Search Results (788)

As a major metabolic abnormality following hyperglycemia, hypertension, and hyperlipidemia, hyperuricemia has emerged as a significant global public health issue. The pathological mechanisms of hyperuricemia are complex; it not only directly triggers gout but is also closely associated with various chronic diseases, such as cardiovascular disease, diabetes, and chronic kidney disease, posing a systemic threat to individual health. This article systematically reviews the epidemiological characteristics, pathophysiological mechanisms, clinical consequences, and related risk factors of hyperuricemia, and especially focuses on the research advances and mechanisms of comprehensive intervention strategies, including diet, exercise, pharmacotherapy, and lifestyle modifications. Dietary interventions primarily function by regulating the activity of enzymes and transporters related to uric acid metabolism, ameliorating gut microbiota dysbiosis, and alleviating inflammatory responses. Exercise interventions synergistically improve uric acid homeostasis through multiple mechanisms, including the regulation of purine metabolic enzyme activity and the improvements of body composition, insulin resistance, and oxidative stress. Pharmacotherapy, serving as a core measure for patients with moderate-to-severe conditions, directly lowers serum uric acid levels by inhibiting uric acid production or promoting excretion. Although various intervention modalities exhibit distinct effects in regulating uric acid production, promoting excretion, and improving the metabolic-inflammatory environment, challenges such as significant heterogeneity in individual response and uncertainty regarding long-term efficacy remain prevalent. Furthermore, given the increasing trend toward a younger onset of hyperuricemia, prevention and control strategies targeting children and adolescents require urgent reinforcement. Future efforts should focus on conducting multi-center, large-sample clinical studies with clear mechanisms and establishing individualized health management plans based on population characteristics, thereby promoting the precise prevention and treatment of hyperuricemia. Full article

Subsistence family farming in Peru is increasingly constrained by ecosystem degradation, climate variability, and limited access to productive services, particularly where environmental exposure is high. This study develops an Agro-productive and Territorial Vulnerability Index (IVAPT) to evaluate environmental, ecosystem, and socioeconomic vulnerability of subsistence agriculture at the district level nationwide. The index integrates district-level agricultural survey data (ENA-2024) with multi-temporal MODIS NDVI series (2000–2024) and comprehensive climatic, topographic, land-cover, and accessibility indicators, processed through multivariate statistics. Three objective weighting schemes (ENTROPY, CRITIC, PCA) construct thematic sub-indices of Environmental Exposure (EnvExp), Ecosystem Condition (EcoCond), and Socioeconomic Capacity (SocioCap). Results show more than half of Peru’s 1552 districts fall within moderate to very high vulnerability, with highest concentration in the Amazon region (Loreto, Ucayali, Madre de Dios), Andean-Amazonian transitions, and highland districts (Huancavelica, Apurímac, Ayacucho, Puno) where biophysical constraints, ecosystem pressure, and socioeconomic isolation converge. Dimensional spatial complementarity EnvExp peaking on coast, EcoCond in Amazon, SocioCap in Andes demonstrates effective vulnerability reduction requires dimension-specific interventions. Despite divergent weighting schemes, spatial patterns remained consistent, validating identified hotspots. IVAPT provides a reproducible framework supporting evidence-based territorial planning and targeted investments in water infrastructure, ecosystem restoration, and climate adaptation. Full article

Background: Patellofemoral pain (PFP) is a prevalent overuse injury among recreational cyclists worldwide. Despite its ubiquity, little is known about the lived experiences of people with PFP, especially in Saudi Arabia, where healthcare and cultural factors may have a specific impact on how the condition is managed. The aim of this study was to explore the lived experiences of recreational cyclists with patellofemoral pain in Al Madinah, Saudi Arabia. Method: A qualitative, descriptive design using reflexive thematic analysis was employed. Eleven male recreational cyclists aged 28–44 years diagnosed with PFP were purposely recruited from Al Madinah Physical Therapy Centre. Female participants were excluded due to cultural constraints regarding sports participation. The participants consented to participate in the study and to be audio recorded. Data were collected through semi-structured interviews using an interview guide. The interview data were transcribed verbatim and thematically analysed using Atlas.ti, version 24. Results: The thematic analysis revealed six themes highlighting the multidimensional impact of PFP. The participants described localised mechanical impairment with rapid onset during activity and persistent symptoms lasting up to two weeks. Pain was exacerbated by eccentric loading and cycling-specific stressors, such as uphill riding, leading to significant anxiety and avoidance behaviours. To maintain activity, these cyclists employed adaptive strategies, including bike modifications and self-management. Notably, PFP imposed substantial cultural and social burdens, hindering spiritual practices, specifically Salah (prayer) postures, professional duties, and family caregiving. While the participants demonstrated resourcefulness through a hybrid of physiotherapy and independent research, pharmacological relief was viewed as a transient solution. Conclusions: Patellofemoral pain imposes significant multidimensional burdens on recreational cyclists in Al Madinah, which are exacerbated by cultural practices. Physiotherapy offers targeted interventions for pain relief, functional restoration, and participation enhancement, necessitating the need for culturally sensitive management programmes. Full article

