Search Results (14,283)

Background: Intensive care units (ICUs) are high-acuity environments that require respiratory therapists (RTs) to maintain vigilance, manage emotions, and make rapid clinical decisions. In such settings, performance stability is critical for patient safety. Although emotional intelligence (EI) and work–life balance (WLB) have been linked to professional outcomes in health care, their independent and direction-specific associations with job performance among ICU respiratory therapists remain underexamined. Methods: A national cross-sectional survey was conducted among respiratory therapists working in ICUs across Saudi Arabia (June 2025–January 2026). EI was measured using the Wong and Law Emotional Intelligence Scale. WLB was assessed using the work interference with personal life (WIPL), personal life interference with work (PLIW), and work–personal life enhancement (WPLE) scales. Job performance was evaluated using the Individual Work Performance Questionnaire. Correlation and multivariable linear regression analyses were performed to estimate independent associations. Results: A total of 392 RTs were included in the final analysis. Higher EI was independently associated with greater task performance (B = 0.21, p < 0.01) and contextual performance (B = 0.30, p < 0.001), and with lower counterproductive work behaviours (B = −0.24, p < 0.001). Among WLB dimensions, PLIW showed the strongest adverse association, predicting lower task performance (B = −0.20, p < 0.05) and higher counterproductive behaviours (B = 0.39, p < 0.001), but was not significantly associated with contextual performance in the fully adjusted model. WPLE demonstrated modest positive associations with performance, whereas WIPL was not significant in adjusted models. Conclusions: Job performance among ICU respiratory therapists is shaped by both emotional regulatory capacity and cross-domain strain. Personal life interference with work emerged as the most influential adverse predictor, whereas EI was associated with constructive performance patterns. Findings should be interpreted in light of the cross-sectional design and self-reported data. Sustaining performance in high-acuity settings requires attention to emotional competencies and structural sources of role conflict alongside clinical expertise. These findings inform workforce strategies to support performance and sustainability in critical care settings. Full article

Population aging has markedly increased the burden of cancer in older adults, in whom frailty, sarcopenia, and reduced physiological reserve limit tolerance to treatment and worsen clinical outcomes. Aging is accompanied by progressive functional decline and by biological processes such as cellular senescence, characterized by irreversible cell cycle arrest, chronic low-grade inflammation, and impaired immune surveillance. The accumulation of senescent cells and the persistence of a senescence-associated secretory phenotype contribute to tissue dysfunction and generate a microenvironment that favors tumor initiation and progression. Physical exercise has been associated with attenuation of inflammation, improvements in metabolic and immune function, and with lower levels of senescence-related biomarkers. Although aerobic exercise has been extensively studied in this setting, resistance training holds relevance for older adults due to its capacity to counteract sarcopenia, preserve muscle strength and power, and sustain functional independence. Structured and periodized approaches to resistance exercise may further enhance these benefits by delivering targeted stimuli aligned with age-related physiological deficits. Block strength training (BST), a periodized model that concentrates training adaptations into sequential phases of maximal strength, power, and muscular endurance, has demonstrated consistent improvements in functional performance and reductions in frailty risk in community-dwelling older adults. BST improves physical function. It may also influence biological processes related to aging and cancer; however, mechanistic evidence specific to BST remains to be established. We hypothesized that the exercise in block as a targeted, a structured and physiologically grounded resistance training intervention highlights the potential of BST to promote functional aging and healthy. In the case of cancer biology, and the environment near to tumour, the relationship between aging mechanisms in older adults and controlled exercise effects are currently in advance, but mechanistic trials are still lacking. Finally, we propose a novel training method, structured and personalized, that could impact different clinical outcomes in older patients with cancer. Full article

