Search Results (5,578)

(1) Background: Menstruation is subject to stigma worldwide, which has led to restrictive cultural norms and taboos rooted in religion, customs, and patriarchal systems. The resulting ‘cultural stigma’ associated with menstruation exacerbates health inequities, restricts access to sexual and reproductive health rights (SRHRs), and undermines girls’ and women’s participation in educational, economic, social, and spiritual activities. This scoping review examines interventions to address menstrual stigma experienced by girls and women in India (2) Methods: We used the Joanna Briggs Institute (JBI) methodology for scoping reviews. After systematic searches on 14 March 2024 across six databases (Academic Search complete, APA PsycInfo, Womens Studies International, Web of Science Core collection, MEDLINE, and Index Medicus-SEAR), we screened 1323 records. (3) Results: Findings from 13 unique study reports reveal diverse approaches to addressing menstrual stigma, including income generation initiatives, sexual education, peer training, technological tools, and arts-based approaches. While the interventions initiated dialogue among girls and women in India, they often lacked broader community engagement, leaving structurally embedded patriarchal norms unchallenged. Additionally, most programs targeted adolescent schoolgirls, with limited attention to waged girls and adult women. (4) Conclusions: Addressing menstrual stigma is critical to advancing gender equality and health equity in India. More research is needed to understand effective ways to galvanize community-wide support in dismantling the deeply rooted patriarchal structures that shape interconnected stigma processes leading to health inequities among girls and women in India. Full article

The content and career cluster of agriculture, food, and natural resources (AFNR) provides opportunities for K-12 teachers to engage students to solve complex authentic problems that blend science, technology, engineering, and mathematics (STEM), yet limited research has been conducted on how to effectively leverage teaching and learning to integrate STEM using the context of AFNR through the school-based agricultural education program. This conceptual paper was developed through a collaborative sensemaking process focused on systems thinking as a way of knowing to integrate STEM within the contexts of AFNR, utilizing the SBAE program in the United States. A comprehensive career and technical education (CTE) program model of SBAE develops secondary education students’ career readiness skills through classroom and laboratory instruction, leadership development, and supervised agricultural experiences. The literature was reviewed to describe the current status of integrated STEM in SBAE, including learning by doing, solving real-world problems, application of content knowledge in out-of-school and community-based settings, learner-centered pedagogies, and development of career readiness skills for the workforce. By employing systems thinking as the theoretical framework and integrated STEM as a conceptual framework, the authors engaged in collaborative sensemaking of their professional and scholarly experiences and proposed findings and discussion of a three-model framework (i.e., teacher, program, and learning approach) to support integrated STEM education through AFNR and SBAE. Limitations of the framework are also discussed. The AFNR career cluster was used as the context to discuss how the three-model framework (i.e., teacher, program, and learning approach) of integrated STEM through AFNR could be operationalized for SBAE. Discussion and implications of the three-model framework for other career clusters in career and technical education (CTE) and non-formal education in community settings are presented. Conclusions and recommendations are provided for advancing STEM integration in SBAE for teacher development, program development, and research. Full article

Acute kidney injury (AKI) is a systemic syndrome capable of inducing remote organ dysfunction. Kidney–lung crosstalk is a form of interorgan communication in acute nephrology, with the heart acting as a pivotal intermediary. Emerging evidence supports the involvement of a gut–lung–kidney axis. Complement activation in these multiorgan crosstalk has emerged as a central amplifier of multiorgan damage. We reviewed the literature on kidney–lung interactions and complement activation in AKI through a bibliographic search of PubMed, Scopus, and Web of Science. Most available data derive from experimental studies or intensive care unit (ICU) populations, often reported in reviews. We further report our real-world experience in a non-ICU nephrology setting, including 186 consecutive patients with AKI. Pulmonary involvement was present at hospital admission in 118 patients (63%). AKI stage 1 was observed in 20/118 patients (17%) with pulmonary involvement compared with 18/68 patients (27%) without pulmonary involvement (p < 0.001). In conclusion, AKI should be regarded as a systemic disease from its earliest stages. Kidney–lung interactions are clinically relevant even in mild AKI and outside critical care settings, underscoring the need for integrated organ assessment in routine nephrology practice. This review integrates complement activation as a central amplifier of kidney–lung crosstalk and multiorgan dysfunction, bridging experimental evidence with real-world observations from a non-critical care AKI population. By focusing on early AKI stages and the timing of pulmonary involvement, we highlight AKI as an active driver of systemic organ interactions rather than a late consequence of critical illness. Full article