Background/Objectives: This study aimed to compare clinical and psychosocial characteristics of substance-using adolescents in Türkiye with and without a history of suicide attempts to identify distinguishing risk factors. Methods: A retrospective analysis was conducted using medical records from 140 adolescents (aged 13–18) treated at a specialized Child and Adolescent Substance Use Center in Türkiye between March 2023 and February 2025. Sociodemographic, clinical, and substance use data were collected. Group comparisons were performed using chi-square and t-tests. Logistic regression was used to identify predictors of suicide attempt history. Results: Among the sample, 52 adolescents (37.1%) had a history of suicide attempts. Compared to their counterparts, the substance-using adolescents were more likely to be female (73.1%) and have histories of psychiatric hospitalization, institutional care, criminal behavior, and polysubstance use (p < 0.05). High-frequency use (≥3 days/week) of methamphetamine, stimulants, cannabinoids, and alcohol was significantly more common in this group (all p < 0.01). The Addiction Profile Index–Adolescent form (API-A) scores indicated more severe addiction profiles. Using cross-validated LASSO and confirmatory logistic regression, female gender emerged as the only robust independent predictor of suicide attempt history (OR = 6.84). Conclusions: Adolescents with a history of suicide attempts exhibit more severe substance use, particularly involving cannabinoids, and greater psychosocial adversity. This distinct risk profile underscores the need for early, gender-sensitive, and multidimensional interventions. Full article

Background: Moral development (MD) arises from the interaction of attachment, temperament, emotion regulation, and decision-making. Children and adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD) frequently show impairments across these domains, suggesting increased vulnerability to disruptions in MD. However, the mechanisms linking ADHD to MD remain poorly understood. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. PubMed was searched for studies published between January 2014 and November 2024 examining MD-related constructs, including moral reasoning, fairness, aggression, bullying, callous–unemotional (CU) traits, decision-making, and reward sensitivity, in individuals aged 0–18 years with diagnosed or subclinical ADHD. Due to substantial heterogeneity in study design, measures, and outcomes, a qualitative synthesis was performed. Results: Of the 2104 records identified, 23 studies met inclusion criteria. Insecure or disorganized attachment, difficult temperament, and emotional dysregulation consistently emerged as developmental risk factors for impaired MD. Hyperactivity–impulsivity and deficient inhibitory control were strongly associated with aggressive and antisocial behaviors. Children with ADHD demonstrated a pronounced preference for immediate over delayed rewards, altered decision-making in social contexts, and reduced sensitivity to positive feedback. CU traits and aggression were frequently identified as behavioral correlates of MD impairments, particularly in interaction with family adversity and comorbid externalizing conditions. Social dysfunction, including bullying involvement, peer rejection, and interpersonal difficulties, was common and contributed to elevated long-term psychosocial risk. Conclusions: ADHD is associated with multidimensional vulnerabilities in MD through intertwined cognitive, emotional, and relational pathways. Interventions targeting attachment security, emotion regulation, reward processing, and social skills may foster MD and reduce later social difficulties. Longitudinal and cross-cultural research is needed to clarify causal mechanisms and inform developmentally sensitive prevention and treatment strategies. Full article

This study examines how investment efficiency and risk jointly shape sustainable grain supply-chain upgrading. Using firm-level panel data for 25 listed grain supply-chain firms in China from 2015 to 2023, this study examines efficiency–risk structures and their heterogeneity across upstream, midstream, and downstream segments. A three-stage data envelopment analysis (DEA) is applied to measure investment efficiency while controlling for environmental heterogeneity and statistical noise, and a multidimensional investment risk index is constructed using principal component analysis (PCA), with an emphasis on sustainability metrics. The results reveal a clear supply-chain gradient: downstream firms exhibit the highest mean third-stage investment efficiency (crete = 0.633) and scale efficiency (scale = 0.634), midstream firms are intermediate (crete = 0.308; scale = 0.326), and upstream firms remain lowest (crete = 0.129; scale = 0.138). This ordering is also visible year by year, while risk profiles indicate higher exposure upstream and pronounced volatility midstream. Efficiency decomposition shows that upstream inefficiency is mainly driven by scale inefficiency rather than insufficient pure technical efficiency. Overall, efficiency–risk mismatch—manifested as persistent low scale efficiency and elevated risk exposure in upstream, volatility in midstream, and stability in downstream—constitutes a key micro-level barrier to long-term and resilient upgrading. The study thus offers policy-relevant insights for segment-specific interventions that align with sustainable agricultural development: facilitating land consolidation and integrated risk management for upstream scale inefficiency, promoting supply-chain finance and digital integration for midstream risk volatility, and leveraging downstream stability to drive coordinated upgrading and sustainable value creation through market-based incentives. Full article