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

Pediculosis remains a public health problem among primary schoolchildren worldwide, including in Thailand. This study aimed to determine the prevalence of pediculosis and identify associated determinants among primary schoolgirls from different socio-geographic regions of Thailand to inform effective control strategies. A cross-sectional survey was conducted among 494 schoolgirls from eastern, northeastern, and southern provinces. Data on demographic characteristics, socioeconomic status, personal hygiene practices, parental knowledge and attitudes toward head lice, and school health policies were collected using questionnaires and interviews with school administrators. Univariable analyses and a generalized linear mixed model (GLMM) with school as a random effect were used to account for clustering. The overall prevalence of pediculosis was 50.81% (95% CI: 46.31–55.20), with significant variation across provinces. In univariable analysis, several factors were associated with infestation. However, after accounting for clustering, only class level (adjusted OR = 3.09; 95% CI: 1.31–7.29) and self-performed hair washing (adjusted OR = 2.93; 95% CI: 1.57–5.49) remained significantly associated with pediculosis, while other associations were attenuated. Parental knowledge was moderate, and commonly held beliefs regarding prevention and treatment varied. None of the participating schools had routine head lice screening policies. These findings indicate that pediculosis is likely influenced by both individual and school-level factors. Control efforts may benefit from coordinated school-based approaches, alongside improved access to effective treatment and targeted health education. Full article

Background/Objectives: Regadenoson is increasingly used as a vasodilator stress agent for perfusion cardiac magnetic resonance (CMR) imaging due to its favorable pharmacologic profile. However, its long-term prognostic value in patients with myocardial ischemia remains insufficiently established. Methods: We retrospectively analyzed all regadenoson stress-CMR studies performed at our institution between May 2017 and July 2020, including patients with follow-up longer than three months. Inducible ischemia and late gadolinium enhancement (LGE) were assessed using standardized criteria. The primary composite endpoint included cardiovascular death, non-fatal myocardial infarction, late coronary revascularization (≥3 months after CMR), or hospitalization for unstable angina. Event-free survival was analyzed with Kaplan–Meier curves, and prognostic factors were evaluated using a Fine–Gray competing-risks model. Results: Of 705 examinations, 698 were eligible, and 517 patients (78.5%) completed follow-up over a median of 1.93 years (IQR 1.37–2.79). Inducible ischemia was identified in 142 patients (27.5%). During follow-up, 38 composite events occurred. Event incidence was significantly higher in patients with ischemia (109.6 events/1000 person-years; 95% CI 75.7–158.7) than in those without (13.3 events/1000 person-years; 95% CI 7.2–24.7; RR 8.25; 95% CI 4.01–16.98; p < 0.001). In multivariable analysis, inducible ischemia independently predicted adverse outcomes (HR 4.50; 95% CI 1.86–10.9; p = 0.001), whereas LGE was not independently associated (HR 1.28; 95% CI 0.46–3.57; p = 0.63). Conclusions: Regadenoson stress-CMR provides robust medium-term risk stratification in patients with suspected or known coronary artery disease. Detection of inducible ischemia strongly predicts major cardiovascular events, underscoring its prognostic and clinical relevance. Full article

Gender-based and sexual violence are major public health concerns, and norms about consent are central to their prevention. This study examines how attitudes toward sexual consent relate to digital sexual consent and to the occasional feeling of distrust in public consent campaigns and institutions. We conducted a cross-sectional online survey embedded in the evaluation of a municipal consent campaign in Zaragoza (Spain). Adults (N = 404; 56.7% women) completed a 14-item short version of the Sexual Consent Scale–Revised, two items on digital sexual consent, and three items on institutional reluctance (perceived “sermonizing” tone, distrust in effectiveness, and lack of personal identification with the message). Correlation and multiple regression models with robust standard errors were estimated, controlling for gender, age, education, income, relationship status, and social media use. Attitudes toward sexual consent were strongly and positively associated with digital sexual consent. Gender was the most consistent sociodemographic correlate: men showed less egalitarian attitudes than women across all consent measurements. Institutional reluctance was systematically related to less supportive consent attitudes: perceiving institutional messages as exaggerated or personally irrelevant predicted lower support for sexual and digital consent norms, whereas trust in the campaign’s effectiveness was associated with more egalitarian attitudes. The findings support the continuity between sexual and digital consent and highlight gender and institutional trust as key determinants for the prevention of gender-based and sexual violence. Public health and social policies should integrate digital consent into consent education and co-design campaigns that minimize defensive reactions and rebuild trust in institutions. Full article

