Search Results (3,772)

Skin diseases represent a growing global health challenge and continue to stimulate interest in safe, sustainable, and evidence-based dermatological interventions. In the present review, Cabo Verde is used as an ethnobotanical and biogeographical framework rather than as a source of strictly endemic dermatological plants. We focused on selected edible plant species occurring in and utilized throughout Cabo Verde, including native, naturalized, and cultivated taxa that are integrated into local food systems and traditional healthcare practices. Species were included only when they fulfilled the following criteria: a documented occurrence in Cabo Verde, recognized edible use, available phytochemical characterization, and at least one peer-reviewed study reporting dermatologically relevant biological activity. Literature identified through structured searches of PubMed, Scopus, and Web of Science were critically evaluated, with emphasis placed on phytochemistry, biological activity, safety considerations, evidence level, and translational relevance. The reviewed species, mainly from the genera Psidium, Syzygium, Eugenia, Artocarpus, Ficus, Morus, and Passiflora, have been associated with wound-healing, anti-inflammatory, antioxidant, antimicrobial, photoprotective, anti-aging, and skin-regenerative effects. Nevertheless, the available evidence remains dominated by in vitro and animal studies, whereas controlled human investigations are scarce. Accordingly, these species should be regarded as promising sources of dermatologically relevant compounds rather than as clinically validated dermatological therapies. Full article

Antimicrobial resistance in various microorganisms and opportunistic pathogens associated with HIV/AIDS poses a serious threat to human life and healthcare systems worldwide. Different forms of cancer are likely to arise in immunocompromised patients. The antimicrobial and anticancer effects of methanol extract and fractions from Raphionacme hirsuta have been investigated. The carbon tetrachloride fraction showed a remarkably low minimum inhibitory concentration (MIC) of 0.02 mg/mL against Escherichia coli, Mycoplasma hominis, and Cryptococcus neoformans, while the n-hexane fraction showed a similar MIC against C. neoformans. Furthermore, the carbon tetrachloride fraction exhibited promising IC₅₀ values of 18.21 and 25.22 µg/mL against HeLa and MCF-7 cancerous cell lines, respectively. The fraction was subjected to GC-TOF-MS and yielded four major compounds, including 7,9-Di-tert-butyl-1-oxaspiro (4,5) deca-6,9-diene-2,8-dione (5.322%), Hexanedial (3.691%), 4-(4-tert-Butylphenyl)-1,3-thiazol-2-ylamine (3.329%), and Di-n-decylsulfone (3.201%). These substances could potentially account for the plant species’ initial biological activity, which is why it is necessary to investigate their in vivo actions. The gummy extract had less biological activity than the fractions. To the best of our knowledge, this is the first study to report the antimicrobial and anticancer activities, as well as the phytochemistry, of the plant species. Full article

Non-aeruginosa Pseudomonas (NAP) species represent a diverse and ubiquitous group of Gram-negative bacteria inhabiting a wide range of environmental niches, from soil and water to plant rhizospheres and clinical settings. While Pseudomonas aeruginosa has historically dominated clinical and research focus, the significance of NAP species, such as Pseudomonas fluorescens, Pseudomonas putida, and Pseudomonas stutzeri, as both opportunistic human pathogens and versatile biotechnological agents is increasingly recognized. Their remarkable genomic plasticity, driven by large accessory genomes and mobile genetic elements, underpins their metabolic versatility and adaptability but also facilitates the acquisition of virulence determinants and antibiotic resistance genes, contributing to their emergence in healthcare settings, particularly among immunocompromised individuals. This review provides a comprehensive analysis of NAP species, focusing on recent advances in their taxonomy facilitated by genomic tools like Whole-Genome Sequencing (WGS) and Multilocus Sequence Typing (MLST), which reveal complex species groups and challenge traditional classifications. We delve into the genomic landscape, exploring pangenome dynamics, horizontal gene transfer (HGT), and the genomic signatures that may differentiate clinical from environmental isolates. The clinical relevance of NAPs is examined, detailing the spectrum of infections, epidemiological trends, risk factors, and insights into virulence mechanisms, including secretion systems (T3SS, T6SS) and pathogenicity islands. Addressing a critical need, this review incorporates detailed sections on the diagnostic challenges posed by NAPs, including common misidentifications and the role of modern techniques like MALDI-TOF MS and WGS, and outlines current and novel therapeutic strategies, considering the growing problem of antimicrobial resistance (AMR) within this group. Furthermore, the biotechnological applications of NAPs in bioremediation and biocatalysis are discussed alongside evolving biosafety considerations, reflecting the shift from strict containment to integrated monitoring approaches for genetically engineered strains. By synthesizing current knowledge and highlighting research gaps, this review underscores the necessity of integrated, One Health approaches to understand and manage the dual nature of non-aeruginosa Pseudomonas species as both environmental inhabitants and clinically relevant pathogens. Full article

