Search Results (955)

Background: This narrative review introduces the “From Plaque to Perfusion” framework, a clinically pragmatic approach that maps multimodality imaging technologies to critical decision points in the acute coronary syndrome (ACS) patient journey. By integrating non-invasive assessment, invasive procedural guidance, and post-event tissue characterisation, this framework provides a structured pathway for deep phenotyping of ACS. Artificial intelligence (AI) is highlighted as an essential enabling layer that enhances diagnostic precision, automates quantification, and supports scalable, data-driven care. Contemporary ACS management pathways, while effective, often leave residual clinical uncertainty. The diagnostic objective has evolved beyond confirming myocardial injury to comprehensively phenotyping the entire ACS cascade: defining the plaque substrate, identifying the culprit mechanism, and quantifying the myocardial consequence. This requires a systematic integration of advanced imaging modalities. Methods: This narrative review is based on a comprehensive literature search of major medical databases (PubMed/MEDLINE, Scopus, Embase, Google Scholar) for high-level evidence, including randomized controlled trials, meta-analyses, and international expert consensus documents published between January 2010 and February 2026. Results: The “From Plaque to Perfusion” framework consists of three core stages. First, non-invasive assessment with coronary computed tomography angiography (CCTA), fractional flow reserve (FFR-CT), and PET-CT defines plaque substrate and vascular inflammation. Second, invasive precision in the catheterization laboratory, guided by optical coherence tomography (OCT) and intravascular ultrasound (IVUS), resolves the culprit mechanism and optimizes percutaneous coronary intervention (PCI). Third, post-event tissue characterization with cardiac magnetic resonance (CMR) quantifies myocardial injury and refines prognosis. AI-driven platforms are shown to enhance each stage by automating analysis, standardizing interpretation, and providing actionable metrics for clinical decisions, including complex scenarios like Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA). Conclusions: The “From Plaque to Perfusion” framework, enabled by AI, reframes ACS imaging as an integrated, mechanism-driven pathway. This approach moves beyond isolated test interpretation toward a scalable model of precision, phenotype-led care that promises to improve diagnostic certainty and personalize patient management. Full article

Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quantitative imaging information for risk stratification. This review synthesizes contemporary evidence on dose-optimized CT, advanced spectral technologies, and artificial intelligence (AI)-enabled analytics that are reshaping diagnosis, treatment selection, and triage. This review summarizes data supporting low-dose and ultra-low-dose CT protocols that preserve diagnostic accuracy while substantially reducing dose, and discusses how dual-energy CT, photon-counting CT, and radiomics facilitate noninvasive stone characterization and extraction of imaging biomarkers beyond size and location. It also reviews AI approaches for automated detection, segmentation, and volumetric quantification across CT, KUB, and ultrasounds, highlighting their potential to standardize stone-burden metrics. It further examines predictive models, including logistic regression, nomograms, and machine learning, for perioperative infectious complications, emergency department admission or intervention, procedure success, and long-term recurrence, and outlines reporting and validation frameworks and implementation considerations, including software as a medical device regulation and human oversight. In contrast to prior reviews that consider imaging and AI separately, this review integrates dose reduction, spectral characterization, and AI-driven analytics within real-world clinical pathways to distinguish established clinical applications from those that remain investigational. Integrating advanced CT and AI outputs into well-validated prediction models embedded in real-world workflows may enable safer imaging, more consistent triage, and more personalized follow-up for urinary stone disease. Full article

While global nutrient insufficiency remains a critical health challenge, eggs have emerged as a potential solution due to their profile as an accessible and nutrient-dense food source. To quantitatively assess this potential for mitigating nutrient insufficiencies and guide the production of nutrient-enriched eggs, the study proposes the concept of egg nutriomics, establishing a comprehensive evaluation system with 35 indicators across seven nutritional dimensions (fatty acids, amino acids, vitamins, trace elements, pigments, antioxidant capacity, and dietary restriction factors). Methodologically, the system normalizes raw analytical data into standardized scores (0–100) using indicator-specific functional models, with weights rationally allocated based on the essentiality of the nutrients. These quantitative metrics are subsequently translated into intuitive results using visualization tools such as heatmaps and radar charts. This study applied this system to evaluate six commercial egg varieties (pasteurized, lutein-enriched, ω-3 enriched, animal welfare, low-cholesterol, and conventional cage eggs), profiling multidimensional nutrition that allows for the intuitive visualization of performance scores across distinct dimensions. These profiles extend beyond comprehensive evaluation by revealing specific quantitative advantages—such as ω-3 enriched eggs scoring 79 in the fatty acid dimension compared to 49 for conventional eggs—thus providing a reference to guide precision modulation as illustrated by a dietary ω-3 enrichment case study involving 200 laying hens. Building upon this foundation, the strategy empowers a shift from the sole pursuit of high yields to precision nutritional modulation. This multi-dimensional strategy bridges nutritional analysis with production control, facilitating the development of nutrient-dense eggs as a potential application to mitigate human malnutrition. Full article