Background and Objectives: Metaplastic breast carcinoma (MBC) is a rare, aggressive malignancy that is often resistant to conventional chemotherapy and characterized by a triple-negative phenotype. While immune checkpoint inhibition shows promise, the prognostic significance and distribution of programmed death-ligand 1 (PD-L1) expression within the heterogeneous architecture of MBC remain poorly understood. This study aimed to evaluate PD-L1 expression and the density of tumor-infiltrating lymphocytes (TILs) to clarify their roles in patient stratification and overall survival (OS). Materials and Methods: We retrospectively analyzed 48 MBC cases diagnosed between 2010 and 2025. PD-L1 expression was quantified using the Combined Positive Score (CPS) with the 22C3 antibody clone across diverse histological components. The density of stromal TIL density was assessed following internationally standardized guidelines. Clinical outcomes and clinicopathological parameters, including metastasis, lymphovascular invasion (LVI), and histological subtype, were correlated with biomarker status using Kaplan–Meier survival analysis and Cox proportional hazards regression models. Results: PD-L1 positivity (CPS ≥1) was identified in 72.9% of cases, one of the highest rates documented in literature. Notably, an inverse relationship was observed with PD-L1-negative tumors, which exhibited significantly higher rates of distant metastasis (46.2% vs. 17.1%; p = 0.039). Multivariate analysis confirmed that low density of TILs (HR = 9.66; p = 0.016), metastasis (HR = 4.40; p = 0.023), and the presence of LVI (HR = 3.84; p = 0.047) were strong independent predictors of mortality. While PD-L1 status alone did not directly dictate overall survival, mean overall survival was markedly reduced in the low TILs cohort (32.2 months) compared to the high TILs group (114.2 months). Conclusions: The high prevalence of PD-L1 expression supports routine screening for immunotherapy eligibility in MBC. Our findings suggest that PD-L1-negative cases represent a high-risk biological subset driven by alternative immune evasion mechanisms. Integrating TIL density with conventional pathological parameters provides a more robust prognostic framework, enabling personalized therapeutic strategies for this challenging malignancy. 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

Data-driven approaches to blood glucose predictive modeling face significant challenges due to the inherent variability in biological systems. While these methods efficiently capture statistical patterns through automated processes, they often lack the biological interpretability necessary to link model behavior with underlying physiological mechanisms. In contrast, physiological models offer accurate mechanistic representations but require complex parameterization and specialized domain expertise. In this work, we present an approach for predicting blood glucose levels (BGLs) leveraging the concept of physiology-informed neural networks (PINNs). This approach addresses the challenge of BGL prediction by incorporating the parameters of insulin and meal dynamics within the architecture of a predictive network. It employs a two-stage learning approach for modeling physiology and predicting BGLs. The neural network is pretrained to approximate the solutions of the physiological dynamics, and the output of this pretrained model, representing the insulin and glucose concentration states, is then fed as input into a predictive model, enabling simultaneous optimization of predictive accuracy and physiological parameter estimation, offering advantages over traditional modeling approaches in terms of personalized prediction and interpretability. The results highlight the model’s ability to estimate physiological parameters while maintaining strong predictive performance that aligns with the underlying physiological principles. This framework offers significant potential for personalized predictive modeling where precise and efficient understanding of individual metabolism is essential. Full article

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape for hepatocellular carcinoma (HCC); however, a considerable proportion of patients do not achieve durable clinical benefits. This highlights the need for reliable predictive biomarkers, which are currently lacking. The accumulated evidence supports a relevant role of the gut–liver axis in modulating immunotherapy outcomes, and several studies have identified distinct microbial features associated with either responders or non-responders. Responders to immunotherapy frequently present with higher microbial diversity and enrichment of beneficial taxa, whereas the expansion of pro-inflammatory and pathogenic bacteria has been associated with primary resistance and increased treatment-related toxicity in non-responders. However, the available findings remain heterogeneous across cohorts, likely owing to differences in geography, diet, liver disease etiology, treatment regimens, and microbiome analytical methods. Machine-learning models integrating metagenomic and metabolomic data have shown encouraging results in defining microbial signatures associated with treatment outcomes, although variability among cohorts currently limits their clinical applicability and generalizability. Beyond microbial taxonomic composition, microbiota-derived metabolites—such as short-chain fatty acids, bile acids, inosine, and tryptophan catabolites—appear to play a crucial role in shaping the tumor microenvironment and host immune responses, thus representing additional candidate biomarkers, also due to the relative ease of their measurement. Finally, microbiota-targeted interventions are emerging as potential strategies to enhance immunotherapy efficacy. Overall, the gut microbiome and its metabolic activity represent promising tools, albeit still under investigation, for patient stratification and personalized management in HCC treated with ICIs. Therefore, this review aims to summarize and critically discuss the current evidence on gut microbiota-derived biomarkers of response and resistance to ICIs in HCC, with particular focus on microbial composition, microbiota-related metabolites, and emerging microbiome-based therapeutic strategies. This narrative review provides an updated overview of the role of gut microbiota as both a biomarker and a therapeutic target in patients with hepatocellular carcinoma (HCC) receiving immune checkpoint inhibitor (ICI) therapy. Full article

