Search Results (190,940)

Cardiac surgery represents a cornerstone of modern cardiovascular medicine, yet it is intrinsically linked to significant systemic stress responses that can compromise remote organ function. Among postoperative complications, cardiac surgery-associated acute kidney injury (CSA-AKI) remains a significant clinical challenge characterized by high morbidity and complex pathophysiology. While hemodynamic instability and ischemia–reperfusion injury are established risk factors, renal dysfunction frequently persists despite optimal perfusion. This observation suggests the involvement of potent circulating mediators in cellular injury. Extracellular vesicles (EVs) are essential for intercellular communication and serve as central hubs for transporting bioactive lipids, proteins, and genetic material. Accumulating evidence indicates that the mechanical and oxidative stress inherent to cardiopulmonary bypass triggers substantial release of EVs from platelets, erythrocytes, and injured vascular tissues. These vesicles may function as vectors that traffic oxidized mitochondrial components and pro-inflammatory cargo to the renal parenchyma. This signaling cascade appears to disrupt renal homeostasis through a proposed “dual-hit” mechanism involving the induction of endothelial dysfunction and endothelial-to-mesenchymal transition (EndMT), followed by tubular epithelial injury via mitochondrial fragmentation, redox imbalance, and downregulation of anti-aging factors. The complexity of these EV-mediated interactions may contribute to an incomplete understanding of why specific patient phenotypes fail to recover. This narrative review examines the mechanisms of surgery-induced EV biogenesis, the molecular pathogenesis of endothelial and tubular damage, and the role of intercellular crosstalk. Additionally, we discuss future perspectives on targeting the “EV vector” through therapeutic apheresis and mitochondrial pharmacotherapy to potentially improve clinical outcomes in high-risk surgical patients. Full article

We introduce the Normalising Flow GARCH (NF-GARCH), a two-stage hybrid framework that enhances traditional GARCH models by replacing restrictive parametric innovation distributions with learned densities via normalising flows. Our approach preserves the interpretability of standard variance dynamics while addressing the common issue of innovation misspecification. In the first stage, we estimate standard GARCH variants (sGARCH, TGARCH, and gjrGARCH) to extract standardised residuals. In the second stage, a Masked Autoregressive Flow learns the underlying residual distribution, with samples from the flow subsequently driving the GARCH recursion for out-of-sample forecasting. Evaluated on 13 daily financial series (six FX pairs and seven equities), NF-GARCH demonstrates systematic, statistically significant improvements in forecast accuracy for skewed-t baselines. Wilcoxon signed-rank tests confirm superior performance specifically for gjrGARCH-sstd and sGARCH-sstd specifications. While the framework offers enhanced flexibility and generative realism, we observe that computational overhead is increased, and the log-variance specification of eGARCH exhibits instability when paired with flow-based innovations. These results suggest that while NF-GARCH effectively captures empirical tail behaviour in univariate settings, future research should explore conditional flow architectures and multivariate extensions to account for time-varying innovation shapes. For risk management, gains are most relevant where skewed-t baselines are used and where closer residual realism supports scenario analysis; effect sizes remain modest relative to model risk and implementation cost. Full article

Background: Patterns of failure (POFs) after first-line immune checkpoint inhibitor (ICI)-based therapy in patients with synchronous metastatic non-small cell lung cancer (NSCLC) without oncogenic driver alterations may guide the selection of candidates for local consolidative therapy (LCT). Methods: We retrospectively evaluated patients diagnosed with synchronous metastatic NSCLC between January 2017 and December 2023. Patients with oncogenic driver alterations, those who did not receive ICIs as first-line therapy, or those who lacked follow-up imaging were excluded. Patients were stratified into four groups according to the number of metastatic lesions: 1, 2, 3–5, and >5 lesions. POFs were classified as original site recurrence (OSR) or new site recurrence with or without OSR (NSR). Competing risk analyses were performed. Results: A total of 221 patients were analyzed, with a median follow-up of 28.1 months. Initial failure patterns did not differ significantly across lesion-number groups (p = 0.417). The 2-year cumulative incidence of OSR was not significantly different between the groups (p = 0.828). A trend toward a lower NSR was observed in patients with a single metastatic lesion (p = 0.063). Analysis of subsequent failures revealed a higher rate of NSR in the 1-lesion group than in the other groups (p = 0.043). No independent predictors of OSR were identified in multivariate analysis. Conclusions: In synchronous metastatic driver-negative NSCLC treated with first-line ICI-based therapy, both OSR and NSR were common and not clearly associated with metastatic burden, suggesting that lesion number alone may be insufficient for selecting candidates for LCT. Full article

