Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.

Background/Aims: Krebs von den Lungen-6 (KL-6) is associated with prognosis in patients with COVID-19. However, there is limited data on the correlation between the prognosis of COVID-19 and varying KL-6 levels at different time points. We investigated the optimal cutoff values of the initial and peak serum KL-6 levels to predict mortality and evaluated their correlation with mortality. Methods: This retrospective cohort study collected data on serially collected serum KL-6 levels in patients hospitalized with COVID-19 between October 2020 and January 2022 at a single tertiary hospital in South Korea. The area under the receiver operating characteristic curve and Youden index were used to determine the cutoff points for the initial and peak KL-6 levels that best predicted 30-day mortality. The association between the initial and peak KL-6 values was assessed by univariate and multivariate logistic regression models. Results: A total of 349 patients were included in this study. The mean initial and peak KL-6 levels were significantly higher in the non-survivor group than in the survivor group. The initial and peak KL-6 values that best predicted 30-day mortality were 491.85 U/mL and 660.05 U/mL, respectively. An initial KL-6 level greater than 491.85 U/mL and a peak KL-6 level greater than 660.05 U/mL were significantly associated with 30-day mortality. Conclusions The initial and peak levels of KL-6 were significantly associated with 30-day mortality in hospitalized patients with COVID-19. These findings suggest that serially monitoring blood KL-6 levels could be a valuable prognostic indicator for COVID-19.

Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

BACKGROUND: Bone defects are commonly encountered due to accidents, diseases, or aging, and the demand for effective bone regeneration, particularly for dental implants, is increasing in our aging society. Mesenchymal stem cells (MSCs) are promising candidates for regenerative therapies; however, obtaining sufficient quantities of these cells for clinical applications remains challenging. DW-MSCs, derived from embryonic stem cells and developed by Daewoong Pharmaceutical, exhibit a robust proliferative capacity even after extensive culture. METHODS: This study explores the therapeutic potential of DW-MSCs in various animal models of bone defects. DWMSCs were expanded for over 13 passages for in vivo use in rat and canine models of bone defects and osteomyelitis. The research focused on the in vivo osteogenic differentiation of DW-MSCs, the establishment of a fibrin-based system for bone regeneration, the assessment of bone repair following treatment in animal models, and comparisons with commercially available bone grafts. RESULTS: Results showed that DW-MSCs exhibited superior osteogenic differentiation compared to other materials, and the fibrinization process not only preserved but enhanced their proliferation and differentiation capabilities through a 3D culture effect. In both bone defect models, DW-MSCs facilitated significant bone regeneration, reduced inflammatory responses in osteomyelitis, and achieved effective bone healing. The therapeutic outcomes of DW-MSCs were comparable to those of commercial bone grafts but demonstrated qualitatively superior bone tissue restructuring. CONCLUSION Our findings suggest that DW-MSCs offer a promising approach for bone regeneration therapies due to their high efficacy and anti-inflammatory properties.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Obesity and metabolic syndrome are acknowledged as key factors contributing to the development of non-alcoholic fatty liver disease (NAFLD). Vitamin D (VitD) is a multifaceted secosteroid hormone known for its anti-fibrotic and anti-inflammatory properties, with its deficiency often linked to obesity. Our study aimed to investigate whether VitD supplementation could mitigate the liver pathology associated with NAFLD. Materials and Methods: The NAFLD model was developed by subjecting male C57BL/6 mice to a high-fat diet (HFD) for 14 weeks.These mice were supplemented with VitD through intraperitoneal injection at a dosage of 7 μg/kg, administered three times per week for 7 weeks. Results: HFD resulted in VitD deficiency, insulin resistance, and increased liver weight. It elevated serum levels of liver aminotransferases and triglyceride, ultimately leading to steatohepatitis with fibrosis. This model exhibited increased levels of transforming growth factor (TGF)-β1, pro-inflammatory cytokines, HNF4α transcription factors, reactive oxygen species (ROS), renin-angiotensin system activity, and epithelial-mesenchymal transitions (EMT) within the liver. Supplementation with VitD resulted in the recovery of liver weight, improvement in histologic features associated with steatohepatitis, and reduction in alanine aminotransferases and triglyceride levels induced by the HFD. Additionally, it mitigated the HFD-induced over-expressions of TGF-β1 and fibrosis-related genes, along with pro-inflammatory cytokines and ROS. Notably, no adverse effect was found due to VitD supplementation in this model. Conclusion VitD ameliorates steatohepatitis within obesity-induced NAFLD through its multifaceted pathways. VitD supplementation emerges as a potentially safe, cost-effective, and direct treatment approach for NAFLD patients dealing with obesity or metabolic dysfunction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Aortic regurgitation (AR) in children is usually caused by congenital valve anomalies, and Behçet syndrome (BS) can be suspected in cases of isolated AR. Patients with BS undergoing aortic valve surgery due to aortic valve invasion have a high risk of complications, such as leakage around the valve and dehiscence. Cardiovascular involvement occurs in 7%–46% of adult patients with BS and is the main cause of mortality; however, its prevalence is unclear and rare in children. A 12-year-old boy was diagnosed with severe AR associated with BS. A progressive subaortic pseudoaneurysm was observed after aortic valve replacement. The periaortic intracardiac pouch was at risk of rupture; therefore, the patient underwent a Bentall operation. After the Bentall procedure, a newly developed subaortic pseudoaneurysm was detected below the prosthetic valve. However, the risk of rupture reduced as the subaortic pseudoaneurysm regressed with anti-inflammatory drugs alone without reoperation. Repeated surgery is inevitable in patients with BS undergoing aortic valve surgery due to the progressive chronic inflammatory reactions that present with a pseudoaneurysm. Here, we report an 8-year follow-up of a pediatric case of BS with subaortic pseudoaneurysm, highlighting the importance of close follow-up, medical management, and early diagnosis in treating this condition.