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: 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.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Asthma is a chronic inflammatory disorder of the lungs that results in airway inflammation and narrowing. BS012 is an herbal remedy containing Asarum sieboldii, Platycodon grandiflorum, and Cinnamomum cassia extracts. To elucidate the anti-asthma effect of BS012, this study analyzed the immune response, respiratory protection, and changes in metabolic mechanisms in an ovalbumininduced allergic asthma mouse model. Female BALB/c mice were exposed to ovalbumin to induce allergic asthma. Bronchoalveolar lavage fluid and plasma were analyzed for interleukin and immunoglobulin E levels. Histological analyses of the lungs were performed to measure morphological changes. Apoptosis-related mediators were assayed by western blotting. Plasma and lung tissue metabolomic analyses were performed to investigate the metabolic changes. A T-helper-2-like differentiated cell model was used to identify the active components of BS012. BS012 treatment improved inflammatory cell infiltration, mucus production, and goblet cell hyperplasia in lung tissues. BS012 also significantly downregulated ovalbumin-specific immunoglobulin E in plasma and T-helper-2-specific cytokines, interleukin-4 and -5, in bronchoalveolar lavage fluid. The lungs of ovalbumin-inhaled mice exhibited nerve growth factor-mediated apoptotic protein expression, which was significantly attenuated by BS012 treatment. Ovalbumin-induced abnormalities in amino acid and lipid metabolism were improved by BS012 in correlation with its anti-inflammatory properties and normalization of energy metabolism. Additionally, the differentiated cell model revealed that N-isobutyl-dodecatetraenamide is an active component that contributes to the anti-allergic properties of BS012. The current findings demonstrate the anti-allergic and respiratory protective functions of BS012 against allergic asthma, which can be considered a therapeutic candidate.

Background: It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia. Methods: This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment. Results: After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events. Conclusion The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

OBJECTIVES: We examined the associations of socioeconomic factors, health behaviors, and comorbidities with early-onset lung cancer. METHODS: The study included 6,794,287 individuals aged 20-39 years who participated in a Korean national health check-up program from 2009 to 2012. During the follow-up period, 4,684 participants developed lung cancer. Multivariable Cox regression analysis was used to estimate the independent associations of potential risk factors with incident lung cancer. RESULTS: Older age (multivariable hazard ratio [mHR], 1.13; 95% confidence interval [CI], 1.12 to 1.14) and female sex (mHR, 1.62; 95% CI, 1.49 to 1.75) were associated with increased lung cancer risk. Current smoking was also associated with elevated risk (<10 pack-years: mHR, 1.12; 95% CI, 1.01 to 1.24; ≥10 pack-years: mHR, 1.30; 95% CI, 1.18 to 1.45), but past smoking was not. Although mild alcohol consumption (<10 g/day) was associated with lower lung cancer risk (mHR, 0.92; 95% CI, 0.86 to 0.99), heavier alcohol consumption (≥10 g/day) was not. Higher income (highest vs. lowest quartile: mHR, 0.86; 95% CI, 0.78 to 0.94), physical activity for at least 1,500 metabolic equivalent of task-min/wk (vs. non-exercisers: mHR, 0.83; 95% CI, 0.69 to 0.99) and obesity (vs. normal weight: mHR, 0.89; 95% CI, 0.83 to 0.96) were associated with lower lung cancer risk, whereas metabolic syndrome was associated with increased risk (mHR, 1.13; 95% CI, 1.03 to 1.24). CONCLUSIONS In young adults, age, female sex, smoking, and metabolic syndrome were risk factors for early-onset lung cancer, while high income, physical activity, and obesity displayed protective effects.