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article

Firefighting requires high and multidimensional fitness to ensure operational readiness and public safety. In Portugal, there is limited data regarding firefighters’ fitness and exercise programs to improve readiness are lacking. This study evaluated the effects of a 6-month minimal-equipment exercise program on the physical fitness of firefighter recruits. Thirty-five male subjects (23.0 ± 2.72 years) were assessed at baseline,3 and 6 months for body composition, handgrip strength, running speed, cardiovascular endurance, anaerobic power, and upper- and lower-body strength. The intervention entailed daily sessions with 15 min of continuous running (50–65% HRmax) and active stretching, followed by alternating routines, including endurance running, free weights, interval sprints, calisthenics, and drills. A repeated-measures ANOVA and Bonferroni adjusted post hoc comparisons identified time-based changes. Significant improvements occurred across all fitness variables. Body fat fell by 8.4% and VO₂max increased (p < 0.001), surpassing occupational thresholds required for extended suppression tasks. Bench press and sit-up performance improved by 88% and 81%, respectively, while countermovement jump showed double-digit gains (13%), all of which can translate directly to hose advancement, victim rescue, and forcible entry. These results highlight that resource-constrained departments can implement effective, low-cost exercise programs for enhancing pivotal fitness components, supporting policy initiatives to include structured training throughout firefighters’ careers. Full article

Unsafe behaviors remain a major contributor to workplace accidents within broader safety-management systems. Acknowledging the essential influence of organizational and leadership factors, this study focuses on systematically identifying and prioritizing individual-level determinants of unsafe behavior through an integrated qualitative–quantitative methodology to clarify their specific role within the wider safety framework. Grounded Theory analysis of semi-structured interviews with 40 industry professionals yielded a conceptual model encompassing demographic characteristics, general health, individual competencies, personality traits, and psychological factors. Subsequently, the Fuzzy Delphi Method, applied with 20 domain experts, validated and ranked these determinants. The analysis highlighted risk perception as the most influential factor, followed by work experience, skill level, knowledge, and risk-taking propensity, whereas variables such as family welfare, substance use, and self-display exhibited relatively minor effects. These findings reveal the multidimensional nature of unsafe behavior and underscore the importance of focusing on high-impact personal attributes to enhance workplace safety. By recognizing that many individual factors are shaped by organizational and psychosocial conditions, the study provides evidence-based insights for developing integrated safety management and targeted intervention strategies in industrial settings. Full article

Background: Food insecurity among college students is a multidimensional challenge shaped by individual, interpersonal, institutional, community, and policy factors. Although many campuses require or provide meal plans, students may experience food insecurity when barriers related to agency (choice and autonomy), utilization (nutrition security), and availability persist. This study explored how undergraduate students at a private, urban U.S. university experience and navigate the multiple dimensions of food insecurity. Methods: We conducted in-depth, semi-structured interviews via Zoom between December 2024 and January 2025 with n = 22 undergraduate students recruited based on food security status, determined by a Fall 2024 longitudinal survey using the USDA Six-Item Short Form. Transcripts were double-coded by trained research assistants in ATLAS.ti using an inductive codebook. Thematic analyses followed a phronetic, iterative approach, organizing findings within a socio-ecological determinants framework and comparing themes by food security status. Results: We identified nine themes across four domains (individual, interpersonal, institutional and community, and political). At the individual level, constrained personal resources for groceries and cooking, time scarcity leading to skipped meals, and health impacts that detracted from academics emerged as key themes. Interpersonally, reliable family financial support was protective and informal support from peers/coaches filled gaps sporadically for some. At the institutional and community level, dining hall hours misaligned with student schedules, perceived limited variety and nutrition quality reduced food agency and utilization, and transportation impeded use of the sole grocery partner accepting university meal plan benefits. Notably, meal plans including unlimited meal swipes provided stable access but did not guarantee food security when food agency and utilization barriers persisted. Many students relied on campus events for free food; formal assistance (e.g., food pantry) was largely underused. At the policy level, Supplemental Nutrition Assistance Program (SNAP) awareness and enrollment was limited among our sample. Conclusions: Meal plan access alone is insufficient to ensure food security. Campus strategies should extend beyond access to prioritize flexibility, variety, and alignment with students’ schedules and preferences, while strengthening communication and eligibility support for external benefits. Future work should design and evaluate interventions that integrate all dimensions of food security and address institutional policies affecting students’ basic needs. Full article