Background/Objectives: Fenestrated and branched endovascular aortic repair (F/BEVAR) is increasingly used for the treatment of complex aortic aneurysms, and is traditionally performed under general anesthesia (GA). Data on the use of local anesthesia (LA) for F/BEVAR remain limited. This study aimed to report early outcomes of F/BEVAR performed under LA versus GA, with a focus on feasibility and perioperative complications in a high-risk patient population. Methods: This single-center retrospective analysis included patients undergoing F/BEVAR under LA or GA. Primary outcomes were in-hospital mortality and in-hospital complications. Secondary outcomes included early reintervention, intensive care unit and hospital length of stay, blood transfusion requirements, and technical success. Results: A total of 359 patients were included, of whom 25 (7.0%) were treated under LA and 334 (93.0%) under GA. Conversion from LA to GA occurred in 6 patients (24%). Patients in the LA group represented a higher-risk cohort, with advanced age, higher ASA class, larger aneurysm diameters, and a greater proportion of emergency and ruptured repairs. Technical success was high, and procedural metrics were within expected ranges. In-hospital mortality was numerically higher in the LA group (12.0% vs. 2.9%, p = 0.05). Overall, in-hospital complications were more frequent in the LA group (68.0% vs. 41.3%, p = 0.009), including a higher rate of spinal cord ischemia (24.0% vs. 8.5%, p = 0.02). Blood transfusion requirements were also greater in patients treated under LA (p = 0.004), while blood loss, ICU stay, and hospital length of stay were comparable. Early reintervention occurred more frequently in the LA group (31.8% vs. 10.4%, p = 0.009). Conclusions: LA appears feasible in selected high-risk patients undergoing complex F/BEVAR. However, given substantial baseline differences between groups, no conclusions can be drawn regarding comparative safety or efficacy relative to GA. These findings should be considered preliminary. Full article

Drawing an analogy to the 5th century heresy of Nestorianism, this paper argues that the separation of life from faith in the modern era constitutes a kind of functional Nestorianism, that negates the universality of the Christian claim and renders such a claim. The paper argues that, in their form and function, many Catholic educational institutions unwittingly adopt this functional Nestorianism by means of a structural compartmentalisation of the faith into confined spaces within the curriculum and within the institutional imagination and that in doing so, such institutions risk mirroring—at the level of educational practice—and thus perpetuating, what Pope Paul VI referred to as ‘the drama of our time’, that being, ‘the split between the Gospel and culture’. The paper concludes by offering six Christocentric principles to guide curriculum development and implementation in Catholic educational institutions. Full article

Background: Acute myeloid leukemia with extramedullary disease (EMD-AML) represents a distinct clinical entity associated with diagnostic and therapeutic challenges, and its prognostic significance remains uncertain. Methods: A retrospective study of 617 adults with newly diagnosed AML (2005–2018) was conducted, analyzing 246 patients with EMD-AML and 371 without EMD involvement. The clinical characteristics and treatment outcomes were analyzed. Propensity score matching (PSM) was applied to adjust for baseline confounders. Results: Patients with isolated EMD-AML and those with concurrent bone marrow involvement had comparable clinical outcomes. NPM1 mutations (48% vs. 25%, p = 0.0002) and t(8;21) translocation (23.2% vs. 3.7%, p < 0.001) were enriched in the EMD-AML cohort. After PSM, EMD-AML patients achieved a higher overall response rate compared with non-EMD-AML (88.1% vs. 72.0%, p = 0.0002) but experienced significantly higher relapse rates (35.7% vs. 15.5%, p < 0.0001). Despite the achievement of a higher response rate, EMD-AML was associated with shorter median overall survival (OS) (14.2 vs. 64.1 months, p < 0.0001) and event-free survival (EFS) (9.5 vs. 55.9 months, p < 0.0001). In a multivariable analysis, EMD-AML remained independently associated with worse OS and EFS (OS HR 1.79, p = 0.01; EFS HR 1.95, p = 0.001). Allogeneic hematopoietic stem cell transplantation did not confer a survival advantage in EMD-AML patients. Conclusions: EMD-AML, whether isolated or concurrent with bone marrow disease, represents a high-risk entity characterized by poor long-term outcomes despite strong initial response rates. Obtaining tissue biopsies for molecular profiling may help improve risk stratification, identify targetable mutations and guide individualized treatment. Full article

