Search Results (2,081)

Multidrug-resistant (MDR) bloodstream infections (BSIs) constitute a major challenge in intensive care units, with the COVID-19 pandemic compromising infection control and potentially increasing MDR incidence. Comparative data between COVID and non-COVID ICU populations remain limited. The MARTINI study is a retrospective observational analysis held in a tertiary hospital during the COVID-19 pandemic (2020–2022) encompassing adult patients with MDR BSIs admitted to COVID and non-COVID ICUs. Demographics, comorbidities, severity scores, microbiology, resistance patterns, and outcomes were accessed and compared. A binary logistic regression model and multivariate regression was performed to identify independent predictors of ICU mortality. Among the study’s 156 patients (106 COVID-ICU, 50 non-COVID-ICU), COVID-ICU patients were significantly older with higher comorbidity and severity scores. Gram-negative pathogens predominated in both cohorts, mainly Acinetobacter baumannii and Klebsiella pneumoniae, with comparable resistance mechanisms. Timing of bacteremia onset and initiation of appropriate therapy did not differ between groups. However, ICU mortality was markedly higher in COVID-ICU patients (74.5% vs. 38%, p < 0.001). Age, SOFA score, the presence of systemic inflammation (SIRS) and COVID-19 infection were identified as independent predictors of mortality. Although pathogen distribution and resistance were similar across groups, COVID-ICU patients experienced significantly poorer outcomes. Strengthened infection control and timely and targeted antimicrobial therapy are essential to diminish MDR bacteremia risk in critically ill populations. Full article

Background: Chronic total occlusion percutaneous coronary intervention (CTO PCI) is a complex revascularization procedure requiring advanced techniques to ensure procedural success and safety. Hybrid strategies combining antegrade dissection/re-entry (ADR) and retrograde approaches have become increasingly adopted in contemporary practice. Objectives: To systematically review and synthesize evidence comparing outcomes of ADR and retrograde CTO PCI techniques, with pooled estimates of success rates and adverse events. Methods: This review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane CENTRAL, and Google Scholar for studies published between January 2015 and June 2025. Eligible studies included randomized controlled trials and observational studies reporting outcomes of ADR and/or retrograde CTO PCI. Data extraction was performed by two independent reviewers. Risk of bias was assessed using the Newcastle–Ottawa Scale and the Cochrane RoB 2.0 tool. A random-effects meta-analysis was conducted for consistently reported outcomes. Results: Twenty studies encompassing over 87,000 CTO PCI procedures were included. Pooled analysis of 16 studies demonstrated a technical success rate of 83.4% and a procedural success rate of 84.6%. The in-hospital major adverse cardiac event (MACE) rate was 3.3%. Hybrid strategies integrating ADR and retrograde approaches yielded the highest success rates (86–91%) with acceptable safety profiles. Use of adjunctive tools such as IVUS, dual arterial access, and re-entry devices was associated with improved outcomes. Discussion: Hybrid CTO PCI techniques are safe, effective, and reproducible across diverse clinical settings. When performed by experienced operators using modern adjuncts, these strategies provide durable benefits and should be considered standard for complex occlusions. Limitations include variation in study quality, heterogeneous procedural definitions, and lack of long-term data in several cohorts. Full article

Inclusive design is not a design framework provided by legislation, but a design strategy that takes into account the diversity of abilities of users. Ensuring the mobility of people with disabilities is central to their social inclusion, which is why accessibility standards have been developed. Some elements of this are costly (e.g., low-floor vehicles), but simple and cost-effective solutions can make a significant difference in inclusive public transport. This paper uses qualitative methods—interviews, case studies and literature review—to illustrate one of the problem areas, the difficulties of grabs, and one of the potential areas for improvement: grab rails. In non-crash accidents, properly designed handholds play a key role in preserving the integrity of passengers and in creating a sense of physical/psychological safety, contributing to intuitive and independent use and to the social sustainability of public transport. Full article

Introduction: The digital era has provided the development of innovative health devices that enable the precise characterization of health and disease, facilitating diagnoses and interventions. This study aimed to systematically review and verify the quality of mobile applications (apps) available for the monitoring and assessment of breast cancer-related lymphedema (BCRL). Methods: A systematic search was conducted in the Apple App Store and Google Play Store for apps related to BCRL monitoring and assessment. Two independent reviewers extracted descriptive data and evaluated app quality using the validated User Mobile App Rating Scale (uMARS). Results: Out of 630 apps screened, four met the inclusion criteria and were analyzed. Two Korean apps targeted patients, providing educational content, self-assessment tools, and bilingual interfaces. Two British apps, LymVol and LymphaTech Lite, focused on volumetric measurement and clinical use, although LymVol lacked compatibility with recent Android versions. Quality assessment using the uMARS indicated that the included applications performed consistently across the evaluated domains, despite low download numbers and the absence of user ratings. Conclusions: Although mobile apps have the potential to enhance lymphedema monitoring and assessment, more accessible and scientifically validated tools are needed to ensure effective use by healthcare professionals and patients. Developers are encouraged to create accessible, linguistically inclusive smartphone apps that incorporate standardized assessment protocols and regular updates to ensure usability and accuracy. Rigorous validation studies covering reproducibility, diagnostic accuracy, and real-world clinical outcomes should be conducted by researchers to guarantee safety and reliability. Full article

