Search Results (4,646)

Artificial intelligence (AI) and machine learning (ML) are reshaping cancer research and care. In pediatric oncology, early evidence—most robust in imaging—suggests value for diagnosis, risk stratification, and assessment of treatment response. Pediatric endocrine tumors are rare and heterogeneous, including intra- and extra-adrenal paraganglioma (PGL), adrenocortical tumors (ACT), differentiated and medullary thyroid carcinoma (DTC/MTC), and gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN). Here, we provide a pediatric-first, entity-structured synthesis of AI/ML applications in endocrine tumors, paired with a methods-for-clinicians primer and a pediatric endocrine tumor guardrails checklist mapped to contemporary reporting/evaluation standards. We also outline a realistic EU-anchored roadmap for translation that leverages existing infrastructures (EXPeRT, ERN PaedCan). We find promising—yet preliminary—signals for early non-remission/recurrence modeling in pediatric DTC and interpretable survival prediction in pediatric ACT. For PGL and GEP-NEN, evidence remains adult-led (biochemical ML screening scores; CT/PET radiomics for metastatic risk or peptide receptor radionuclide therapy response) and serves primarily as methodological scaffolding for pediatrics. Cross-cutting insights include the centrality of calibration and validation hierarchy and the current limits of explainability (radiomics texture semantics; saliency ≠ mechanism). Translation is constrained by small datasets, domain shift across age groups and sites, limited external validation, and evolving regulatory expectations. We close with pragmatic, clinically anchored steps—benchmarks, multi-site pediatric validation, genotype-aware evaluation, and equity monitoring—to accelerate safe, equitable adoption in pediatric endocrine oncology. Full article

Background: Patient-centered care (PCC) is a cornerstone of quality, yet its translation into managerial decision-making remains underexplored. Middle nurse managers (MNMs) play a pivotal role in enabling patient-centeredness, but their perspectives on PCC decisions are rarely investigated. Aim: This study explored MNMs’ perceptions of what constitutes a patient-centered decision in hospital settings and identified the essential dimensions underpinning such decisions. Methods: A qualitative descriptive design was adopted using semi-structured interviews. Thirty-eight MNMs from three hospitals in central Italy were included. Data were analyzed using Elo and Kyngäs’ content analysis approach. Results: Two overarching themes emerged as central to patient-centered managerial decision-making (PCMDM): “Meaning and definition of PCMDM,” and “Influencing dimensions of PCMDM”. MNMs described PCMDM as an evolving and adaptable process shaped by patient needs and organizational constraints and unfolding across distinct phases. Key influencing dimensions included the manager’s role, organizational environment, human resource management and knowledge of the patient. Conclusions: PCMDM is a continuous, ethical, and reflective process mediated by MNMs, who reconcile institutional priorities, team dynamics, and patient needs to create conditions for high-quality PCC. Implications for Practice: Strengthening PCMDM requires coordinated action aimed at equipping nurse managers with advanced leadership capabilities, building organizational structures that sustain patient-centered decisions, and empowering patients to actively co-shape the care process. Full article

Breast cancer is a significant public health concern, with triple-negative breast cancer (TNBC) being the most aggressive subtype characterized by considerable heterogeneity and the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. Currently, there are no practical alternatives to chemotherapy, which is associated with a poor prognosis. Therefore, developing new treatments for TNBC is an urgent need. Reactive oxygen species (ROS) and redox adaptation play central roles in TNBC biology. Targeting the redox state has emerged as a promising therapeutic approach, as it is vital to the survival of tumors, including TNBC. Although TNBC does not produce high levels of ROS compared to ER- or PR-positive breast cancers, it relies on mitochondria and oxidative phosphorylation (OXPHOS) to sustain ROS production and create an environment conducive to tumor progression. As a result, novel treatments that can modulate redox balance and target organelles essential for redox homeostasis, such as mitochondria, could be promising for TNBC—an area not yet reviewed in the current scientific literature, thus representing a critical gap. This review addresses that gap by synthesizing current evidence on TNBC biology and its connections to redox state and mitochondrial metabolism, with a focus on innovative strategies such as metal-based compounds (e.g., copper, gold), redox nanoparticles that facilitate anticancer drug delivery, mitochondrial-targeted therapies, and immunomodulatory peptides like GK-1. By integrating mechanistic insights into the redox state with emerging therapeutic approaches, I aim to highlight new redox-centered opportunities to improve TNBC treatments. Moreover, this review uniquely integrates mitochondrial metabolism, redox imbalance, and emerging regulated cell-death pathways, including ferroptosis, cuproptosis, and disulfidptosis, within the context of TNBC metabolic heterogeneity, highlighting translational vulnerabilities and subtype-specific therapeutic opportunities. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Minimum wage is an important tool for reducing income inequality and supporting social welfare. Consequently, governments around the world have established minimum wage systems. As such, minimum wage policies connect distributive justice with the economy’s capacity to sustain broad-based welfare over time, placing the equity–efficiency trade-off at the center of societal sustainability. However, the micro-level impact of the minimum wage system on firms has always been an important topic for scholars. This study uses panel data from listed Chinese manufacturing firms over a period from 2005 to 2021 to construct an indicator of the minimum wage standards implemented in the firm locations. Employing the multiple linear regression model, this paper empirically examines the effects of minimum wage on labor productivity. The empirical findings demonstrate that minimum wage significantly reduced the sample firms’ labor productivity. Moreover, the negative impact of the minimum wage was primarily concentrated among non-state-owned firms, labor-intensive firms, firms operating in industries characterized by intense product market competition, firms situated in regions with strong legal protections, firms with comparatively low average employee wages, and export-oriented firms. Subsequently, this study delves into the mechanism through which minimum wage negatively affects labor productivity. We find that implementation of minimum wage leads to a reduction in corporate investment, indicating that there is no significant substitution relationship between capital and labor. These adjustment margins provide microfoundations through which statutory wage floors can influence the resilience and inclusiveness of development, indicating that the pace and design of wage increases should balance income protection with the preservation of productive capacity to support sustainable human development—grounded in steady productivity growth, equitable income distribution, and stable firm investment. Our findings contribute to a better understanding of the mechanism through which minimum wage affects labor productivity in theory, while concurrently furnishing policy insights for the optimization of the minimum wage system and maintaining sustainable societal development in practice. Full article