The U.S. pharmacist workforce has undergone significant growth and demographic changes, particularly increased female participation and part-time employment. However, limited evidence exists on how labor supply behavior varies by gender and employment status. This study used a nationally representative sample of 12,064 pharmacists from the 2019–2022 American Community Survey (ACS) to examine heterogeneity in labor supply decisions. Ordinary Least Squares (OLS) regression models were estimated separately by employment status (full-time versus part-time) and gender. The dependent variable was the number of hours worked. Independent variables included wage rate, age, marital status, number of children, and race/ethnicity. Findings showed that women were more than twice as likely to work part-time as men and minority pharmacists were largely underrepresented in the study sample relative to nonminority pharmacists. Gender gaps in wages, hours worked, and demographic and family characteristics also differed markedly between full-time and part-time pharmacists. Finally, findings lent support to the contention than women are more likely to work fewer hours than men as they are expected to spend more hours at home raising children. Understanding how individual characteristics shape pharmacists’ labor supply, and how these relationships differ by gender and employment status, is essential for workforce planning and healthcare human resources policy. Full article

The electrocardiogram (ECG) is essential for cardiovascular diagnosis but limited by inter-observer variability, low sensitivity for subclinical disease, and labor-intensive telemonitoring analysis. Artificial intelligence (AI), particularly deep learning, addresses these constraints by extracting high-dimensional patterns that correlate with arrhythmias, structural abnormalities, and systemic conditions. This integrative review synthesizes recent advances in AI-enabled ECG, covering technical foundations—including foundation models and validation strategies—and clinical applications, such as arrhythmia detection, structural heart disease identification, and digital biomarker derivation. We discuss emerging trends like self-supervised learning, multimodal integration, generative models, and explainability techniques. Furthermore, we tackle critical challenges regarding generalizability, algorithmic bias, privacy, and regulatory systems. Finally, we outline research priorities, including curated open datasets, and deployment in resource-constrained settings. With stringent validation, transparent governance, and human-centered design, AI-ECG has the potential to enhance cardiovascular diagnostics and clinical outcomes across a variety of healthcare settings. Full article