Chronic stress is a time-dependent condition characterized by sustained dysregulation across neural, autonomic, and endocrine systems, with important consequences for both health and socioeconomic outcomes. Unlike acute stress, which is typically characterized by short-lived physiological activation, chronic stress reflects an accumulated allostatic load and a longer-term recalibration of stress response systems. Recent advances in physiological sensing and artificial intelligence (AI) have supported the development of computational approaches for chronic stress detection using electroencephalography (EEG), heart rate variability (HRV), photoplethysmography (PPG), electrodermal activity (EDA), and wearable multimodal platforms. This narrative review examines current AI-based studies through three main inferential paradigms: resting baseline dysregulation, longitudinal physiological monitoring, and reactivity-based inference. Across modalities, classical machine learning (ML) methods, particularly support vector machines (SVMs) and tree-based ensembles, remain the most commonly used approaches, largely because available datasets are small and most pipelines still depend on engineered features. Deep learning (DL) methods are beginning to emerge, but their use remains constrained by the lack of large, standardized, longitudinal datasets specifically designed for chronic stress research. Major challenges include ambiguity in stress labeling, limited longitudinal validation, circadian confounding, inter-individual variability, and small cohort sizes. Future progress will depend on standardized datasets, biologically grounded multimodal integration, hybrid baseline-reactivity modeling, adaptive personalization, and more interpretable AI systems. Greater emphasis is also needed on clinical relevance and generalizability if AI-based chronic stress monitoring is to move beyond experimental settings. Full article

Treatment for large critical-sized bone defects and impaired fracture healing remain challenging. Clinically used protein-based osteoinductive factors, such as recombinant bone morphogenetic proteins (BMPs), can be effective; however, they are costly and limited by stability, dose-delivery issues, and safety concerns. Preclinical small molecules offer an alternative because they are chemically stable, scalable to manufacture, and readily integrated for systemic administration or localized release from scaffolds, hydrogels, cements, and implant coatings. With an emphasis on delivery formats and mechanistic themes, this review examines small molecules that have been shown to improve bone regeneration in preclinical models, contrasting those of biological origin with synthetic and repurposed compounds. Across studies, these selected compounds promote osteoblast commitment, differentiation, and matrix mineralization via BMP/Smad signaling and Wnt/beta-catenin (β-catenin) activation, often through glycogen synthase kinase-3 beta (GSK-3β) inhibition or relief of pathway antagonism or Hedgehog (Hh) pathway stimulation. Beyond osteoinduction, several candidates address issues that commonly limit repair, including angiogenesis, oxidative stress, inflammatory tone, osteoimmune regulation, and suppression of osteoclast-mediated resorption. Direct head-to-head comparisons are rare across both classes and reporting heterogeneity complicates interpretation. Key translational gaps include limited cytotoxicity and immunologic profiling, dose and release optimization, durability of benefit, and insufficient evaluation of rational combinations. More rigorous in vivo studies, including larger animal models and standardized outcome metrics, are needed to prioritize promising candidates and guide clinical development. Full article