Although metaverse model houses have recently emerged as an interactive alternative to traditional housing marketing tools, empirical research addressing users’ experiences within these environments remains limited. This study aimed to examine how three dimensions of user experience (UX)—operational, sensory, and exploratory—affect user satisfaction and behavioral intentions in metaverse model houses. A total of 83 participants explored a metaverse model house using a tablet PC and completed a questionnaire. Multiple linear regression analysis revealed that exploratory experience significantly influenced user satisfaction, while sensory experience was positively associated with all behavioral intentions, including the intention to revisit, recommend, reside, and purchase. These findings advance our understanding of UX in virtual housing environments and highlight the importance of immersive and exploratory elements in designing effective metaverse model houses. The results offer practical implications for improving digital housing marketing strategies and guiding the future development of metaverse-based architectural platforms. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Background/Objectives: Evaluating the feasibility of randomized controlled trials (RCTs) represents a critical next step for advancing human–animal interaction (HAI) science and rigorously exploring the role of animal-assisted interventions (AAIs) in psychiatric acute care. This study presents strategies for conducting a pilot RCT comparing an animal-assisted intervention involving dogs (AAI) with an active conversational control (CC), which incorporated conversation with a human volunteer, and treatment as usual (TU) for improving mental health outcomes in psychiatrically hospitalized adults. Methods: We recruited participants from an acute-care psychiatric unit at an academic medical center. AAI and CC were delivered by volunteer handlers with and without their registered therapy dogs. Feasibility data included number of recruitment contacts, recruitment rate, and reasons for non-enrollment. We describe recruitment challenges encountered and mitigating strategies for successful study completion. Results: Recruitment occurred over 23 months with a goal of 60 participants participating in at least one intervention day. A total of 264 patients were referred to the study and 72 enrolled. The additional 12 people were recruited to replace participants who did not complete any intervention days and did not provide any intervention data. Study recruitment goals were met with a recruitment rate of 27.30%. Conclusions: Research to improve the lives of patients in acute psychiatric care is a vital public health goal, yet RCTs are difficult to conduct in acute care settings. Studies like this strengthen HAI and psychiatric science by providing a roadmap for implementing successful AAI RCT designs. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

Employee satisfaction remains a central theme in management, with substantial evidence linking it to organizational performance. This study examines the nature and strength of that relationship and investigates whether its magnitude varies across different performance indicators. Using existing data from 23 organizations where organization culture was assessed through the Denison Organizational Culture Survey (DOCS) between 2015 and 2022, the study analyzes 1532 employee responses. Organizational performance was evaluated across five dimensions: growth, profitability, quality of products and services, employee satisfaction, customer satisfaction, and overall performance. Bivariate regression analyses reveal positive and statistically significant relationships between employee satisfaction and all other performance indicators. The strongest associations were observed for overall performance and customer satisfaction, while moderate relationships emerged for profitability, growth, and quality of products and services. Employee satisfaction accounted for approximately 36% of the variance in overall performance. The findings support the view that employee satisfaction functions both as a driver and as an outcome of organizational performance. They further indicate that the strength of this relationship is greater for human-centered outcomes than for financial indicators. Practically, the results underscore the importance of cultivating intrinsic motivation, trust, and employee participation to enhance both satisfaction and perceived performance. Future research should investigate the causal direction between employee satisfaction and customer satisfaction and explore how organizational culture moderates this relationship across different sectors. Full article