This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the optimal variant among different Extreme Learning Machine (ELM) models. This tool represents a concrete contribution to the use of artificial intelligence in smart agriculture, enabling more efficient and accessible management of cherry tree crops. The overall goal is to evaluate machine learning-based strategies that enable efficient and low-computational-cost detection of the disease, facilitating its implementation on devices with limited resources. The ERICA database is used by following a proper methodology in order to learning and validation stages are completely independent. Preprocessing includes renaming, cropping, scaling, grayscale conversion, vectorization, and normalization. Subsequently, the impact of reducing image resolution is studied, identifying that a size of 63 × 23 pixels maintains a good balance between visual detail and computational efficiency. Six ELM variants are trained: standard, regularized (R-ELM), class-weighted (W1-ELM and W2-ELM), and multilayer (ML2-ELM and ML3-ELM), and classical machine learning approaches are optimized and compared with classical ML approaches. The results indicate that W1-ELM achieves the best performance among tested variants, reaching an accuracy of 0.77 and a geometric mean of 0.45 with a training time in order of seconds. Full article

This paper introduces a novel observer-based, fully distributed fault-tolerant consensus control algorithm for model-free adaptive control, specifically designed to tackle the consensus problem in nonlinear multi-agent systems. The method addresses the issue of followers lacking direct access to the leader’s state by employing a distributed observer that estimates the leader’s state using only local information from the agents. This transforms the consensus control challenge into multiple independent tracking tasks, where each agent can independently follow the leader’s trajectory. Additionally, an extended state observer based on a data-driven model is utilized to estimate unknown actuator faults, with a particular focus on brake faults. Integrated into the model-free adaptive control framework, this observer enables real-time fault detection and compensation. The proposed algorithm is supported by rigorous theoretical analysis, which ensures the boundedness of both the observer and tracking errors. Simulation results further validate the algorithm’s effectiveness, demonstrating its robustness and practical viability in real-time fault-tolerant control applications. Full article

Visibility and accessibility are two key elements in the qualitative analysis of maintainability and cover most work of such qualitative analysis. At present, visibility and accessibility are analyzed by virtual maintenance technology, which greatly improves the efficiency of maintainability analysis. Generally, in the maintainability analysis based on virtual maintenance, in order to analyze the visibility and accessibility, different analysis tools are established based on human visual features and arm motion features, respectively, for independent analysis. However, in actual maintenance, visibility and accessibility are simultaneously required to better complete maintenance. Therefore, judging whether the object is accessible while it is visible is obviously more efficient than calling different tools to analyze visibility and accessibility, and can better fit engineering practices. In this paper, the quantitative correlation between the optimal human visual range and the maximum accessible range was established by introducing auxiliary objects in the virtual environment based on the basic physiological characteristics of human visibility and accessibility. Whether the object is accessible was judged while it was within the optimal human visual range on the basis of this quantitative correlation. Full article

Background: Metabolic syndrome involves interconnected disturbances in insulin sensitivity, hepatic function, and renal performance. Simple, integrative indices may improve early detection of multisystem metabolic risk. Methods: In this cross-sectional study, adults were stratified into metabolic risk categories (scores 2–11) and evaluated using the triglyceride–glucose (TyG) index, the fibrosis-4 (FIB-4) score, and estimated glomerular filtration rate (eGFR). Correlation analyses and multivariate regression models (HC3 robust standard errors) were applied to identify independent predictors of hepatic (FIB-4) and renal (eGFR) function. Results: TyG and FIB-4 increased significantly with higher metabolic risk (ANOVA p < 10⁻⁶), while eGFR showed a mild, non-significant decline. TyG correlated strongly with triglycerides (r = 0.78) and fasting glucose (r = 0.69), whereas FIB-4 correlated inversely with eGFR (ρ = −0.30). In regression models, age was the strongest predictor of both FIB-4 (β_std = 0.33) and eGFR (β_std = −0.47). Additional predictors of lower eGFR included FIB-4, systolic blood pressure, BMI, and UACR, whereas TyG showed no independent effect after adjustment. Conclusions: The combined use of TyG, FIB-4, and eGFR provides complementary insight into the metabolic–hepatic–renal continuum. These indices highlight progressive insulin resistance, hepatic stress, and subclinical renal involvement, supporting their utility as accessible tools for early identification of high-risk metabolic phenotypes. Full article

