Narcolepsy significantly affects the daily life of individuals due to excessive daytime sleepiness and cataplexy, reducing the quality of life and increasing the risk of accidents and social difficulties. Despite its profound impact on daily life, only a few studies have investigated the specific difficulties and awareness of patients with narcolepsy. Therefore, in this study, we aimed to assess the daily challenges and disease awareness of patients with narcolepsy. Methods: We conducted an online survey of 299 patients with narcolepsy. The survey included questions on patient demographics, clinical characteristics, difficulties, and disease awareness. Results: The patients reported considerable difficulties in academic performance, memory, and driving, with those with narcolepsy type 1 reporting greater difficulties than those with narcolepsy type 2. Older age and depression were positively associated with higher difficulty scores, whereas female sex was associated with lower difficulty scores. Notably, disease awareness varied, with 30.4% and 5.4% of participants showing moderate and no awareness of narcolepsy, respectively. Conclusions: Overall, this study highlights the daily life challenges and varying disease awareness of patients with narcolepsy. Our findings emphasize the need for targeted education and effective policy interventions to reduce the difficulties and improve the quality of life of patients with narcolepsy.

Background: Since the World Health Organization’s pandemic declaration in March 2020, Korea has witnessed shifts in lifestyle behaviors, impacting habits tied to socioeconomic status and contributing to metabolic syndrome (MetS). To investigate this issue, the current study aimed to investigate changes in MetS prevalence, particularly based on income levels before and after the coronavirus disease 2019 (COVID-19) pandemic. Methods: This study used data from the 8th Korea National Health and Nutrition Examination Survey (2019–2020). A total of 6,840 individuals aged 30–65 years were included in this study. Household income was divided into high (≥75th percentile), middle (25–75th percentile), and low (≤25th percentile). A multivariable logistic regression analysis was performed to explore the interaction between this association before and after the COVID-19 pandemic. Results: A statistically significant difference was found in the prevalence of MetS before and after the COVID-19 pandemic (26.7% to 30.2%, P=0.001). These changes differed based on income levels. The increase in the prevalence of MetS was statistically significant in the low- and high-income groups but not in the middle-income group (low: 8.0%p increase [P=0.039], middle: 1.0%p increase [P=0.522], high: 6.4%p increase [P<0.001]). The interaction between household income and the COVID-19 pandemic on MetS was statistically significant (P for interaction=0.032). Conclusion This study revealed that P for interaction between household income, MetS, and the period before and after the COVID-19 pandemic was significant. Changes in physical activity and eating habits during the COVID-19 pandemic may have contributed to these differences.

Objectives: Assessing medical disputes requires both medical and legal expertise, presenting challenges for patients seeking clarity regarding potential malpractice claims. This study aimed to develop and evaluate a chatbot based on a chain-of-thought pipeline using a large language model (LLM) for providing medical dispute counseling and compare its performance with responses from human experts. Methods: Retrospective counseling cases (n = 279) were collected from the Korea Medical Dispute Mediation and Arbitration Agency’s website, from which 50 cases were randomly selected as a validation dataset. The Claude 3.5 Sonnet model processed each counseling request through a five-step chain-of-thought pipeline. Thirty-eight experts evaluated the chatbot’s responses against the original human expert responses, rating them across four dimensions on a 5-point Likert scale. Statistical analyses were conducted using Wilcoxon signed-rank tests. Results: The chatbot significantly outperformed human experts in quality of information (p < 0.001), understanding and reasoning (p < 0.001), and overall satisfaction (p < 0.001). It also demonstrated a stronger tendency to produce opinion-driven content (p < 0.001). Despite generally high scores, evaluators noted specific instances where the chatbot encountered difficulties. Conclusions A chain-of-thought–based LLM chatbot shows promise for enhancing the quality of medical dispute counseling, outperforming human experts across key evaluation metrics. Future research should address inaccuracies resulting from legal and contextual variability, investigate patient acceptance, and further refine the chatbot’s performance in domain-specific applications.

Background: The World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments. Materials and Methods: We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19. Results: During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00–1.09; P=0.04) showed a significant association with long COVID after 12–18 months in a multivariable logistic regression analysis. Conclusion Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

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.

Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

Background: Since the World Health Organization’s pandemic declaration in March 2020, Korea has witnessed shifts in lifestyle behaviors, impacting habits tied to socioeconomic status and contributing to metabolic syndrome (MetS). To investigate this issue, the current study aimed to investigate changes in MetS prevalence, particularly based on income levels before and after the coronavirus disease 2019 (COVID-19) pandemic. Methods: This study used data from the 8th Korea National Health and Nutrition Examination Survey (2019–2020). A total of 6,840 individuals aged 30–65 years were included in this study. Household income was divided into high (≥75th percentile), middle (25–75th percentile), and low (≤25th percentile). A multivariable logistic regression analysis was performed to explore the interaction between this association before and after the COVID-19 pandemic. Results: A statistically significant difference was found in the prevalence of MetS before and after the COVID-19 pandemic (26.7% to 30.2%, P=0.001). These changes differed based on income levels. The increase in the prevalence of MetS was statistically significant in the low- and high-income groups but not in the middle-income group (low: 8.0%p increase [P=0.039], middle: 1.0%p increase [P=0.522], high: 6.4%p increase [P<0.001]). The interaction between household income and the COVID-19 pandemic on MetS was statistically significant (P for interaction=0.032). Conclusion This study revealed that P for interaction between household income, MetS, and the period before and after the COVID-19 pandemic was significant. Changes in physical activity and eating habits during the COVID-19 pandemic may have contributed to these differences.