Search Results (45,676)

Background/Objectives: Acute-on-chronic liver failure (ACLF) is a life-threatening complication of cirrhosis, characterized by organ failures and high short-term mortality. Alcohol-related cirrhosis is one of the most frequent underlying etiologies of ACLF in Europe. Infections, particularly those leading to sepsis are recognized triggers; however, their relative contribution, clinical features, and prognostic impact in critically ill patients with alcohol-related cirrhosis remain incompletely defined. This study aimed to systematically identify and characterize precipitating events of ACLF in this population and to compare outcomes between sepsis- and non-sepsis-related cases. Methods: We conducted a retrospective cohort study including 188 ICU patients with alcohol-related cirrhosis who were treated for ACLF at a tertiary university medical center. ACLF was defined and graded according to the European Association for the Study of the Liver—Chronic Liver Failure Consortium (EASL-CLIF) criteria, and sepsis was diagnosed according to Sepsis-3 definitions. Clinical data, precipitating events, microbiological evidence, organ support requirements, and in-hospital outcomes were systematically analyzed. Results: Sepsis was the most frequent precipitating event, identified in 118 patients (62.8%), while 70 patients (37.2%) developed ACLF due to non-septic triggers such as gastrointestinal bleeding. Patients with sepsis-associated ACLF presented with more advanced disease (ACLF grade 2–3 in 80.5% vs. 57.1%, p = 0.004), higher Chronic Liver Failure Consortium—Acute-on-Chronic Liver Failure Score (CLIF-C ACLF) scores (median 55 vs. 50, p = 0.04), longer ICU stays (median 11 vs. 4.5 days, p < 0.001), and markedly higher in-hospital mortality (60.2% vs. 20.0%, p < 0.001) compared to patients without sepsis. Pneumonia (48.3%), urinary infections (17.8%) and spontaneous bacterial peritonitis (16.1%) were the leading infectious foci triggering sepsis. Microbiological evidence was obtained in 82.2% of sepsis cases, with frequent polymicrobial infections and opportunistic pathogens including Enterococcus faecium and Candida albicans. Conclusions: In critically ill patients with alcohol-related cirrhosis, infections leading to sepsis are the predominant precipitating event of ACLF and the strongest determinant of short-term prognosis. Compared with non-sepsis triggers, sepsis-associated ACLF is characterized by more severe disease, greater need for organ support, longer ICU stays, and substantially higher mortality. These findings highlight the urgent need for early recognition, rapid diagnostic strategies, and optimized infection management to improve outcomes in this high-risk population. Full article

We investigated respiratory virus epidemiology in older adults across pre-pandemic (2007–2019), pandemic (2020–2022), and post-pandemic (2023–2024) periods, focusing on how public health interventions shaped surveillance, prevalence, and sex-specific trends. We conducted a retrospective cross-sectional study at a 1000-bed tertiary hospital in the Republic of Korea during 2007–2024, analyzing 4692 nasopharyngeal swab specimens collected from adults aged ≥ 65 years with suspected respiratory infections during 2007–2024. The specimens were tested for 15 respiratory viruses using multiplex real-time polymerase chain reaction. The outcomes included virus-specific detection rates and seasonal, sex-based and temporal trends before and after the COVID-19 pandemic. During the pre-pandemic period, older adults accounted for 13.2% of the tested individuals, which significantly increased to 52.0% in the later periods. Influenza A was the most frequently detected virus, followed by rhinovirus and human metapneumovirus. Influenza, RSV A/B, and coronaviruses 229E and OC43 showed peak positivity in winter, parainfluenza virus type 3 peaked in summer, and rhinovirus circulated year-round. Virus circulation was markedly suppressed during 2020–2022 and partially rebounded during 2023–2024. This study highlights the shift in diagnostic access and epidemiologic patterns of respiratory virus infections in older adults following the COVID-19 pandemic. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

Reflection skills are a key but challenging element in teacher training. Feedback on reflective writing assignments can improve reflection skills, but it is affected by challenges (high variability in judgments and time investment). AI-generated feedback offers many options. Therefore, the aim of this study was to examine the potential of AI-generated feedback compared to that provided by lecturers for developing reflective skills. A total of 93 randomly selected pre-service teachers (70% female) in a course at a German university wrote two reflections and received feedback from either lecturers or ChatGPT 4.0 based on the same prompts. Pre-service teachers’ written reflections were assessed, and an online questionnaire based on standard instruments was applied. Control variables included metacognitive learning strategies and reflection-related dispositions. Based on a linear mixed model, the main effects on reflective skills were identified for time ($\widehat{\beta }$ = 0.41, p = 0.003) and feedback condition ($\widehat{\beta }$ = −0.42, p = 0.032). Both forms of feedback similarly fostered reflective skills over time, with academic self-efficacy emerging as a pertinent disposition ($\widehat{\beta }$ = 0.25, p = 0.014). The limitations of this study and implications for teacher training are discussed. Full article