Background: Gastric cancer (GC) remains a significant health burden in the U.S, particularly among ethnic minorities. We identified patient-level risk factors contributing to advanced-stage (AS) diagnosis and poor survival to guide strategies to address GC-related health disparities. Methods: We conducted a retrospective cohort analysis of 18,396 histologically confirmed GC cases (4102 early-stage (ES) and 14,294 AS) diagnosed between 2000 and 2019, using data from the California Cancer Registry linked to the California Office of Statewide Health Planning and Development. Eligible cases were adults age ≥ 18 with complete diagnostic and follow-up data. Multivariable logistic and Cox regression models were used to identify predictors of AS-GC and five-year disease-specific (DSS) and overall-survival (OS) outcomes. Analyses were further stratified by Asian and Hispanic subgroups. Results: Korean heritage was the strongest predictor of ES-GC [OR 0.58 (95% CI, 0.47–0.71), p < 0.001] and was independently associated with the lowest GC-specific mortality risk [HR 0.73 (95% CI: 0.67–0.80), p < 0.0001]. The youngest age group (18–44 years) had the highest AS-GC rate (91.4%). Asian ethnicity, receipt of care at NCI-designated cancer centers, and prior upper endoscopy were associated with improved OS and DSS. In contrast, comorbidities such as GERD, diabetes, liver disease, smoking and alcohol abuse, and older age ≥ 75, U.S.-birth, and rural residence were linked to worse outcomes. Conclusions: Distinct demographic, clinical, and healthcare access factors contribute to disparities in GC outcomes. These findings support the development of culturally tailored early-detection programs, and risk-based screening for GC care, particularly in vulnerable populations. Full article

In this article, digital transformation is examined as a key driver of structural and pedagogical change in higher education. This process is shown to expand access to learning, increase flexibility, support personalized educational trajectories, and enhance data-driven decision-making. At the same time, the effectiveness of digital transformation depends on institutional readiness, the quality of technological infrastructure, and the professional competencies of teaching staff. This research of this study is to assess the influence of digital transformation on the quality of higher education. This research employs a mixed-methods approach. Quantitative data from surveys of 4971 students and 483 instructors were analyzed using descriptive statistics, analysis of variance ANOVA, and multivariable regression, while qualitative focus group findings were examined through thematic analysis. The results indicate generally positive attitudes toward digitalization. The respondents emphasized flexibility and improved conditions for independent learning as key advantages of digital environments. However, this study also identifies several challenges, including infrastructural inequality, limited digital skills, and insufficient pedagogical adaptation. The article concludes that successful digital transformation requires a comprehensive strategic vision and sustained institutional support. For universities, strengthening digital competencies, modernizing infrastructure, and implementing management models focused on continuous improvement are essential conditions for ensuring sustainable development and enhancing the quality of education. Full article

Dementia significantly impacts cognitive, behavioral, and functional abilities, creating challenges for both individuals and caregivers. Recent advancements in HCI have introduced innovative technological solutions to support people with dementia (PwD) and their caregivers. This scoping review systematically examines 32 recent publications from leading digital libraries, categorizing technological interventions into four key domains: Assistive and Smart Technology for Daily Life, Social Interaction and Communication, Well-being and Psychological Support, and Caregiver Support and Training. Our analysis highlights how emerging technologies are transforming dementia care. These technologies enhance quality of life by promoting independence, fostering social engagement, and providing emotional and cognitive support. However, the review also identifies critical gaps, particularly in addressing the needs of individuals with early-stage dementia and the lack of individualized support mechanisms. By emphasizing user-centered design, accessibility, and ethical considerations, this paper offers a structured roadmap for future research and practice in dementia care. It bridges the gap between technological innovation and the real-world needs of PwD and their caregivers, providing valuable insights for researchers, practitioners, and policymakers. This review not only synthesizes current advancements but also sets the stage for future HCI-driven innovations in dementia care, aiming to improve outcomes for an aging global population. Findings primarily reflect older adults; implications for early-onset dementia are discussed. Full article