Using a sample of Chinese junior high school students (N = 1133), this study examined the latent heterogeneity and structural characteristics of mobile phone addiction risk. Latent profile analysis was conducted to identify subgroups with varying levels of risk. Network analysis was then used to model the relationships among smartphone addiction, materialism, and childhood neglect and compare structural differences across subgroups. Finally, five machine learning models were applied to model smartphone addiction scores and compare model performance across different combinations of variables. Results revealed three distinct risk groups of smartphone addiction. Network analysis indicated that loneliness-related nodes exhibited the highest expected influence in the overall network. Across latent profiles, childhood neglect-related nodes consistently occupied central positions, whereas materialism-related nodes showed relatively stable centrality. Network comparison tests further demonstrated significant structural differences across risk groups. In addition, incorporating latent profile information and centrality indices improved model performance, suggesting that these features capture individual differences in smartphone addiction. These findings provide structural evidence for the heterogeneity of mobile phone addiction risk and offer implications for subgroup-specific intervention strategies. Full article

Assessing genotoxicity, specifically gene mutations and chromosomal aberrations, is fundamental to chemical risk assessment. Notably, the early identification of an aneugenic mechanism is of crucial importance, allowing, in principle, for a threshold-based risk assessment approach. To investigate this issue while pushing towards innovation in risk assessment by leveraging New Approach Methodologies, in silico approaches stand out as a particularly promising avenue. Building on these premises and given the lack of QSAR models for aneuploidy in the public domain, the present study exploited the genotoxicity-relevant alert lists implemented in the OECD QSAR Toolbox to base the investigation of structure-activity relationships for aneuploidy. To address the lack of relevant structured data resources, a dataset of 65 confirmed aneugenic substances was specifically curated and designed for the study. The results highlighted widely differing performances among the various profilers, confirming a general limited discriminatory power for aneuploidy. On the other hand, a granular analysis of the results from individual structural alerts enabled the successful isolation of some features associated with the aneugenic mode of action. Moreover, a subset of tubulin-binding chemicals was investigated to determine whether targeting a specific protein improves the characterization of toxicological alerts. The findings provide a refined definition of specific toxicity determinants for tubulin binders and serve as a promising tool for early hazard assessment, potentially informing relevant AOPs. While the computational approach appears promising, the overarching challenge that emerges is the limited availability of well-curated experimental data. In fact, reliable data on aneuploidy are scarce and fragmented across the literature. Furthermore, existing compilations of micronucleus study results are often complicated by conflicting interpretations. Full article

Human lifespan is increasing in parallel with the development levels of societies. Consequently, the number of elderly individuals worldwide is also rising day by day. One of the most significant risks these individuals face is falling. In this study, fall and daily activity data were collected from different home environments using a continuous-wave (CW) radar. Micro-Doppler signatures were generated from 700 data samples obtained from 10 individuals. Furthermore, the dataset was expanded by doubling the number of spectrogram images through data augmentation. The YOLO architecture, generally used in vision-based studies for object detection and tracking, was preferred for radar-based fall and activity detection. Classifications were performed with different YOLO structures, and comparative results are presented. At this stage, binary (fall/non-fall) and multi-class (seven different classes) classifications were carried out, achieving 100% accuracy for binary classification and 88.02% for multi-class classification. Additionally, the generalizability of the proposed architecture is demonstrated using the Leave-One-Subject-Out (LOSO) approach on the collected data and through the analysis of a public dataset. These results demonstrate the applicability of YOLO architectures in radar-based fall detection studies. Full article

Atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) are intensifying under climate change, yet their coupled behavior in the Red Sea remains insufficiently quantified. This study investigates the spatial and temporal characteristics of AHWs, MHWs, and their concurrent occurrence across the Red Sea from 1990 to 2024 using ERA5 surface air temperature (SAT) and NOAA OISST v2.1 satellite-derived sea surface temperature (SST). Remote-sensing daily satellite-derived Level-4 (L4) OISST products were used in this study to enable spatially complete and temporally consistent detection of MHWs in this narrow, semi-enclosed basin despite contamination and coastal sampling constraints. Both SAT and SST exhibit statistically significant warming trends (p < 0.05), with basin mean increases of 0.40 ± 0.07 °C/decade and 0.31 ± 0.05 °C/decade, respectively. The strongest warming was observed in the central and northern Red Sea. This warming is accompanied by significant increases in the frequency and duration of AHWs, MHWs, and their concurrent AHW-MHW events, particularly after 2010, indicating a shift toward more frequent heatwave conditions. AHWs occur more frequently than MHWs across the Red Sea, whereas MHWs exhibit long duration, particularly in the northern Red Sea, where annual durations exceed 45–50 days/year. Concurrent AHW-MHW events account for about 66% of MHWs in the Red Sea, and their characteristics show a significant increasing trend across the entire basin. These findings identify the Red Sea as a regional hotspot of increasing concurrent heatwave events and highlight the importance of satellite-based monitoring for assessing evolving climate risks in semi-enclosed basins. Full article