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes. Full article

Background: The metamorphopsia questionnaire (MeMoQ) is an established patient-reported outcome measure (PROM) in the context of macular disease. However, its performance has not been proved in those being treated for various macular conditions with intravitreal anti-vascular endothelial growth factor (Anti-VEGF). The objective was to eliminate misfitting items, enhance measurement precision, and ensure optimal response categorisation. Methods: Rasch analysis was performed iteratively on 2286 responses from patients with macular diseases being treated with Anti-VEGF to optimise the MeMoQ. Fit statistics, reliability indices, person and item separation measures, and principal component analysis (PCA) of residuals were assessed to determine the optimal model. This study was conducted in an outpatient clinic specialising in retinal diseases in Hywel Dda University Health Board. Results: Misfitting items were removed in successive iterations, leading to optimised category probability curves and stable fit statistics for the MeMoQ. The resulting model for all responses included two final items, with person separation remaining inadequate reducing from 1.23 to 1.12 and reliability from 0.60 to 0.56. Category probability curves demonstrated good ordering of response variables with Andrich thresholds separated by >1.2 logits. In the subgroups of neovascular age-related macular degeneration and diabetic macular oedema person separation remained below two and reliability remained low. Conclusions: Rasch analysis demonstrated that the MeMoQ was not a valid or reliable PROM in this patient population. Therefore, the MeMoQ may not provide a reliable index of patient’s perception and visual experience when undergoing Anti-VEGF treatment. Full article

Effective software engineering education today requires tools that adapt to individual learner proficiency and progress, while ensuring positive student engagement. Gamified platforms represent an effective approach to learning and maintaining motivation, but their efficacy depends on a robust underlying architecture. This systematic literature review analyzes state-of-the-art artificial intelligence (AI)-based adaptive architectures designed to support gamified learning tools, highlighting their architectural models (such as intelligent tutoring systems, multi-agent systems, and immersive virtual reality/augmented reality environments), adaptation mechanisms (including Generative AI and chatbots), and personalization strategies. A significant focus is placed on Process Mining and Learning Analytics as methodological approaches to organize learning paths and guide dynamic adaptation based on student behavior. The results of the selected studies demonstrate advantages such as increased engagement, longer-term participation, and personalized learning pace. However, challenges remain, such as common assessment criteria, integrating different technologies, and system scalability. The findings offer concrete insights for designing the next generation of effective gamified learning tools, based on data and software engineering processes. Full article

With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, this paper proposes a bias-corrected federated learning framework tailored for video recommendation over stochastic communication links. At the local training stage, a bias-corrected mechanism is introduced to explicitly account for video duration and user activity, mitigating feature-level bias and enabling the learned representations to more accurately reflect users’ intrinsic preferences. To meet the timeliness requirements of real-time federated learning, the successful upload probability of local model transmission is analytically characterized under time-varying channel conditions. Building upon this probabilistic model, a statistically corrected global aggregation strategy is designed to preserve the unbiasedness of the global update with respect to the ideal fully reliable FedAvg scheme, even when a subset of local nodes fails to upload their models within the specified delay constraint. Comprehensive experimental evaluations validate that the proposed framework significantly improves recommendation accuracy and maintains robustness against communication unreliability in practical distributed environments. Full article