This study investigates the efficacy of large language models (LLMs) in supporting responsible and optimized geographic site selection for large-scale solar energy farms. Using Microsoft Bing (predecessor to Copilot), Google Bard (predecessor to Gemini), and ChatGPT, we evaluated their capability to address complex technical and social considerations fundamental to solar farm development. Employing a series of guided queries, we explored the LLMs’ “understanding” of social impact, geographic suitability, and other critical factors. We tested varied prompts, incorporating context from existing research, to assess the models’ ability to use external knowledge sources. Our findings demonstrate that LLMs, when meticulously guided through increasingly detailed and contextualized inquiries, can yield valuable insights. We discovered that (1) structured questioning is key; (2) characterization outperforms suggestion; and (3) harnessing expert knowledge requires specific effort. However, limitations remain. We encountered dead ends due to prompt restrictions and limited access to research for some models. Additionally, none could independently suggest the “best” site. Overall, this study reveals the potential of LLMs for geographic solar farm site selection, and our results can inform future adaptation of geospatial AI queries for similarly complex geographic problems. Full article

The COVID-19 pandemic has profoundly influenced healthcare systems and infection control worldwide, with important implications for the epidemiology of antimicrobial resistance. This study examined the prevalence and characteristics of carbapenem-resistant Enterobacterales (CRE) isolates in Southeastern Austria from 2018 to 2022 to assess potential pandemic-related effects. A total of 63 isolates were analyzed using phenotypic and molecular methods, including carbapenemase detection, genotyping, and multilocus sequence typing. The number of CRE isolates appeared to decline during the pandemic years (2021–2022) compared to the pre-pandemic period, with Enterobacter cloacae notably detected in both full pandemic years. Carbapenemase-producing CRE accounted for 44 out of the 63 isolates (69.8%), with metallo-beta-lactamases (VIM-1 and NDM-1) and OXA-48-like carbapenemases predominating. Resistance mechanisms not based on carbapenemase production were more common before the pandemic but rarely detected thereafter. To our knowledge, this is the first report of dual-carbapenemase-producing CRE isolates in Austria. Multi-locus-sequence typing indicated limited nosocomial transmission, with most isolates representing independent introductions linked to external sources. The decline in CRE prevalence may reflect reduced international travel and healthcare access during the pandemic, which could have limited the importation of resistant strains. These findings reflect the potential role of global mobility in the spread of CRE and illustrate how public health interventions can shape antimicrobial resistance trends. Full article

The global shift toward digital pathology necessitates accessible and cost-effective training tools, posing a significant challenge for resource-constrained veterinary faculties. The objective of this study was to evaluate the academic impact of a cost-effective, in-house-developed digital pathology platform implemented at a Romanian Veterinary Faculty during the 2024–2025 academic year. Grade data from six academic years (2018–2025) were used as historical controls. Statistical analysis, including independent-samples t-tests, ANOVA, and multivariable regression, was performed on individual student grade data to assess changes in academic performance and grade distribution. Results demonstrate a statistically significant increase in mean grades and a higher proportion of top-performing students following platform adoption. The 2024–2025 cohort outperformed four of five prior cohorts, showing a marked shift toward higher grades and fewer low grades. We conclude that this low-cost digital pathology platform may significantly enhance student academic outcomes in veterinary pathology education. These findings validate a scalable and sustainable model for improving pedagogical delivery in resource-conscious institutions worldwide. Further longitudinal research is essential to track the long-term sustainability of these academic gains and confirm the platform’s impact across multiple graduating cohorts. Full article

Background/Objectives: Age-related declines in motor and functional abilities can compromise independence and quality of life in later life. Motor competence (MC) plays an important role in maintaining quality of life and independence. However, few reliable instruments exist to assess MC in this population. The study adapts the Motor Competence Assessment (MCA) battery to meet the MC assessment and safety requirements of community-dwelling older adults. Methods: Seventy-six community-dwelling, physically active older adults (age = 73.4 ± 7.0 years) enrolled in a multi-phase adaptation process involving expert review, pilot and field testing, and validation of six motor tasks across three MC domains. Adaptations emphasized in the following four stages: accomplishing participant safety, autonomy, and the reliability of MC measurement principles. Results: The adapted version demonstrated very high completion rates, being safe and reliable for accessing MC, showing strong reliability in the manipulative domain. The use of the Challenge by Choice principle improved participant autonomy, confidence, and perceived motor competence. The main alterations to stability and locomotor tasks allow feasibility while maintaining test validity. Adjustments in instructions and practice trials reduced cognitive load and improved performance, addressing age-related perceptual and comprehension challenges. Ball Kicking and Throwing Velocity tests showed high reliability (ICC between 0.828 and 0.925), with minor gender-related differences. Conclusions: The adapted MCA is a safe, feasible, and reliable instrument for assessing MC in community-dwelling older adults. It preserves the conceptual foundations of MC while accommodating age-related MC alterations, offering a valuable resource for research and clinical applications. Full article