This study examined the inhibitory effects of pluviatolide, a lignan derived from Podophyllum hexandrum, on mast cell activation and IgE-mediated type I hypersensitivity, focusing on FcεRI-dependent and calcium-mediated pathways. Using bone marrowderived mast cells (BMMCs) and rat basophilic leukemia (RBL)-2H3 cells, we found that pluviatolide significantly decreased β-hexosaminidase release and suppressed the expression and secretion of TNF-α and IL-6 in a concentration-dependent manner, without causing cytotoxicity. While we initially hypothesized that it would selectively modulate antigen-specific FcεRI signaling, pluviatolide also inhibited degranulation induced by calcium ionophore and thapsigargin, indicating its effects extend to receptorindependent, Ca2+-dependent activation mechanisms. Immunoblot analyses revealed decreased phosphorylation of proximal kinases (Lyn, Syk), adaptor proteins (LAT, PLCγ1), MAPKs (ERK1/2, JNK, p38), and NF-κB p65. In a passive cutaneous anaphylaxis (PCA) mouse model, oral administration of pluviatolide significantly reduced Evans blue extravasation and mast cell degranulation in ear tissues. These findings demonstrate that pluviatolide suppresses both early and late-phase mast cell responses through multi-nodal inhibition of activation pathways, highlighting its potential as a therapeutic candidate for both IgE-mediated and non-IgE-mediated allergic disorders.

Janus kinase (JAK) inhibitors are an important treatment option for ulcerative colitis, providing rapid onset of action, oral administration, and efficacy even after biologic failure. The 3 approved agents—tofacitinib, filgotinib, and upadacitinib—differ in JAK isoform selectivity, leading to clinically meaningful differences in efficacy and safety. Evidence from network meta-analyses, clinical trials, and real-world studies consistently shows that upadacitinib provides the highest efficacy for induction and maintenance of remission, whereas filgotinib demonstrates the most favorable safety profile. The strong efficacy of upadacitinib and tofacitinib is particularly relevant in patients with severe disease, including acute severe ulcerative colitis, and upadacitinib maintains high efficacy regardless of prior advanced therapy exposure. JAK inhibitors also benefit extraintestinal manifestations. Although risks such as herpes zoster, serious infection, thromboembolism, and major cardiovascular events differ among agents, long-term data suggest generally acceptable safety when used appropriately. Intraclass JAK-to-JAK cycling is feasible, with about half of patients achieving steroid-free clinical remission in retrospective cohorts. Based on mechanistic, clinical, and real-world evidence, filgotinib may be a first-line option for patients with lower disease activity or when safety is a priority, whereas upadacitinib or tofacitinib may be preferred in higher disease activity. Strategically selecting agents may improve durability and outcomes.

Objective: This study evaluated the effectiveness of a humanities-based cognitive behavioral therapy (CBT) program for adolescents aged 11–15 years exhibiting symptoms of problematic gaming, focusing on its impact on depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), and Internet gaming disorder (IGD). Methods: Elementary and middle school students with IGD symptoms were recruited and divided into a humanities-based CBT group (20 students) and a control group receiving supportive therapy (21 students). Participants’ IGD symptoms and levels of depression, anxiety, and ADHD before and after the intervention were compared and evaluated. Results: Verifying the effectiveness of the developed humanities treatment program showed a significant decrease in clinical scale scores indicating anxiety, ADHD, and IGD. In particular, the differences in IGD and anxiety scores between the intervention and control groups were significant, demonstrating the effectiveness of the humanities-based CBT program. Positive correlations were found between the pre–post scores for depression and IGD and between anxiety, depression, and IGD following the humanities-based intervention. Conclusion In this study, experts in various fields developed a humanities-based CBT program for adolescents with problematic gaming behavior and verified its effectiveness, demonstrating that programs utilizing the humanities and writing can positively affect symptoms of IGD, anxiety, depression, and ADHD in adolescents. These findings indicate the need to verify the effectiveness of humanities-based therapy programs for adolescents in more diverse regions and age groups.

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Purpose: This study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). Materials and Methods: Traditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis. Results: The study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812– 0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758–0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705–0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726–0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, MLCAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively. Conclusion ML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2)

Background and Objectives: Ultimaster™, a third-generation sirolimus-eluting stent using biodegradable polymer, has been introduced to overcome long term adverse vascular events, such as restenosis or stent thrombosis. In the present study, we aimed to evaluate the 12-month clinical outcomes of Ultimaster™ stents in Korean patients with coronary artery disease. Methods: This study is a multicenter, prospective, observational registry across 12 hospitals. To reflect real-world clinical evidence, non-selective subtypes of patients and lesions were included in this study. The study end point was target lesion failure (TLF) (the composite of cardiac death, target vessel myocardial infarction [MI], and target lesion revascularization [TLR]) at 12-month clinical follow up. Results: A total of 576 patients were enrolled between November 2016 and May 2021. Most of the patients were male (76.5%), with a mean age of 66.0±11.2 years. Among the included patients, 40.1% had diabetes mellitus (DM) and 67.9% had acute coronary syndrome (ACS).At 12 months, the incidence of TLF was 4.1%. The incidence of cardiac death was 1.5%, MI was 1.0%, TLR was 2.7%, and stent thrombosis was 0.6%. In subgroup analysis based on the presence of ACS, DM, hypertension, dyslipidemia, or bifurcation, there were no major differences in the incidence of the primary endpoint. Conclusions The present registry shows that Ultimaster™ stent is safe and effective for routine real-world clinical practice in non-selective Korean patients, having a low rate of adverse events at least up to 12 months.

Free-living amoebae (FLA) are found in diverse environments, such as soils, rivers, and seas. Hence, they can be used as bioindicators to assess the water quality based solely on their presence. In this study, we determined the presence of FLA in river water by filtering water samples collected from various sites and culturing the resulting filtrates. FLA were detected in all the water samples with varying quality grades (Grades Ι-V). The significant increase in the size of the amoebae population with the deterioration in the water quality. Monoxenic cultures of the amoebae were performed, and genomic DNAs were isolated, among which 18S rDNAs were sequenced to identify the amoeba species. Of the 12 species identified, 10 belonged to the Acanthamoeba genus; of the remaining 2 species, one was identified as Vannella croatica and the other as a species of Vermamoeba. Acanthamoeba was detected in samples with Grades Ι to VI quality, whereas the Vermamoeba species was present only in Grade Ι water. V. croatica was found exclusively in water with Grade ΙΙ quality. Following morphological observations, genomic DNA was sequenced using 16S rDNA to determine whether the species of Acanthamoeba harbored endosymbionts. Most of the isolated Acanthamoeba contained endosymbionts, among which 4 species of endogenous bacteria were identified and examined using transmission electron microscopy. This study provides evidence that the distribution of amoebae other than Acanthamoeba may be associated with water quality. However, further confirmation will be required based on accurate water quality ratings and assessments using a more diverse range of FLA.