Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Background: Posttransplant diabetes mellitus (PTDM) complicates kidney transplant recipients (KTRs) in morbidity and mortality. This study aimed to predict PTDM risk in KTRs using machine learning and deep learning models. Methods: Data were obtained from the Korea Organ Transplantation Registry, a nationwide cohort study of KTRs. Four machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, light gradient boosting machine and logistic regression, and deep learning were implemented on 41 pretransplant and 31 posttransplant variables to predict PTDM. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, precision, recall, and F1 score. Results: Among 3,213 KTRs, 497 patients (15.5%) developed PTDM within 1 year. The PTDM group had higher age, body mass index (BMI), triglyceride level, and prevalence of hypertension and cardiovascular disease, and lower total cholesterol level at baseline than the No-PTDM group. The XGBoost model showed the highest AUC (0.738) and F1 score (0.42), and modest accuracy (0.86), while the CatBoost model exhibited the highest accuracy (0.87) and precision (0.79). Feature importance in XGBoost was highest for recipient age, followed by baseline BMI, triglyceride level at posttransplant 6 months, baseline glycated hemoglobin and high-density lipoprotein cholesterol level, white blood cell (WBC) count and serum uric acid level at 6 months, baseline WBC count, and tacrolimus trough level at discharge. Conclusion The XGBoost model demonstrated the best performance for predicting PTDM within 1 year, offering an accurate tool for early identification and personalized care of high-risk KTRs for PTDM.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.