The comprehensive in vitro proarrhythmia assay (CiPA) initiative, led by the U.S. Food and Drug Administration (FDA), provides a framework for predicting drug-induced arrhythmia risk. To support domestic CiPA implementation, we evaluated the proarrhythmic risk of sildenafil, a phosphodiesterase type 5 (PDE5) inhibitor. Sildenafil has been misused recreationally, with reports of highdose non-medical intake. To simulate misuse, in vitro assays were conducted using concentrations up to 100× the maximum therapeutic plasma concentration (Cmax). The assessment followed the three core components of the CiPA paradigm. Patch clamp assays were conducted in Human Embryonic Kidney 293 (HEK293) and Chinese hamster ovary (CHO) cells transiently expressing Nav1.5, Cav1.2, and hERG ion channels. In silico modeling was performed using the CiPAORdv1.0 model based on IC₅₀ and Hill coefficient values. Functional evaluation included multi-electrode array (MEA) recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), and in vivo electrocardiography (ECG) analysis in rats was performed to observe QT interval prolongation. Sildenafil significantly inhibited hERG currents by 40.5% at 100× Cmax. In silico modeling predicted a low Torsades de Pointes (TdP) risk based on qNet biomarkers. In contrast, MEA recordings showed a concentration-dependent prolongation of corrected field potential duration (FPDc), with a significant 13.3% increase at 100× Cmax. The TdP risk estimated from MEA modeling was 64%. In vivo ECG analysis revealed significant QT prolongation at 50× Cmax. Despite low in silico TdP predictions, functional assays suggest that high concentrations of sildenafil as in misuse may pose a clinically relevant risk of QT prolongation and arrhythmia.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Purpose: Prostate cancer ranks as the second most common cancer in men globally, representing a significant cause of cancer-related mortality. Metastasis, the spread of cancer cells from the primary site to distant organs, remains a major challenge in managing prostate cancer. Pyruvate dehydrogenase kinase 4 (PDK4) is implicated in the regulation of aerobic glycolysis, emerging as a potential player in various cancers. However, its role in prostate cancer remains unclear. This study aims to analyze PDK4 expression in prostate cancer cells and human samples, and to explore the gene's clinical significance. Materials and Methods: PDK4 expression was detected in cell lines and human tissue samples. Migration ability was analyzed using Matrigel-coated invasion chambers. Human samples were obtained from the Kyungpook National University Chilgok Hospital. Results: PDK4 expression was elevated in prostate cancer cell lines compared to normal prostate cells, with particularly high levels in DU145 and LnCap cell lines. PDK4 knockdown in these cell lines suppressed their invasion ability, indicating a potential role of PDK4 in prostate cancer metastasis. Furthermore, our results revealed alterations in epithelial-mesenchymal transition markers and downstream signaling molecules following PDK4 suppression, suggesting its involvement in the modulation of invasion-related pathways. Furthermore, PDK4 expression was increased in prostate cancer tissues, especially in castration-resistant prostate cancer, compared to normal prostate tissues, with PSA and PDK4 expression showing a significantly positive correlation. Conclusions PDK4 expression in prostate cancer is associated with tumor invasion and castration status. Further validation is needed to demonstrate its effectiveness as a therapeutic target.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Secondary dyslipidemia, characterized by elevated blood cholesterol and triglycerides, arises from various underlying conditions. The identification and appropriate handling of these causes is crucial for effective treatment. Major contributors include unhealthy diets, diseases impacting lipid metabolism, and medication side effects. Prioritizing the correction of secondary causes before initiating conventional lipid-lowering therapies is essential. Subsequent lipid profiles guide the selection of appropriate guideline-based lipid-lowering interventions.

This study aimed to develop and evaluate a multimodal deep learning model that integrates panoramic radiographs and clinical data to improve the diagnosis of medication-related osteonecrosis of the jaw (MRONJ). Materials and Methods:The study included 705 panoramic radiographs (401 MRONJ and 304 Normal) collected from Ewha Womans University Mokdong and Seoul Hospitals. Radiographs were paired with patient information, including age, sex, body mass index (BMI), type of medication, duration, route of administration, dosage, and dental risk factors. Two deep learning models were compared: a ResNet-50 model using panoramic radiographs alone and the Modified ResNet-50 model incorporating both image and clinical features. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Lesion localization and feature importance were analyzed using Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). Result: Significant differences in clinical characteristics were observed between the groups; the MRONJ group was older (74.5±10.0 vs. 68.6±12.7 years, P<0.01) and had a higher proportion of females (92.5% vs. 82.2%, P<0.01). In model performance, the Modified ResNet-50 model outperformed the PR-only model. Specifically, the Modified ResNet-50 model achieved an accuracy of 75.0%, a recall of 84.3%, and an AUC of 0.843, whereas the PR-only model showed lower performance with an accuracy of 72.1%, a recall of 81.4%, and an AUC of 0.720. SHAP analysis identified prolonged medication use and specific drug types (e.g., zoledronate, P<0.01) as influential predictors. Conclusion: The proposed multimodal deep learning framework demonstrated improved diagnostic performance and interpretability compared to radiograph-only models. This approach may serve as a practical and explainable AI-assisted tool for MRONJ diagnosis in routine dental practice.