Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Background and Objectives: Inconsistencies in nasal nitric oxide (nNO) values, due to anatomical variations and comorbidities, challenge the accurate assessment of upper airway inflammation severity. We hypothesized that changes in nNO levels following treatment for chronic rhinitis would be consistent and provide relative value. This study aimed to evaluate the correlation between changes in nNO levels and symptomatic improvements following treatment for chronic rhinitis. Methods: This prospective observational study included 46 participants diagnosed with chronic rhinitis between December 2021 and November 2023. nNO measurements, evaluations of four nasal and two ocular symptoms, and quality of life questionnaires were conducted at baseline and after one month of treatment. Baseline laboratory tests included serum total immunoglobulin E levels, blood eosinophil percentages, and skin prick tests. Results: The Total Nasal Symptom Score (TNSS), TNSS with ocular symptoms (TNSS eye), and Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) scores significantly decreased following treatment (all p<0.001). nNO levels also decreased significantly after treatment (p=0.036). Moreover, changes in nNO were significantly correlated with changes in TNSS, TNSS eye, and RQLQ scores (p=0.047, r=0.294; p=0.021, r=0.340; and p=0.004, r=0.419, respectively). Conclusion In patients with chronic rhinitis, changes in TNSS, TNSS eye, and RQLQ scores were correlated with changes in nNO levels after treatment. nNO may serve as a potential objective evaluation tool for chronic rhinitis, particularly in patients who have difficulty reporting symptoms.

Autoimmune encephalitis (AIE) is a type of immunoreactive encephalitic disorder and is recognized as the most prevalent noninfectious encephalitis. Nevertheless, the rarity of definitive AIE diagnosis through biopsy or autopsy represents a significant hurdle to understanding and managing the disease. In this article, we present the pathological findings of AIE and review the literature based on a distinct case of AIE presenting as CD8+ T-lymphocyte predominant encephalitis. We describe the clinical progression, diagnostic imaging, laboratory data, and autopsy findings of an 80-year-old deceased male patient. The patient was diagnosed with pulmonary tuberculosis 6 months before death and received appropriate medications. A week before admission to the hospital, the patient manifested symptoms such as a tendency to sleep, decreased appetite, and confusion.Although the patient temporally improved with medication including correction of hyponatremia, the patient progressed rapidly and died in 6 weeks. The brain tissue revealed lymphocytic infiltration in the gray and white matter, leptomeninges, and perivascular infiltration with a predominance of CD8+ T lymphocytes, suggesting a case of AIE. There was no detectable evidence of viral infection or underlying neoplasm. The autopsy revealed that this patient also had Alzheimer’s disease, atherosclerosis, arteriolosclerosis, and aging-related tau astrogliopathy. This report emphasizes the pivotal role of pathological examination in the diagnosis of AIE, especially when serological autoantibody testing is not available or when a patient is suspected of having multiple diseases.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Despite the widespread impact of the severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019, COVID-19) and vaccination in South Korea, our understanding of kidney diseases following these events remains limited. We aimed to address this gap by investigating the characteristics of glomerular diseases following the COVID-19 infection and vaccination in South Korea. Methods: Data from multiple centers were used to identify de novo glomerulonephritis (GN) cases with suspected onset following COVID-19 infection or vaccination. Retrospective surveys were used to determine the COVID-19–related histories of patients who were initially not implicated. Bayesian structural time series and autoregressive integrated moving average models were used to determine causality. Results: Glomerular diseases occurred shortly after the infection or vaccination. The most prevalent postinfection GN was podocytopathy (42.9%), comprising primary focal segmental glomerulosclerosis and minimal change disease, whereas postvaccination GN mainly included immunoglobulin A nephropathy (IgAN; 57.9%) and Henoch-Schönlein purpura nephritis (HSP; 15.8%). No patient progressed to end-stage kidney disease. Among the patients who were initially not implicated, nine patients with IgAN/HSP were recently vaccinated against COVID-19. The proportion of glomerular diseases changed during the pandemic in South Korea, with an increase in acute interstitial nephritis and a decrease in pauci-immune crescentic GN. Conclusion: This study showed the characteristics of GNs following COVID-19 infection or vaccination in South Korea. Understanding these associations is crucial for developing effective patient management and vaccination strategies. Further investigation is required to fully comprehend COVID-19’s impact on GN.

Background: To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Methods: This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). Results: In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. Conclusion Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.