Background: Vitamin D deficiency remains a widespread public health issue in Europe, despite the availability of sunlight, dietary sources, supplements, and food fortification. National fortification strategies differ substantially in their regulatory approaches, food vehicles, and fortification levels, influencing the population’s vitamin D intake and status. Objective: The primary objective of this study was to map vitamin D food fortification policies across European Union (EU) Member States, European Free Trade Association (EFTA) countries, and the United Kingdom (UK), focusing on regulatory frameworks, eligible food categories, and implementation models. Methods: A structured review of national legislation and official guidance on vitamin D food fortification was conducted between December 2025 and March 2026 across EU Member States (n = 27), EFTA countries (n = 4), and the UK. For EU Member States, the framework established by Regulation (EC) No 1925/2006 was examined alongside national implementation measures. For EFTA countries and the UK, corresponding national legislation and official regulatory guidance were reviewed. Data were extracted on fortification policy status, eligible food categories, legal basis, and fortification levels. Targeted searches of PubMed and Scopus were performed to identify modeling studies and policy analyses supporting the interpretation of the findings. Results: Fortification policies show marked heterogeneity. Mandatory fortification is limited to a few countries and specific foods: Finland (homogenized skim milk), Sweden (low-fat milk, fermented dairy, plant-based alternatives, and fat spreads), Belgium (margarine and selected fats), and Poland (margarine and fat spreads). In most other European countries, vitamin D fortification is voluntary under EU legislation or equivalent national legislation, depending on market uptake. Food vehicles vary regionally, with Northern Europe extending fortification beyond fats to include fluid milk and plant-based drinks, whereas other regions mainly fortify margarines, cereals, dairy products, and plant-based beverages. Fortification levels also differ, with some countries specifying maximal or exact levels, while others lack national standards. Data on fortified foods are limited in several Central and Southern European countries. Modeling indicates that multi-vehicle fortification is more effective than single-vehicle approaches, safely increasing population intakes while reducing deficiency prevalence. Conclusions: Vitamin D fortification policies across Europe are highly heterogeneous. Most countries rely on voluntary approaches, which provide limited coverage. Strengthening policy through mandatory and well-coordinated multi-vehicle strategies, informed by modeling and population-based studies, can improve vitamin D intake, reduce deficiency prevalence, and enhance health equity. Full article

We summarize our research program on the use of coherent states and covariant integral quantization in quantum cosmology. In particular, we present a recent development within this framework and include new results that shed light on some of its basic properties. Specifically, we investigate the quantum dynamics of a perturbed, fluid-filled Friedmann universe beyond the standard approximation in which the total state factorizes into background and perturbation wave functions. We assume the background geometry to be a superposition of two distinct coherent states—effectively a quantum cat state with no classical counterpart—each coupled to inhomogeneous perturbations. Starting from vacuum initial conditions, we analyze the evolution of a contracting universe through a bounce into the expanding phase. We find that an initially factorized state evolves into a biverse. This state consists of two distinct semiclassical branches, each described by a single coherent state and carrying enhanced perturbations in a slightly non-Gaussian state. We then explore how this dynamics depends on key model parameters, such as the perturbation wavelength and the choice of background solutions, and study their impact on the interaction between branches. The observed universe is assumed to correspond to one branch of this biverse state. This scenario illustrates how genuinely quantum properties of the background geometry may leave observable imprints in the early universe. Full article

This study examines whether economic diplomacy—proxied by KOF-based indicators of political globalisation and economic policy openness—is associated with multidimensional sustainable development (SD) across 208 countries over the period 2000–2023. Using two-way fixed-effects panel models with Driscoll–Kraay standard errors, complemented by instrumental-variable and dynamic panel checks, we find a positive but modest within-country association between diplomatic embeddedness and Sustainable Development Goal (SDG) performance. The association is driven primarily by political globalisation—reflecting diplomatic networks, international organisation membership, and treaty engagement—rather than trade policy openness. De facto integration exhibits stronger links to SDG outcomes than de jure policy indicators. The relationship is concave, with diminishing marginal returns beyond a diplomacy proxy value of approximately 60. A latent-class framework identifies two institutional archetypes: the association is more pronounced and robust under stronger governance (71 countries), while it attenuates under weaker governance (85 countries). Goal-level estimates reveal systematic trade-offs—gains in inequality reduction (SDG 10) and innovation (SDG 9) alongside adverse associations with climate outcomes (SDG 13)—and a structural breakpoint around 2017 consistent with the onset of slowbalisation. The results suggest that diplomacy can support SD, but its payoff depends on governance capacity and the management of cross-goal externalities. Full article

Background: Owing to significant clinical heterogeneity, the achievement of accurate survival forecasting for individuals with colorectal cancer and peritoneal metastasis continues to be a complex undertaking. We aimed to transcend traditional prognostic limitations by evaluating machine learning boosting models against standard regression-based methods in terms of estimating overall survival (OS). Methods: Utilizing a multi-institutional registry of 150 patients diagnosed with synchronous peritoneal metastasis of colorectal cancer, we integrated 124 clinicopathological variables to refine our predictive models. Beyond standard preprocessing—including standardization and median imputation—we rigorously compared XGBoost and LightGBM against Ridge, Lasso, and linear regression via five-fold cross-validation. To specifically address right-censoring, an XGBoost Cox model was implemented and validated using Harrell’s C-index, with SHAP and LIME providing essential model interpretability. Results: Boosting models consistently outperformed linear alternatives, which struggled with high error rates and negative R2 values. Specifically, XGBoost achieved an MAE of 475 ± 60 and an RMSE of 585 ± 88. The XGBoost Cox model reached a C-index of 0.64 ± 0.06. SHAP analysis highlighted inflammatory markers and peritoneal disease extent as the most influential prognostic drivers. Conclusions: While boosting models offer a clear accuracy advantage over linear methods, their prognostic power remains moderate. These findings underscore the potential of ensemble learning in oncology, yet mandate external validation before these tools can be integrated into clinical decision-making. Full article

