members, consensus was achieved. Results: The guidelines provide detailed recommendations on foundational therapies, including the use of moisturizers, cleansing and bathing practices, allergen avoidance, and patient education. Guidance on topical therapies, such as topical corticosteroids and calcineurin inhibitors, is also provided to help manage inflammation and maintain skin barrier function in patients with AD. Additionally, recommendations on conventional systemic therapies, including corticosteroids, cyclosporine, and methotrexate, are provided for managing moderate to severe AD. Conclusion KADA’s updated AD guidelines offer clinicians evidence-based strategies focused on basic therapies, topical therapies, and conventional systemic therapies, equipping them to enhance quality of care and improve patient outcomes in AD management.

Central neurogenic hyperventilation (CNH) is a rare condition that characterized by an increase in the rate and depth of respiration to an extent that produces advanced respiratory alkalosis due to various central nervous disorder. The mechanism of CNH remains unclear. We reported a case of CNH in patients with progressive ischemic stroke in pons, including parabrachial nucleus, which is thought to have disrupted inhibitory impulses to the medullary respiratory center.

Objective: This study evaluated the accuracy of the pre-hospital shock index multiplied by the AVPU scale (PSIAVPU) as a predictor of massive transfusion (MT) and traumatic coagulopathy. Methods: This research was a retrospective single-center study that included patients consecutively presenting to a trauma center between 2017 and 2020. The predictive value of the PSIAVPU for MT, in-hospital mortality, and traumatic coagulopathy was measured using the area under the curve (AUC) of the receiver operating characteristic curve. The AUC of the PSIAVPU was compared with the Reverse Shock Index multiplied by the Glasgow Coma Scale (rSIG) measured at the trauma center presentation. Results: One thousand seven hundred and ninety-two patients were included, of which 163 patients (9.09%) received MT and 195 patients (10.88%) died during their hospital stay. Traumatic coagulopathy was observed in 245 patients. The AUC values for the PSIAVPU in terms of predicting MT, hospital mortality, and traumatic coagulopathy were 0.755, 0.752, and 0.736, respectively. Conclusion In patients with trauma, the predictive power of the PSIAVPU was higher than that of the prehospital shock index and was comparable to that of the rSIG. The PSIAVPU is a useful indicator that can be used easily and quickly for trauma patients at the prehospital stage.

Purpose: To unravel the mechanism regulating the phosphorylation of glycogen synthase kinase 3 (GSK3) and the correlation between the inhibitory phosphorylation of GSK3α and sperm motility in human. Materials and Methods: The phosphorylation and priming phosphorylated substrate-specific kinase activity of GSK3 were examined in human spermatozoa with various motility conditions. Results: In human spermatozoa, GSK3α/β was localized in the head, midpiece, and principal piece of tail and p-GSK3α(Ser21) was enriched in the midpiece. The ratio of p-GSK3α(Ser21)/GSK3α was positively coupled with normal sperm motility criteria of World Health Organization. In high-motility spermatozoa, p-GSK3α(Ser21) phosphotyrosine (p-Tyr) proteins but p-GSK3α(Tyr279) markedly increased together with decreased kinase activity of GSK3 after incubation in Ca2+ containing medium. In high-motility spermatozoa, p-GSK3α(Ser21) levels were negatively coupled with kinase activity of GSK3, and which was deregulated in low-motility spermatozoa. In high-motility spermatozoa, 6-bromo-indirubin-3′-oxime, an inhibitor of kinase activity of GSK3 increased p-GSK3α(Ser21) and p-Tyr proteins. p-GSK3α(Ser21) and p-Tyr protein levels were decreased by inhibition of PKA and Akt. Calyculin A, a protein phosphatase-1/2A inhibitor, markedly increased the p-GSK3α(Ser21) and p-Tyr proteins, and significantly increased the motility of low-motility human spermatozoa. Conclusions Down regulation of kinase activity of GSK3α by inhibitory phosphorylation was positively coupled with human sperm motility, and which was regulated by Ca2+, PKA, Akt, and PP1. Small-molecule inhibitors of GSK3 and PP1 can be considered to potentiate human sperm motility.