Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

Background/Aims: Post-infectious irritable bowel syndrome (PI-IBS) is characterized by chronic gastrointestinal symptoms that arise following an episode of infectious enteritis. The incidence rates vary, ranging from 5% to 32% and the risk factors are not well known. We aim to investigate the incidence and risk factors of PI-IBS in enteritis patients admitted to university hospitals in Korea. Methods: This multi-center prospective study was conducted in patients hospitalized for infectious enteritis. Each patient underwent 1 outpatient visit and 3 telephone surveys during the first year after discharge to determine if PI-IBS occurred within the follow-up period. Results: In the 3-month survey, 7 out of 354 patients (2%) were diagnosed with PI-IBS, and after 1 year, only 1 patient met the criteria for IBS.No statistically significant difference was found between the PI-IBS group and the non-PI-IBS group in terms of age, sex, underlying diseases, medication history, gastrointestinal symptoms, enteritis location, causative strain, hospitalization and treatment periods, and laboratory findings. Female sex (P = 0.003), enteropathogenic Escherichia coli (EPEC) infection (P = 0.044), and a longer total treatment period (P = 0.018) were independent risk factors for diarrhea lasting ≥ 3 months after enteritis. Conclusions The incidence of PI-IBS in Korea was relatively low, and most cases improved over time. No risk factors associated with the development of PI-IBS were found. However, persistent diarrhea after enteritis was associated with female sex, EPEC infection, and severe or long-lasting enteritis. IBS symptoms may persist after severe enteritis but usually improve with time.

Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Background/Aims: Small bowel capsule endoscopy (SBCE) has become the standard for initial evaluation in the diagnosis of small bowel lesions. Although optimal visualization of the mucosa is important, patients experience difficulty in consuming a large volume of bowel preparation agents. This study aimed to compare the efficacy and safety of 1-L polyethylene glycol (PEG) with ascorbic acid (AA) and 2-L PEG with AA. Methods: In this prospective, multicenter, non-inferiority study, patients who received SBCE were randomly assigned to consume 1-L PEG with AA or 2-L PEG with AA for small bowel preparation. The primary outcome was adequate small bowel visibility quality (SBVQ). The secondary outcomes included diagnostic yield, cecal complete rate, and adverse events. Results: One hundred and forty patients were enrolled in this study, 70 patients per group. In the per-protocol analysis, there were no significant differences in the adequate SBVQ rate (94.0% vs 94.3%; risk difference, –0.3; 95% confidence interval, –8.1 to 7.6; p=1.000), diagnostic yield rate (49.3% vs 48.6%, p=0.936), or cecal complete rate (88.1% vs 92.9%, p=0.338) between the 1-L PEG with AA group and 2-L PEG with AA group. The incidence of adverse events did not differ significantly between the groups (12.9% vs 11.9%, p=0.871). Conclusions One liter-PEG with AA is not inferior to 2-L PEG with AA in terms of adequate SBVQ for SBCE. One liter-PEG with AA can be recommended as the standard method for bowel cleansing for SBCE.

Human rhinovirus (HRV) causes the common cold and exacerbates chronic respiratory diseases, such as asthma and chronic obstructive pulmonary disease. Despite its significant impact on public health, there are currently no approved vaccines or antiviral treatments for HRV infection. Apoptosis is the process through which cells eliminate themselves through the systematic activation of intrinsic death pathways in response to various stimuli. It plays an important role in viral infections and serves as a key immune defense mechanism in the interactions between viruses and the host. In the present study, we investigated the antiviral effects of quercetin-3-methyl ether, a flavonoid isolated from Serratula coronata, on human rhinovirus 1B (HRV1B). Quercetin-3-methyl ether significantly inhibited HRV1B replication in HeLa cells in a concentration-dependent manner, thereby reducing cytopathic effects and viral RNA levels. Time-course and time-of-addition analyses confirmed that quercetin-3-methyl ether exhibited antiviral activity during the early stages of viral infection, potentially targeting the replication and translation phases. Gene expression analysis using microarrays revealed that pro-apoptotic genes were upregulated in quercetin-3-methyl ether-treated cells, suggesting that quercetin-3-methyl ether enhances early apoptosis to counteract HRV1B-induced immune evasion. In vivo administration of quercetin-3-methyl ether to HRV1B-infected mice significantly reduced viral RNA levels and inflammatory cytokine production in the lung tissues. Our findings demonstrated the potential of quercetin-3-methyl ether as a novel antiviral agent against HRV1B, thereby providing a promising therapeutic strategy for the management of HRV1B infections and related complications.

Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

Background: Lung ultrasound (LUS) has proven valuable in the initial assessment of coronavirus disease 2019 (COVID-19), but its role in detecting pulmonary fibrosis following intensive care remains unclear. This study aims to assess the presence of pulmonary sequelae and fibrosis-like changes using LUS in survivors of severe COVID-19 pneumonia one month after discharge. Methods: We prospectively enrolled patients with severe COVID-19 who required mechanical ventilation in the intensive care unit (ICU) and conducted LUS assessments from admission to the outpatient visit after discharge. We tracked changes in key LUS findings and applied our proprietary LUS scoring system. To evaluate LUS accuracy, we correlated measured LUS values with computed tomography scores. Results: We evaluated B-line presence, pleural thickness, and consolidation in 14 eligible patients. The LUS scores exhibited minimal changes, with values of 19.1, 19.2, and 17.5 at admission, discharge, and the outpatient visit, respectively. Notably, the number of B-lines decreased significantly, from 1.92 at admission to 0.56 at the outpatient visit (p<0.05), while pleural thickness increased significantly, from 2.05 at admission to 2.48 at the outpatient visit (p≤0.05). Conclusion This study demonstrates that LUS can track changes in lung abnormalities in severe COVID-19 patients from ICU admission through to outpatient follow-up. While pleural thickening and B-line patterns showed significant changes, no correlation was found between LUS and high-resolution computed tomography fibrosis scores. These findings suggest that LUS may serve as a supplementary tool for assessing pulmonary recovery in severe COVID-19 cases.

Background: Chromosomal alterations serve as diagnostic and prognostic markers in acute leukemia. Given the evolving landscape of chromosomal abnormalities in acute leukemia, we previously studied these over two periods. In this study, we investigated the frequency of these abnormalities and clinical trends in acute leukemia in Korea across three time periods. Methods: We retrospectively analyzed data from 1,787 patients with acute leukemia (319 children and 1,468 adults) diagnosed between 2006 and 2020. Conventional cytogenetics, FISH, and multiplex quantitative PCR were used for analysis. The patient groups were divided according to the following three study periods: 2006–2009 (I), 2010–2015 (II), and 2016–2020 (III). Results: Chromosomal aberrations were detected in 92% of patients. The PML::RARA translocation was the most frequent. Over the 15-yr period, chromosomal aberrations showed minimal changes, with specific fusion transcripts being common among patients.ALL was more prevalent in children than in adults and correlated significantly with the ETV6::RUNX1 and RUNX1::RUNX1T1 aberrations. The incidence of ALL increased during the three periods, with PML::RARA remaining common. Conclusions The frequency of chromosomal abnormalities in acute leukemia has changed subtly over time. Notably, the age of onset of adult AML has continuously increased. Our results may help in establishing diagnoses and clinical treatment strategies and developing various molecular diagnostic platforms.

Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing. Methods: We developed novel urine test strips and an AI algorithm for image capture.Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters. Results: In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping. Conclusions The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.