Background/Objectives: Sodium–glucose cotransporter-2 (SGLT2) inhibitors have demonstrated cardiovascular benefits beyond glycemic control, yet the specific biological pathways potentially linking SGLT2 inhibitor exposure to cardiovascular outcomes after acute myocardial infarction (AMI) remain incompletely characterized. Two biologically plausible pathways, serum uric acid (SUA) reduction and renal functional preservation, have been proposed, but not directly compared in a unified analytical framework. This study aimed to explore whether associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes may be more strongly linked to SUA reduction and to renal functional changes, using a hypothesis-generating causal mediation analysis. Methods: This retrospective observational cohort study included 142 consecutive patients hospitalized for AMI who underwent percutaneous coronary intervention (PCI) during the index hospitalization, reflecting standard-of-care management for AMI in this tertiary center. Patients were categorized by SGLT2 inhibitor exposure (n = 57) vs. controls (n = 85). Both diabetic (47.2%) and non-diabetic (52.8%) patients were included. The primary endpoint was change in SUA (ΔUA); the secondary endpoint was myocardial infarction (MI) recurrence. Causal mediation analysis with nonparametric bootstrap simulation tested both mechanistic pathways. Results: SGLT2 inhibitor therapy was associated with significant SUA reduction (ΔUA = −0.99 mg/dL vs. +0.56 mg/dL in controls; p < 0.001), consistent across diabetic and non-diabetic subgroups and independent of AMI recurrence. Each 1 mg/dL decrease in SUA was associated with lower odds of recurrent MI in the initial model (β = −0.25; p = 0.041). However, after incorporation of renal functional change, the uric acid-mediated pathway lost significance (ACME p = 0.462), whereas the renal-mediated pathway remained significant (ACME p = 0.038). Serum creatinine change emerged as the strongest independent predictor of MI recurrence (β = 2.22; p = 0.015). Conclusions: The findings are more consistent with a renal-mediated pathway than with an independent uric acid-mediated pathway in explaining the observed associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes. These hypothesis-generating results from a retrospective design warrant prospective validation. Full article

Background: Liver transplant recipients are highly susceptible to infectious complications due to surgical invasiveness and immunosuppressive therapy, and post-transplant bloodstream infection is associated with substantial morbidity and mortality. Although several prediction models for bloodstream infection have been proposed, most focus on emergency department or general ward populations and rely on black-box approaches. This limits their applicability and clinical interpretability in liver transplant settings. Therefore, this study aimed to develop predictive models for post-transplant bloodstream infection using preoperative and perioperative clinical data and to derive an interpretable risk equation through symbolic regression. Methods: We conducted a retrospective observational study including 245 adult liver transplant recipients treated at a single tertiary center. Clinical and laboratory variables were extracted from electronic medical records and analyzed using standard statistical methods. For prediction tasks, multiple conventional machine learning models were developed and compared with a symbolic regression-based model. Predictive performance and model interpretability were evaluated using discrimination metrics and Shapley Additive Explanations. Results: Post-transplant bloodstream infection occurred in 82 patients (33.4%). In the test set, conventional machine learning models showed modest discriminative performance (area under the curve, 0.53–0.64). The symbolic regression model achieved comparable discrimination (area under the curve, 0.63) while providing transparent, threshold-based risk equations. While conventional models primarily relied on laboratory variables, symbolic regression additionally identified perioperative clinical factors and viral serologic markers as important predictors. Discussion: Although overall predictive performance was modest, symbolic regression highlighted viral serologic markers as potential indicators of immunologic vulnerability, extending beyond standard laboratory predictors. Conclusions: This interpretability-focused approach may inform future risk stratification models incorporating richer perioperative data. Full article