Background and Objectives: Dengue is emerging as a multifaceted public health challenge that extends beyond traditional vector-borne disease frameworks. Climate change, rapid urbanisation, environmental transformation, global mobility, and digital ecosystems are progressively reshaping transmission dynamics, outbreak patterns, and preparedness needs worldwide. This narrative review aimed to examine dengue from an integrated public health perspective, focusing on climate-sensitive transmission, urban health, surveillance and preparedness, digital epidemiology, artificial intelligence (AI), and health communication. Materials and Methods: A structured narrative review was conducted through targeted literature searches in PubMed, Scopus, and Web of Science between April and May 2026. To this end, a series of separate thematic search strategies were developed to explore the principal conceptual domains addressed in the review. The synthesis was organised around five interconnected preparedness domains: climate change and environmental transformation; urbanisation and urban health; surveillance, vaccination, and integrated preparedness; digital health, artificial intelligence, and mathematical modelling; and health communication and community engagement. The retrieved literature was analysed using a thematic narrative synthesis approach. Results: The retrieved evidence indicated the progressive expansion and redefinition of dengue risk across both endemic and historically non-endemic regions. Climate variability, environmental transformation, rapid urbanisation, and increasing human mobility have emerged as interconnected drivers capable of influencing vector ecology, transmission dynamics, outbreak frequency, and healthcare system vulnerability. Urbanisation has been frequently associated with infrastructural inequalities, environmental degradation, inadequate water and waste management, and territorial conditions favourable to vector proliferation. The extant literature has also placed significant emphasis on the growing importance of integrated surveillance systems and early warning approaches combining epidemiological, environmental, climatic, entomological, and mobility-related data. Digital epidemiology, AI-based predictive models, and digital surveillance tools may contribute to strengthening outbreak forecasting and preparedness capacity, although important limitations related to data quality, interoperability, interpretability, and implementation remain. In parallel, misinformation, risk communication challenges, and digital communication ecosystems emerged as relevant factors influencing public perception, preventive behaviours, institutional trust, and adherence to public health interventions. Conclusions: Dengue is a systems-level public health challenge shaped by climate change, urbanisation, environmental disruption, human mobility, health-system preparedness, and digital ecosystems. Conventional vector-control strategies alone are unlikely to adequately address this growing complexity. Strengthening dengue preparedness should therefore be considered a broader indicator of public health resilience and long-term health-system adaptation. Full article

During aging, skeletal muscle undergoes a decline in mass and strength. This condition, known as sarcopenia, involves many physiological and metabolic impairments, thus representing a healthcare, social, and economic burden. Various pharmacological and non-pharmacological approaches have been explored to counteract sarcopenia; however, no definite treatment has so far been found. The present narrative review summarizes nanotechnology-based strategies designed to promote muscle preservation and functional recovery in aging. Synthetic organic or inorganic nanoconstructs and natural extracellular vesicles have been used as nanocarriers for drug delivery, have been active as intrinsic therapeutic agents, have been employed to build biomimetic nanoscaffolds to sustain muscle regeneration, or have been combined to form hybrid nanosystems with multiple therapeutic functions. These nanotools demonstrated promising results in vitro and in animal models, being able to counteract major factors responsible for sarcopenia, such as oxidative stress, inflammation, mitochondrial dysfunction, increased proteolysis, and impaired stem cell function. However, nanotools have mostly been tested on biological models far from the physiologically aged human muscle. Moreover, limitations still remain to be solved to make these nanotools suitable for regenerative medicine; in particular, the systemic administration requires nanoconstruct functionalization for skeletal muscle targeting, and proper clearance should be ensured to avoid toxicity and immunogenicity related to long-term use. Full article

This article addresses the growing concern that generative artificial intelligence (AI) may replace human expertise in organizations. Instead of asking whether AI should be used, it examines why human judgment rooted in experience cannot be fully replaced by current AI systems and how organizations can work with AI more effectively. Drawing on research from cognitive science, neuroscience, and organizational studies, the paper explains how people use prior experience to interpret context, notice subtle cues, and make sense of ambiguous situations—capabilities that differ fundamentally from how large language models process data. Evidence from recent studies of AI use in hiring, performance management, healthcare, and knowledge work shows recurring problems, including mistakes in unusual cases, missed context, over-reliance on AI recommendations, and reduced visibility of real skill differences among employees. In response, we propose a five-part Human–AI Collaboration Framework designed to help organizations use AI for efficiency while keeping human judgment active and accountable in key Human Resource Management decisions. The analysis shows that AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding. By framing organizations as systems built on accumulated experience, this article offers practical guidance for responsible AI integration and outlines directions for future research on human–AI collaboration. Full article