Background: Non-suicidal self-injury (NSSI) is defined as the intentional damage to one’s body tissue without suicidal intent and for reasons that are not socially sanctioned. While NSSI has been widely studied in the general population, its clinical correlates and management in autism spectrum disorder (ASD) remain less clearly characterized, and it is often conflated with self-injurious behavior (SIB) described within restricted and repetitive behaviors (RRBs). In individuals with ASD, NSSI may be associated with emotional, behavioral, cognitive, social, medical, and demographic factors, and it differs from SIB typically observed among individuals with severe intellectual disabilities. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Studies published between 2000 and 2025 that assessed NSSI in individuals with a formal ASD diagnosis were included. Etiological/clinical correlates, explanatory mechanisms, and management challenges were examined. Sixteen studies were selected from PubMed, Scopus, and Web of Science. Findings were synthesized using narrative and thematic approaches. Results: The prevalence of NSSI among individuals with ASD ranged from 24% to 50%. Associated factors included emotion dysregulation (including alexithymia and affective distress), behavioral dysregulation (e.g., impulsivity/hyperactivity and aggression), sensory processing difficulties, communication and social impairments, and medical comorbidities (i.e., gastrointestinal and sleep problems), with preliminary evidence also implicating perinatal factors. NSSI was linked to emotion regulation, sensation seeking, and social communication processes. Early intervention and parental involvement were identified as protective factors. Conclusions: NSSI in ASD is a complex, multifactorial phenomenon frequently linked to emotion-regulation needs. Affective imbalance represents a central—though not exclusive—pathway. The review supports standardized terminology, function-based assessment, and clearer differentiation from SIB/RRBs, with implications for individualized interventions and sustained monitoring in persistent or severe cases. Routine screening for medical and sensory contributors may further improve case management and reduce preventable clinical burden. Full article

Following the COVID-19 pandemic, researchers have increasingly focused on monitoring the spread of the virus and improving methods to detect changes in the SARS-CoV-2 genome. Although clinical surveillance provides direct and reliable results, it has limited applicability. Wastewater-based epidemiology (WBE) has therefore emerged as a valuable, non-invasive complementary tool for disease surveillance. It provides a comprehensive picture of virus circulation in a population, including asymptomatic individuals and those who do not seek healthcare. In addition, it facilitates early detection of outbreaks and the collection of epidemiologic data at the community level. However, WBE also presents technical challenges, including variations in sampling and testing protocols, the presence of inhibitors that affect viral RNA extraction, and the need for standardised procedures between studies. These challenges should be addressed for possible future infectious disease outbreaks. One of the challenges facing researchers was to develop efficient methods that could overcome the extraction and detection problems related to inhibitors present in wastewater. To this aim, this systematic review highlights the potential use of WBE, the variety of techniques, and the most effective methods for the detection and quantification of SARS-CoV-2 in wastewater samples. A reproducible electronic search of the literature was conducted in the Web of Science (WoS) and PubMed databases for articles published between 2020 and 2024. Our search revealed that the majority of observed WBE applications emphasised a correlation between SARS-CoV-2 RNA concentration trends in wastewater and epidemiological data. Another relevant issue that the articles often discussed and compared was the techniques used in different steps of sample processing, such as sample collection, concentration and detection, hence the lack of standardised procedures. This paper provides a framework regarding previous research on WBE to gain a better understanding that will lead to functional solutions. Full article

Corporate communication of sustainability within the fashion industry operates in a sector with high reputational exposure and increasing demands for environmental and social accountability. Despite the growing volume of research, the field remains conceptually and methodologically dispersed, with a predominant focus on discourse and limited emphasis on verification and structural integration. This study presents a systematic review of 80 peer-reviewed articles published between 2015 and 2025 in Scopus and Web of Science, examining how sustainability communication in the fashion industry has been conceptualised, investigated, and operationalised across the literature. Following the PRISMA protocol and employing a mixed-method approach combining bibliometric and content analyses, four thematic lines were identified: (1) corporate communication and reputation; (2) digital communication and social media; (3) CSR and sustainability; (4) transparency and greenwashing. Keyword co-occurrence and conceptual clusters were mapped using VOSviewer. Results reveal a predominance of content analysis, case studies, and corporate narratives, with fewer quantitative and mixed-method designs. Research largely focuses on discourse interpretation and credibility-building rather than on empirically verifying sustainability commitments. Thematic developments indicate a shift from general CSR frameworks toward transparency, digital traceability, and social media communication. Key gaps persist in message authenticity, greenwashing evaluation, and communicating public sustainability funds, including Next Generation EU programs. Overall, the review portrays an expanding yet fragmented field in which sustainability communication operates primarily as a reputational mechanism. Methodologically, the study combines a PRISMA-guided systematic literature review with bibliometric mapping techniques to support thematic synthesis and field contextualisation. Full article