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions. Full article

Background/Objectives: Machine learning (ML) models can predict hospital admission from emergency department (ED) triage data with areas under the receiver operating characteristic curve (AUC) exceeding 0.85. Whether incorporating the assigned provider’s identity—as a proxy for unmeasured practice variation—improves prediction has not been systematically studied. We aimed to compare 10 supervised ML classifiers for predicting hospital admission at ED triage, with and without provider identity, and to characterize model reasoning using SHapley Additive exPlanations (SHAP). Methods: We conducted a retrospective cohort study of 186,094 ED visits (2020–2023, training) and 58,151 visits (2024, temporal holdout test) at one academic tertiary-care ED. Ten classifiers spanning linear, distance-based, tree-based, ensemble, probabilistic, and neural network families were each trained in two conditions: baseline (23 triage features) and with provider identity appended. SHAP TreeExplainer was applied to the top-performing models (CatBoost and XGBoost). Results: The admission rate was 31.3% (training) and 31.7% (test). CatBoost achieved the highest baseline AUC of 0.8906 (0.8878–0.8933). Adding provider identity produced negligible AUC changes across all models (ΔAUC range: −0.0029 to +0.0015; all DeLong p > 0.05). SHAP analysis identified ESI level, respiratory rate, temperature, complaint category, and age as the dominant predictors, with clinically intuitive directionality. Conclusions: Provider identity does not meaningfully improve ML prediction of hospital admission beyond standard triage variables. The observed 28-percentage-point variation in provider admission rates is explained by patient case-mix differences than with independent practice pattern effects on prediction. SHAP provides transparent, clinically interpretable explanations suitable for bedside decision support. Full article

This study examines the firm-specific factors associated with the level and quality of compliance with the International Integrated Reporting Framework (IIRF) among companies in the United Arab Emirates (UAE), an emerging economy characterized by a growing sustainability-oriented institutional context. Although the Securities and Commodities Authority (SCA) mandates listed companies to publish an integrated report, it does not prescribe a specific reporting framework. As a result, alignment with the IIRF and the depth of disclosure remain largely discretionary. Using a sample of 89 non-financial firms listed on the Dubai Financial Market (DFM) and Abu Dhabi Securities Exchange (ADX), an Integrated Reporting Disclosure Score (IRDS) was constructed through content analysis based on 43 criteria derived from the IIRF. Regression and dominance analyses were employed to examine the relationship between firm characteristics and the level of IIRF compliance. The results indicate that firm size, profitability, board size, and gender diversity are positively associated with higher levels of IIRF alignment and disclosure quality, while financial leverage and board independence are not significantly associated with disclosure levels. The dominance analysis further shows that firm size, board size, gender diversity, and profitability account for the majority of the model’s explanatory power. Overall, the findings contribute to the literature by providing empirical evidence on voluntary compliance with international integrated reporting standards beyond mandatory reporting requirements in an emerging market context. Full article

Background: The Mediterranean diet (MD) represents a nutritionally balanced eating pattern characterized by high consumption of fruits, vegetables, legumes, nuts, whole grains, olive oil, fish, and extra-virgin olive oil as the principal fat source and limited intake of red meat and refined sugars. Emerging evidence indicates that the MD’s anti-inflammatory and antioxidant properties extend beyond systemic health, potentially reducing the risk and severity of periodontitis. This narrative review aimed to synthesize current evidence on the relationship between adherence to the MD and periodontal health outcomes. Methods: A comprehensive electronic literature search was conducted in PubMed without restrictions on publication date. Fourteen studies, ranging from 2019 to 2025, were included, encompassing human, clinical, experimental, and review designs that examined MD adherence and its effects on periodontal parameters. Eligible studies included cross-sectional, cohort, randomized controlled trials; systematic reviews; and animal models assessing clinical periodontal indices, inflammatory biomarkers, or microbial composition. Extracted data included study design, population characteristics, dietary assessment methods, and primary periodontal findings. Results: Most studies demonstrated that greater adherence to the MD was associated with improved periodontal parameters, including reduced probing pocket depth, clinical attachment loss, and bleeding on probing. Interventional trials showed significant reductions in systemic inflammatory markers such as IL-1β, TNF-α, and CRP, along with decreased counts of periodontopathogenic bacteria. Experimental studies further revealed the protective role of oleic acid and polyphenols in regulating macrophage activity, suppressing osteoclastogenesis, and enhancing IL-10 expression via epigenetic modulation. However, heterogeneity in dietary scoring systems, sample characteristics, and follow-up duration limited direct comparison, and not all associations reached statistical significance. Conclusions: Current evidence supports a beneficial association between MD adherence and periodontal health, mediated through anti-inflammatory, antioxidant, and microbiome-stabilizing mechanisms. Further standardized longitudinal and interventional studies are needed to confirm causality and refine nutritional strategies for periodontal disease prevention and management. Full article