Background: Muscle latency is an often-overlooked factor contributing to increased implant wear and higher rates of hip and knee osteoarthritis. Latency reduces the protective role of muscles against external joint loads during movement initiation, leading to cumulative microtrauma. This study investigates whether preliminary quadriceps contraction can mitigate these adverse effects during early rehabilitation after arthroplasty. Materials and methods: The study was conducted in two university hospitals in Sofia, Bulgaria, including 46 patients (mean age 63.76 ± 9.49 years): 25 with hip arthroplasty and 21 with knee arthroplasty. Participants were randomly assigned to a control group (n = 25; 13 hip, 12 knee: standard postoperative advice) or an experimental group (n = 21; 12 hip, 9 knee: standard advice plus preliminary quadriceps contraction). Primary outcome: pain intensity (VAS). Secondary outcomes: range of motion (ROM, %), manual muscle testing (MMT, %), thigh circumference difference (cm), and success rate of preliminary quadriceps contraction (%). Results: Both groups improved after one month (p < 0.05), but the experimental group showed significantly greater improvement (p < 0.05). Higher success rates of preliminary quadriceps contraction correlated with greater improvements in all outcomes (p < 0.05). Conclusions: Preliminary quadriceps contraction enhances standard postoperative advice by reducing pain, improving mobility and muscle strength, and reducing hypotrophy during early rehabilitation after hip and knee arthroplasty. Patients should be encouraged to perform it consistently, even when pain subsides. Full article

The proper selection of bioactive root-end material is one of the main prognostic factors for the successful healing outcome of apical microsurgery (AMS). The aim of the present in vitro study was to evaluate and compare the marginal adaptability of a novel calcium silicate cement (CSC), Harvard MTA Universal, and Biodentine used as root-end filling materials. The endodontic treatment of 20 extracted human maxillary central incisors was performed. The apicoectomy was simulated, and root-end cavities were prepared ultrasonically using universal retrotips. Teeth were randomly assigned into two equal groups (n = 10) according to the retrofilling cement used: Group 1—Harvard MTA Universal and Group 2—Biodentine. The specimens were stored in relative humidity for 48 h and sectioned longitudinally. The data were processed and analyzed statistically. Harvard MTA exhibited a significantly lower mean gap width (1.16 ± 0.37 µm) than Biodentine (2.48 ± 0.38 µm) (p < 0.05), indicating a more intimate interfacial adaptation. Additionally, the phenomenon of material penetration into the dentinal tubules was observed only in the Harvard MTA group. Within the limitations of this in vitro study, Harvard MTA Universal demonstrated better interfacial properties than Biodentine when applied as a root-end filling material. This novel biomaterial could be regarded as a promising alternative for earlier calcium silicate cements in the context of AMS goals. Clinical relevance: The quality of marginal adaptation is a determinative feature for the clinical performance of CSCs and the long-term prognosis of AMS. Full article

Universal Stress Protein (USP) plays crucial roles in plant stress adaptation, yet their evolutionary dynamics, regulatory mechanisms, and functional diversification in rice (Oryza sativa) remain poorly understood. This study aimed to conduct a genome-wide identification and characterization of the OsUSP gene family to elucidate its role in abiotic stress responses using integrated bioinformatics approaches. Here, we identified 46 OsUSP genes that are unevenly distributed across 11 rice chromosomes and exhibit significant divergence in protein length, molecular weight, and subcellular localization. Phylogenetic analysis classified OsUSPs into three subfamilies, with conserved motif and domain architectures within groups but distinct structural variations across subfamilies. Evolutionary analysis revealed strong collinearity between rice and other monocots, which suggests functional conservation in grasses, whereas limited synteny with dicots indicates lineage-specific divergence. Cis-regulatory element analysis showed enrichment in ABA, MeJA, drought, and hypoxia response motifs, implicating OsUSPs in hormonal and stress signaling. Expression profiling indicated tissue-specific patterns, with subfamily III genes broadly expressed, while subfamily II members were anther-enriched. Stress response profiling revealed that 24 OsUSPs were significantly induced, while LOC_Os02g54590 and LOC_Os05g37970 emerged as particularly notable due to their broad-spectrum responsiveness, being upregulated under all tested stress conditions. Protein–protein interaction (PPI) analysis indicated that OsUSP proteins potentially interact with Leo1/TPR-domain proteins and are involved in stress response and phosphorylation signaling pathways. This study yields key insights into OsUSP-mediated stress adaptation in rice and pinpoints promising candidate genes to facilitate the breeding of climate-resilient rice. Full article

Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios. Full article

The ubiquitous presence of smartphones has led to new phenomenon such as “phubbing” (the act of ignoring one’s immediate surroundings in favor of using a mobile phone). This behavior has become increasingly common among university students, making it an important subject of study due to its potential negative impact on learning environments. The aim of the present study is to analyze the prevalence of phubbing among university students, the existence of significant differences as a function of specific sociodemographic variables (such as gender, age, academic performance, and connection frequency), and, lastly, the predictive capacity of these elements with the different levels of phubbing experienced. The sample was composed of 1121 Spanish university students, and the instrument selected for the collection of data was the Phubbing Scale, which was divided into three factors, “attachment to the mobile phone”, “communication disturbance”, and “smartphone obsession”, through different validity and reliability tests. The results indicated a moderately high prevalence of phubbing among the population studied. Likewise, statistically significant differences were identified at a multivariate level in the three dimensions. Lastly, it is notable that the frequency of smartphone usage significantly and positively predicted the three dimensions of phubbing. Full article

Despite advances in vaccination and treatment, the emergence of Long COVID cases has highlighted the continued public health concern posed by the disease. Studies on the early prediction of COVID-19 severity and the identification of associated biomarkers are decisive for preventing Long COVID. The objective is to utilise laboratory test data from patients diagnosed with COVID-19 and apply machine learning techniques to predict disease severity and identify associated biomarkers. From a university hospital in southern Brazil, we processed biochemical and haematological data from patients with COVID-19 (non-severe = non-ICU admission; severe = ICU admission). The data were used to train 15 machine learning algorithms to predict patient prognosis. The Light Gradient Boosting Machine (LGBM) model demonstrated the most effective performance in predicting the prognosis of patients with COVID-19, with accuracy, sensitivity, specificity, and precision values between 80 and 88%. Biomarkers associated with disease severity included Platelets, Creatinine, Erythrocytes, C-reactive protein, Lymphocytes, Albumin, Glucose, Urea, and Sodium. The results of this study demonstrate that machine learning, particularly LGBM, is an effective method for predicting the severity of COVID-19. Identifying specific biomarkers associated with disease severity is crucial for the early intervention and prevention of Long COVID, thereby improving clinical outcomes and patient management. LGBM maintained its performance across different age groups. Full article

We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article

Objectives: Histopathological growth patterns (HGPs) of liver metastases have been shown to possess prognostic significance. To date, only 2 studies have evaluated the reproducibility of HGP assessment. The aim of our study was to assess the interobserver reproducibility of HGP classification in liver metastases. Methods: A retrospective, consecutive, single-center cohort study was conducted, including patients who underwent surgical resection for liver metastases at the University of Szeged between 2011 and 2023. A comprehensive database was established, incorporating basic histopathological data for each case. Histological slides were independently reviewed by 2 pathologists, 3 pathology specialist trainees, and 2 medical students with varying levels of experience in gastrointestinal pathology. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC) and Fleiss’ kappa. Results: The study included resection specimens from 205 patients, comprising 336 metastatic lesions, predominantly of gastrointestinal origin (n = 188). Excellent interobserver agreement was observed among specialist trainees (ICC = 0.911) and board-certified pathologists (ICC = 0.984). Overall agreement among all 7 evaluators was good (ICC = 0.822). Conclusions: Our findings demonstrate that HGPs can be reliably assessed by individuals with at least 2 years of experience in general pathology. To our knowledge, this is the first study to include the largest number of board-certified pathologists and pathology specialist trainees in a HGP reproducibility analysis. Additionally, no comprehensive literature review on this topic has been previously conducted. Full article

Background: Immunization coverage has increased substantially in the Western Pacific Region, saving millions of lives and supporting disease elimination efforts. However, gaps in coverage and inequitable vaccine access persist, leaving millions unvaccinated. Wealth-based inequalities remain a critical barrier to achieving equitable immunization coverage and maximizing the health benefits of vaccination programs. Methods: We analyzed full immunization coverage among 1-year-olds in 10 middle-income countries of the Western Pacific Region using data from the WHO Health Inequalities Data Repository. National and wealth quintile-specific coverage rates and within-country inequalities were assessed using absolute and relative measures (difference, ratio, slope index of inequality, and relative index of inequality). Trends over time were examined in countries with longitudinal data (n = 5), identifying pro-rich or pro-poor changes based on shifts in quintile-specific coverage. We also calculated the population attributable risk (PAR) and fraction (PAF) to estimate the potential increase in national coverage if wealth-based inequalities were eliminated. Findings: Substantial gaps in immunization coverage persist across all countries studied (n = 10), but with substantial between- and within-country disparities. Coverage was higher among the richest quintiles in half of the countries, with the rest showing no significant disparities. Trends in inequalities were mixed: Cambodia, Mongolia, and Viet Nam experienced pro-poor improvements over time; the Philippines saw widening pro-rich inequalities; and Lao PDR showed little change. Population attributable risks (PAR) showed that eliminating wealth-based inequalities could increase national coverage significantly in five countries (Fiji, Lao PDR, Papua New Guinea, Samoa, and Tonga), with relative gains that could increase national coverage by up to 50% while achieving equity gains. Conclusions: Addressing wealth-based inequalities in immunization could drive substantial gains in national coverage across the Western Pacific Region. Sustained, equity-oriented approaches are essential to achieving universal vaccine access and ensuring no population is left behind. Inequality patterns can guide equity-focused policies to reach underserved and disadvantaged populations. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article