Sustainability in the context of machine learning (ML) plays an important role for accessible models by both researchers as well as clinicians. This article describes a reproducibility study on PiDeeL, a metabolic-pathway-informed deep learning model. It serves to test the hypothesis that the requirement of a simple provision of all digital artifacts is not sufficient to reproduce the computational experiment(s). The reproduction and modification of the computational model foundational to the previous findings shall promote documentation and evaluation of existing scientific models and confirm their applicability. The modification of the original model is based on measuring emissions of training machine learning models using CodeCarbon. Two different systems with different CPU as well as GPU specifications and Windows Subsystem Linux could be tested after guide and code adaptions due to initial incomplete replication attempts given the threshold of computation completion without error message(s). Emissions equivalent to 0.3–0.6 kg of CO₂ per run were shown. Encountered issues along the replication attempts call for refined guidelines on documentation and processing of computational approaches in scientific studies by publishers as well as the scientific community. Thorough peer review including algorithmic reproduction would be necessary to ensure model reusability. Full article

Objective: The objective of this study is to synthesize and critically appraise how artificial intelligence (AI) is being integrated into oral and maxillofacial surgery (OMFS). This review’s novel contribution is to jointly map clinical applications (diagnostics, virtual surgical planning, intraoperative guidance) and operational uses (triage, scheduling, documentation, patient communication), quantifying evidence and validation status to provide practice-oriented guidance for adoption. Study Design: A narrative review of the recent literature and expert analysis, supplemented by illustrative multicenter implementation data from OMFS practice, was carried out. Results: AI demonstrates high performance in radiographic analysis and virtual planning (up to 96% predictive accuracy and sub-millimeter soft-tissue simulation error), with clinical reports of shorter planning times and more efficient patient communication. Early deployments in OMFS clinics have increased appointment bookings, while maintaining high patient satisfaction, and reduced the administrative burden. Remaining challenges include data quality, explainability, and limited multicenter and pediatric validation, which constrain generalizability and require clinician oversight. Conclusions: AI offers substantive benefits across the OMFS care continuum—improving diagnostic accuracy, surgical planning, and patient engagement while streamlining workflows. Responsible adoption depends on transparent validation, data governance, and targeted training, with attention to cost-effectiveness. Immediate priorities include standardized reporting of quantitative outcomes (e.g., sensitivity, specificity, time saved) and prospective multicenter studies, ensuring that AI augments—rather than replaces—human-centered care. Full article

Background/Objectives: Caregivers of infants with congenital muscular torticollis (CMT) frequently seek information online, although the accuracy, clarity, and safety of web-based content remain variable. As large language models (LLMs) are increasingly used as health information tools, their reliability for caregiver education requires systematic evaluation. This study aimed to assess the reproducibility and quality of ChatGPT-5.1 responses to caregiver-centered questions regarding CMT. Methods: A set of 17 questions was developed through a Delphi process involving clinicians and caregivers to ensure relevance and comprehensiveness. ChatGPT generated responses in two independent sessions. Reproducibility was assessed using TF–IDF cosine similarity and embedding-based semantic similarity. Ten clinical experts evaluated each response for accuracy, readability, safety, and overall quality using a 4-point Likert scale. Results: ChatGPT demonstrated moderate lexical consistency (mean TF–IDF similarity 0.75) and high semantic stability (mean embedding similarity 0.92). Expert ratings indicated moderate to good performance across domains, with mean scores of 3.0 for accuracy, 3.6 for readability, 3.1 for safety, and 3.1 for overall quality. However, several responses exhibited deficiencies, particularly due to omission of key cautions, oversimplification, or insufficient clinical detail. Conclusions: While ChatGPT provides fluent and generally accurate information about CMT, the observed variability across topics underscores the importance of human oversight and content refinement prior to integration into caregiver-facing educational materials. Full article

High-density urban villages pose significant environmental challenges to the aging population. Beyond traditional exposures such as noise and air pollution, older adults may experience heightened physiological stress due to visual exposure within street environments, yet the precise micro-environmental triggers of physiological stress remain poorly understood. This study investigates how street-level visual elements relate to elderly walkers’ physiological stress. We conducted on-site walking experiments and monitored the Skin Conductance Level (SCL) of 81 elderly participants walking through two typical urban villages in Lingnan, China. We used a semantic segmentation algorithm to quantify visual environmental elements from first-person-view images and employed a CatBoost (Categorical Boosting) model to predict stress levels. The explainable model (SHAP, SHapley Additive exPlanations) was then used to interpret the complex relationships. The model demonstrated strong predictive power (e.g., R² = 0.72). SHAP analysis revealed roads and sidewalks as the most dominant predictors of SCL changes, exhibiting significant non-linear effects. Their influence surpassed that of environmental aesthetics like vegetation, which showed a more complex, at times even negative, association with stress reduction. The presence of buildings also exhibited a stress-reducing effect, though less so than roads and sidewalks. Key findings revealed the following: (1) Foundational walking infrastructure is the primary determinant of physiological well-being for elderly pedestrians in high-density environments. (2) The stress-reducing effects of vegetation are context-dependent, while buildings function as a form of “social infrastructure” in mitigating stress. Our findings provide crucial, evidence-based guidance for prioritizing interventions in age-friendly urban renewal projects. Our framework offers a transferable tool for human-centered environmental assessment. Full article