Drawing on digital agency theory, expectancy–value frameworks, and self-regulated learning perspectives, this study proposes and tests a moderated mediation model explaining students’ intentions to use AI. Using data from 673 university students, we examined whether sense of positive agency (SOPA) predicts intention to use AI indirectly through perceived value and perceived benefits of AI, and whether these pathways are conditionally shaped by sense of negative agency (SONA). Conditional process analysis (PROCESS Model 59) showed that SOPA had no direct effect on intention to use AI (b = 0.013, p = 0.882). Instead, its influence was fully indirect and conditional. SOPA predicted perceived value and perceived benefits of AI only at moderate to high levels of SONA, with significant SOPA × SONA interactions for both mediators (p = 0.040). Perceived value strongly predicted intention to use AI (b = 0.385, p < 0.001), and this relationship was amplified at higher levels of negative agency (b = 0.138, p = 0.002). In contrast, the effect of perceived benefits on intention weakened as SONA increased (b = −0.125, p = 0.005), becoming non-significant at higher levels of negative agency (Johnson–Neyman point ≈ 2.99). The final model explained 50.4% of the variance in intention to use AI. Overall, the findings indicate a conditional appraisal mechanism: as negative agency increases, perceived value becomes a stronger predictor of intention, whereas the motivational contribution of perceived benefits weakens and becomes non-significant beyond the Johnson–Neyman threshold. These results support an agency-aware account of AI adoption focused on how cognitive appraisals relate to intention under different perceived agency orientations, without implying ethical reasoning or moral deliberation processes not measured in this study. Full article

Inclusive AI-mediated mathematics education for students with learning difficulties refers to a human-centered approach to mathematics teaching and learning that uses artificial intelligence (AI), adaptive technologies, and data-rich environments to support learners who experience persistent challenges in mathematics. These challenges may take the form of a formally identified developmental learning disorder with impairment in mathematics, broader learning difficulties, low and unstable achievement, irregular engagement, or heightened mathematics anxiety that places students at risk of disengagement and poor long-term outcomes. This approach integrates early screening, personalized instruction, and affect-aware support to address both cognitive difficulties and the emotional burden associated with mathematics anxiety. Situated within digitally augmented schools, homes, and community spaces typical of smart cities, it seeks to reduce stress and anxiety, prevent the reproduction of educational inequalities, and promote equitable participation in science, technology, engineering, and mathematics (STEM) pathways. It emphasizes Universal Design for Learning (UDL), ethical and transparent use of learner data, and sustained collaboration among teachers, families, technologists, urban planners, and policy-makers across micro (individual), meso (school and community), and macro (urban and policy) levels. Crucially, AI functions as decision support rather than replacement of pedagogical judgment, with teachers maintaining human-in-the-loop oversight and responsibility for inclusive instructional decisions. Where learner data include fine-grained logs or affect-related indicators, data minimization, clear purpose limitation, and child- and family-friendly transparency are essential. Implementation should also consider feasibility and sustainability, including staff capacity and resource constraints, so that inclusive benefits do not depend on high-cost infrastructures. Full article

In this work, we present a Temporal Graph Neural Network (TGNN) architecture specifically designed for link prediction in dynamic graphs. The proposed approach is evaluated on a dynamic social network constructed from internal email communication between employees of Wrocław University of Science and Technology that was collected over a continuous period of 605 days. To capture short-term fluctuations in communication behavior, we introduce the use of very short temporal aggregation windows, down to a single day, for constructing temporal graph snapshots. This fine-grained temporal resolution allows the model to accurately learn evolving interaction patterns and adapt to the dynamic nature of social communication networks. The TGNN model demonstrates consistently high predictive performance, achieving 99.28% ROC-AUC (Receiver Operating Characteristic—Area Under Curve) and 99.17% Average Precision in link prediction tasks. These results confirm that the model is able to distinguish between existing and emerging communication links with high reliability across temporal intervals. The architecture, optimized exclusively for temporal link prediction, effectively utilizes its representational capacity for modeling edge formation processes in time-dependent networks. The findings highlight the potential of focused TGNN architectures and short-time-window modeling in improving predictive accuracy and temporal resolution in link prediction applications involving evolving social or organizational structures. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

An emerging paradigm in public health focuses on enhancing nutrition in existing food staples to reduce chronic disease at the population scale, rather than relying on individuals to change their behavior. This paradigm leverages plant and animal breeding, production practices, and processing to enhance nutrition, whereby foods consumed by millions can be improved at low incremental cost. This article supports and operationalizes this paradigm, illustrating the potential to improve diets through a case study that increases the arabinoxylan fiber content of commodity wheat through classical plant breeding (a non-GMO technology). The approach described in this article proposes to link agricultural and food science with health system implementation to deliver equitable access, improved healthcare outcomes and cost savings, and improved community health. Based on published dose–response relationships, comparative risk modeling indicates that modest fiber increases achieved by the commodity wheat breeding led to reduced population-level risks of 1–3% for cardiovascular disease, 3–4.5% for type 2 diabetes, and 1–3.5% for colorectal cancer, translating into substantial healthcare cost savings when implemented at a national scale. This article outlines possible low-risk pathways for implementing these nutrition increases at the population scale through commodity supply chains and community-level nutrition improvement efforts and evaluates the ranges of potential population-level impacts. Full article