Background/Objectives: Contemporary healthcare systems face compound challenges (including technological acceleration, demographic aging, rising chronic disease burden, and growing patient expectations) that demand models that are simultaneously efficient, high-quality, and person-centered. Despite a substantial body of research addressing organizational design, human capital management, and clinical competencies, these dimensions have largely been theorized in isolation. This study aims to construct and justify an integrated theoretical framework explaining how organizational models, human capital, and transversal competencies may jointly shape care quality, patient safety, and institutional sustainability in healthcare organizations. Methods: A narrative literature review was conducted, integrating contributions from business economics, healthcare management, organizational psychology, and nursing sciences. This design was selected for its suitability in synthesizing heterogeneous, multidisciplinary knowledge into a coherent conceptual framework, a purpose for which systematic meta-analytic approaches are not appropriate. Sources encompassed 79 references: peer-reviewed journals (PubMed, JSTOR, Google Scholar), institutional reports (WHO, OECD, European Commission, Joint Commission), normative standards (ISO 30414:2018), and Italian regulatory frameworks, spanning foundational twentieth-century contributions through the most recent literature (2025). Results: Four principal findings emerged: (1) healthcare organizations are evolving from rigid hierarchical structures toward flexible, value-based configurations, with the Value-Based Healthcare (VBHC) paradigm redirecting institutional attention from service volume to patient-meaningful outcomes per unit of cost; (2) transversal competencies (communication, empathy, emotional intelligence, teamwork, and transformational leadership) are closely associated with care quality and patient safety, with 70–80% of sentinel events associated with communication failures; (3) human capital, encompassing technical expertise and relational capacity, constitutes the primary lever of competitive advantage in healthcare institutions; and (4) the trajectory from pyramidal toward participatory and self-managed models is supported by international evidence, including the Buurtzorg experience in the Netherlands. Conclusions: The integrated three-pillar framework (combining resource-based theory and dynamic capabilities, Value-Based Healthcare, and evolutionary organizational theory) provides a theoretically grounded basis for understanding how organizational structure, human capital, and transversal competencies are jointly associated with clinical performance. Healthcare institutions should systematically integrate soft-skills training into professional education and invest in participatory organizational structures. Health policy should revise financing mechanisms to incentivize patient-meaningful outcomes over service volumes and support the broader transition toward Value-Based Healthcare models. The Italian SSN is discussed as an illustrative national context rather than as the primary empirical focus of the review. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global public health threat, compromising prevention and treatment of infectious diseases. This narrative review examines AMR as a multifactorial and transnational crisis through epidemiological, One Health, social and bioethical perspectives, and discusses emerging non-antibiotic preventive and therapeutic strategies. Methods: PubMed and Scopus were searched using terms related to AMR, epidemiology, public health, surveillance, One Health, bioethics, equity and alternative therapies. Peer-reviewed medical and public health articles were considered, together with selected reports from international organizations and public health agencies. Results: AMR is driven by inappropriate antibiotic use in human medicine, livestock, aquaculture and agriculture, combined with weaknesses in infection prevention, stewardship, environmental control and surveillance. Epidemiological evidence shows a substantial global burden, marked regional inequalities in resistance patterns, surveillance capacity and policy response, and major consequences, including increased mortality, prolonged hospitalization, rising healthcare costs and disproportionate effects on vulnerable populations. Key bioethical concerns include collective responsibility, equitable access to effective treatment, stewardship, global justice and intergenerational accountability. Emerging non-antibiotic strategies vary in translational maturity: vaccines and selected microbiome-based interventions have preventive or supportive roles in defined settings, bacteriophage therapy is used mainly in compassionate or specialized contexts, and many antimicrobial peptides and nanotechnology-based platforms remain experimental or early translational. Conclusions: AMR requires coordinated global action grounded in One Health, strong public health systems, integrated surveillance, responsible antimicrobial use and sustained innovation. Effective containment must also address social inequalities, ethical stewardship, equitable access to diagnostics and treatment, and responsibility toward future generations. Full article