Deep learning (DL) has transformed cancer imaging by enabling automated tumour detection, classification, and risk prediction. Despite impressive diagnostic performance, limited explainability and poor uncertainty calibration continue to restrict clinical integration. This review is guided by five research questions that examine the challenges, impact, and translational implications of explainable artificial intelligence (XAI) in oncology imaging. We identify key barriers to trust, including dataset bias, shortcut learning, opacity of convolutional neural networks, and workflow misalignment. Evidence suggests that explainable models can increase clinician confidence, reduce false positives, and improve collaborative decision-making when explanations are faithful, semantically meaningful, and uncertainty aware. We evaluate architectural strategies that embed interpretability such as concept-bottleneck models, prototype-based learning, and attention regularization along with post hoc techniques. Beyond performance metrics, we examine how interpretable AI aligns with clinical reasoning processes and analyse regulatory, ethical, and medico-legal considerations influencing deployment. The findings indicate that explainability alone is insufficient, durable trust requires epistemic alignment, prospective validation, lifecycle governance, and equity-focused evaluation. By reframing explainability as a structural design principle rather than a supplementary feature, this review outlines a pathway toward accountable and clinically dependable AI systems in oncology. Full article

The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article

Background: Malnutrition and systemic inflammation are increasingly recognized as important determinants of prognosis in patients with heart failure. Several immunonutritional indices, including the Prognostic Nutritional Index (PNI), the Controlling Nutritional Status (CONUT) score, and the C-reactive protein–albumin–lymphocyte (CALLy) index, have been proposed as markers of nutritional and inflammatory status. However, their prognostic value in elderly patients with heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. This study aimed to evaluate the prognostic significance of these immunonutritional indices in elderly patients with HFpEF over a long-term follow-up period. Methods: This retrospective observational study included 200 elderly patients hospitalized with HFpEF (mean age 86.6 ± 6.5 years). Clinical, laboratory, and echocardiographic parameters were collected at admission. Nutritional status was assessed using PNI, CONUT score, and CALLy index. Patients were followed for mortality during long-term follow-up. Survival analyses were performed using Cox regression models, receiver operating characteristic (ROC) curves, and Kaplan–Meier analysis. Median follow-up was 3.8 years (IQR 2.1–5.9). Results: During follow-up, 123 patients (61.5%) died, while 77 patients (38.5%) were alive at the end of observation. In univariate analysis, PNI, CONUT score, left ventricular ejection fraction (LVEF), and the tricuspid annular plane systolic excursion to systolic pulmonary artery pressure (TAPSE/sPAP) ratio were significantly associated with mortality. In multivariate analysis, the CONUT score, LVEF, and the TAPSE/sPAP ratio remained independent predictors of mortality. ROC analysis showed strong prognostic performance for the TAPSE/sPAP ratio (AUC 0.932), CONUT score (AUC 0.925), and LVEF (AUC 0.897). Optimal cut-off values for mortality prediction were CONUT ≥ 6, LVEF ≥ 65%, and TAPSE/sPAP ≤ 0.55 mm/mmHg. Kaplan–Meier analysis confirmed significantly reduced survival among patients with higher CONUT scores, higher LVEF, and an impaired TAPSE/sPAP ratio. Conclusions: In elderly patients with HFpEF, nutritional status and cardiopulmonary functional parameters are important determinants of long-term prognosis. The CONUT score emerged as the most informative immunonutritional index, while echocardiographic parameters reflecting ventricular function and right ventricular–pulmonary arterial coupling provided additional prognostic information. Integrating nutritional assessment with echocardiographic evaluation may improve risk stratification in elderly patients with HFpEF. Full article

An on-site investigation was conducted to analyze the causes of excessive trihalomethane (THM) formation in Plant A and to mitigate the health risks associated with THM exposure in drinking water. Adjacent Plant B was used as a reference plant. Both water treatment plants used the same source water but employed different pre-oxidants. Systematic stage-specific sampling and analysis of historical monitoring data were conducted to identify the key contributing stage for THM formation. The investigation revealed that 85% of the trichloromethane in Plant A’s finished water originated from the sodium hypochlorite pre-oxidation step, identifying this stage as the key contributing stage. THM concentrations were generally higher at Plant A than at Plant B. A multi-pathway health risk assessment of THM in drinking water indicates that non-carcinogenic risks are negligible, but lifetime carcinogenic risks warrant attention. The findings demonstrate that pre-oxidant selection is a significant governing factor of finished water disinfection by-product (DBP) levels. Following these findings, Plant A implemented measures, including enhanced chlorination management, reduced pre-chlorination, and upgraded sedimentation and V-shaped filters, which substantially reduced chlorinated DBPs in the finished water. Full article