Optimizing medication management and improving patient health outcomes depend primarily on the strength of primary healthcare services, where collaboration between general practitioners (GPs) and pharmacists plays a critical role. This scoping review aimed to identify the main facilitators and barriers influencing pharmacist–GP collaboration. The review was conducted in line with PRISMA-ScR guidelines. A comprehensive search of PubMed, Scopus, and Web of Science identified studies published in English between January 2019 and May 2025, of which twenty met the inclusion criteria. Key facilitators of collaboration included pharmacist co-location within GP practices, clearly defined professional responsibilities, access to shared electronic health records, and supportive government policies. Barriers most frequently reported were limited communication pathways, insufficient interprofessional training, and financial constraints. Overall, the findings suggest that effective pharmacist–GP collaboration relies on structural integration, professional trust, and policy initiatives that enable sustained cooperation. Long-term investment in collaborative infrastructure and workforce development will be essential to strengthen primary care, support patient outcomes, and ensure more efficient use of healthcare resources. Full article

Background and Objective: Adolescent bipolar disorder (BD) has substantial symptom overlaps with other psychiatric disorders. Identifying its distinctive candidate neuroimaging markers may be helpful for exploratory early differentiation and to inform future translational studies after independent validation. Methods: This cross-sectional study enrolled adolescents with BD and age- and sex-matched healthy controls. Assessments included clinical/behavioral scales and an emotional Go/NoGo task-based fMRI (Go trials require a response; NoGo trials require response inhibition) acquired across three mood states (depression, mania, and remission) and matched controls. We applied several conventional machine learning classifiers to task-fMRI data to classify BD versus healthy controls and to identify the most relevant neuroimaging predictors. Results: A total of 43 adolescents with BD (15 in remission, 11 with depression, and 17 with mania) and 43 matched healthy controls were included. Under the Go-NoGo condition, activation-derived features in the remission state showed the strongest discrimination, with RF achieving the best performance (accuracy = 94.29%, AUC = 98.57%). These findings suggest that task-evoked functional alterations may remain detectable during remission. In addition, activation patterns in regions within the limbic system, prefrontal cortex, and default mode network were significantly correlated with clinical scales and behavioral measures implicating these regions in emotion regulation and cognitive functioning in adolescents with BD. Conclusion: This study showed that adolescents with BD during remission without manic and depressive symptoms may still have aberrant neural activity in the limbic system, prefrontal cortex, and default mode network, which may serve as a potential candidate neuroimaging signature of adolescent BD. Full article

This article explores the transformative potential of heritage education in promoting rural development along European pilgrimage routes. Drawing on findings from the Horizon 2020 rurAllure project, this study identifies key training and educational needs among cultural and non-cultural stakeholders in rural areas and proposes a strategic agenda for innovation in heritage-led tourism. Using a qualitative and participatory methodology—including a literature review, expert interviews, and pilot experimentation in Spain, Portugal, Italy, Norway, Hungary, Slovakia, and Romania—the research reveals persistent gaps in digital skills, tourism management, and accessibility. The study demonstrates the value of inclusive, place-based, and digitally enhanced learning approaches in strengthening local identity, enhancing community resilience, and addressing territorial disparities. Building on these empirical insights, the article proposes a strategic research and innovation agenda for heritage-led tourism, aligned with European policy priorities, offering actionable recommendations for policymakers and practitioners. Ultimately, the article positions pilgrimage routes as learning landscapes—spaces for educational innovation, cultural sustainability, and territorial cohesion. Full article

This paper examines the critical role of youth engagement in building urban climate resilience in secondary cities of West Africa, with a specific focus on Bo City, Sierra Leone. As one of the world’s most climate-vulnerable countries, Sierra Leone faces significant challenges exacerbated in urban environments where infrastructure gaps, rapid population growth, climate migration, and limited resources intersect with intensifying climate impacts (rising temperature, extreme weather events, rising sea levels, and socio-economic health impacts). We describe a pathway to invest in the adaptive capacity of this community by developing and implementing a Youth Climate Science Hub designed to inform and empower secondary school students as future climate leaders. Drawing on theories of social–ecological resilience and transformative education, we analyze how youth-centered approaches can bridge the knowledge–action gap in urban climate adaptation. The initiative represents an innovative practice-based example for building resilience in secondary cities expected to receive climate migrants while demonstrating the power of youth mobilization in creating locally appropriate climate solutions. Full article