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

Background: Multiple sclerosis (MS) involves complex physical, cognitive and behavioral challenges that collectively affect quality of life. Integrating lifestyle components such as sleep optimization, dietary behaviors, stress management, and self-management strategies into rehabilitation may enhance outcomes beyond traditional approaches. This scoping review aimed to map rehabilitation interventions that combine psychomotor, cognitive, lifestyle-focused, or multimodal elements and assess quality of life in adults with MS. Methods: This scoping review was conducted in accordance with the PRISMA-ScR guidelines, which guided the identification, screening, and selection of studies. Screening and data extraction were conducted independently by two reviewers. From 135 records, nine primary studies and four secondary evidence sources were included. Results: Most studies involved adults with mild-to-moderate disability and predominantly relapsing–remitting multiple sclerosis. Physical or motor rehabilitation interventions were examined in five studies, while two studies evaluated multimodal rehabilitation programs, one study focused on cognitive rehabilitation, and one study investigated lifestyle-oriented or self-management-integrated approaches. Quality of life was assessed in all included studies, with improvements reported across multiple domains. Fatigue-related outcomes were reported in four studies, sleep-related outcomes in three studies, and digitally delivered or hybrid rehabilitation interventions were implemented in three studies, indicating an emerging trend toward technology-supported rehabilitation approaches. Conclusions: Contemporary MS rehabilitation is moving toward multidimensional, holistic models that integrate lifestyle components. Standardized outcomes, inclusion of more diverse MS phenotypes, and rigorous evaluation of integrated frameworks are required to strengthen the evidence base and inform clinical practice. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

Background and aims: Amid global aging, the role of diet in cognitive health is crucial. The healthful plant-based diet index (hPDI) is linked to cardiometabolic benefits, but its association with cognitive function in older adults, particularly through nutritional status and across different socio-geographic contexts, remains unclear. This study investigated the association between hPDI and multidimensional cognitive function, the mediating role of nutritional status, and potential associated modifications by urban–suburban location and gender. Methods: A community-based cross-sectional study was conducted in Shanghai, China, involving 2079 older adults (aged ≥60). Dietary intake was assessed by a validated food frequency questionnaire (FFQ) to calculate hPDI. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-B), and Clinical Dementia Rating (CDR). Nutritional status was measured by the Mini Nutritional Assessment (MNA). Hierarchical regression, interaction, and mediation analyses were performed, adjusting for comprehensive covariates based on social determinants of health (SDoH). Results: Higher hPDI was significantly associated with better cognitive scores (MMSE: β = 0.083, p < 0.001; MoCA-B: β = 0.069, p < 0.001) and lower odds of worse CDR (OR = 0.944, p < 0.001) in fully adjusted models. In the cross-sectional mediation analysis, MNA statistically mediated a significant proportion of the observed associations (MMSE: 41.25%; MoCA-B: 53.68%; CDR: 38.98%). The protective association was consistent across urban and suburban areas. However, a significant three-way interaction (hPDI × Gender × Area, p < 0.01) was found, with no cognitive benefit observed for males in suburban areas. Conclusions: Adherence to a healthful plant-based diet is associated with better cognitive function in older adults, partly statistically mediated by improved nutritional status. While this association is geographically equitable in Shanghai, suburban males do not appear to benefit, highlighting the need for gender- and context-sensitive dietary interventions. Full article

Understanding regional disparities in Chinese modernization is essential for achieving coordinated and sustainable development. This study develops a multi-dimensional evaluation framework, integrating grey relational analysis, entropy weighting, and TOPSIS to assess provincial modernization across China from 2018 to 2023. The framework operationalizes Chinese-style modernization through five dimensions: population quality, economic strength, social development, ecological sustainability, innovation and governance, capturing both material and institutional aspects of development. Using K-Means clustering, kernel density estimation, and convergence analysis, the study examines spatial and temporal patterns of modernization. Results reveal pronounced regional heterogeneity: eastern provinces lead in overall modernization but display internal volatility, central provinces exhibit gradual convergence, and western provinces face widening disparities. Intra-regional analysis highlights uneven development even within geographic clusters, reflecting differential access to resources, governance capacity, and innovation infrastructure. These findings are interpreted through modernization theory, linking observed patterns to governance models, regional development trajectories, and policy coordination. The proposed framework offers a rigorous, data-driven tool for monitoring modernization progress, diagnosing regional bottlenecks, and informing targeted policy interventions. This study demonstrates the methodological value of integrating grey system theory with multi-criteria decision-making and clustering analysis, providing both theoretical insights and practical guidance for advancing balanced and sustainable Chinese-style modernization. Full article