Background: Equitable allocation of public health budgets across multiple intervention domains remains a major challenge in regional health governance. In Thailand’s Health Region 10, annual healthcare budgets must address diverse health burdens across several provinces, while current planning approaches rely on expert deliberation and historical precedent without systematic exploration of alternative allocation strategies. Public health resource allocation decisions are inherently multi-criteria, integrating health impact, cost-effectiveness, equity, disease severity, clinical and ethical priorities, feasibility, and alignment with national health policy agendas—dimensions that cannot be reduced to a single metric. This study introduces H-RL-MUSYA (Hierarchical Reinforcement Learning for Multi-Domain Unified System of Yielding Adaptive allocations), a decision-support framework designed to assist—not replace—public health practitioners by systematically generating and evaluating a menu of Pareto-efficient allocation strategies across four priority domains: nutrition, mental health, behavioral risk, and accident prevention. The framework explicitly acknowledges that DALYs averted and cost-effectiveness ratios are valuable but partial indicators, and that final resource allocation must integrate additional considerations—including underpinning health policies, priority population needs, feasibility, and contextual judgment—that lie beyond the model’s scope. Results: Applied to Thailand’s Health Region 10 (4.6 million inhabitants), H-RL-MUSYA identified 127 Pareto-efficient policies yielding a representative compromise allocation that averted 847,293 DALYs (34.1% improvement over historical allocations), improved cost-effectiveness by 31.3%, and reduced the health equity Gini coefficient from 0.243 to 0.187. A 12-month prospective pilot confirmed +23.1% composite health improvement with 91% stakeholder acceptance. Conclusions: H-RL-MUSYA demonstrates that AI-assisted policy exploration can meaningfully enrich public health decision-making by surfacing non-intuitive allocation strategies and quantifying equity–efficiency trade-offs, while human expertise, policy context, and democratic deliberation remain essential for final allocation decisions. Full article

Background/Objectives: Sexualized drug use (SDU) and chemsex have emerged as a growing public health concern globally, reflecting complex intersections between sexual practices, psychoactive substance use, and structural vulnerabilities. In Brazil, however, evidence on healthcare access among individuals who engage in SDU/chemsex remains limited and fragmented. This scoping review aimed to map and analyze the available literature on healthcare access in this population, identifying barriers, facilitators, and gaps in care. Methods: The review followed the Arksey and O’Malley framework and Joanna Briggs Institute recommendations, with searches conducted in six databases (MEDLINE/PubMed, Embase, Scopus, SciELO, LILACS, and PsycINFO) for studies published between 2014 and 2025. Results: Eleven studies met the inclusion criteria, predominantly quantitative and concentrated in large urban centers. Findings indicate that healthcare access is shaped by persistent structural and symbolic barriers, including stigma, discrimination, fear of disclosure, and limited professional preparedness. Care remains largely centered on human immunodeficiency virus (HIV) and sexually transmitted infections (STIs) services, with insufficient integration of primary care, mental health, and substance use services, contributing to fragmented care. Significant gaps were identified, including the underrepresentation of women, transgender, and non-binary populations, and the absence of studies focusing on healthcare professionals. Conclusions: Substance use patterns reflect both global trends and local specificities, particularly the prominence of alcohol and cocaine in Brazil. This review provides the first synthesis of Brazilian evidence on chemsex from a healthcare access perspective. The findings highlight critical inequities and support the need for integrated, stigma-free, and context-sensitive care within the Brazilian Unified Health System. Based on these findings, the VIP-SDU/Chemsex Model is proposed as a multilevel framework to explain how structural, symbolic, and programmatic factors shape access and health outcomes. Full article

Design management in Industry 5.0 faces a persistent gap due to fragmented conceptualizations that treat AI, human creativity, and machine capabilities as largely separate elements, limiting their effective integration within complex socio-technical systems. Addressing this gap, the present study develops and empirically validates a Design Management Model grounded in human-centered, ethical, and sustainability-oriented principles, framing design management as an adaptive and relational system rather than a linear or technology-driven process. Departing from the automation-oriented logic of Industry 4.0, the study adopts an augmented cognition perspective in which AI functions as a collaborative partner supporting, rather than replacing, human judgment. A sequential mixed-methods approach was employed, integrating systematic literature review, qualitative content analysis, expert evaluation, and Structural Equation Modeling (SEM) based on data from 316 participants, followed by empirical examination in two service-oriented case contexts, namely tourism/hospitality and healthcare services. The findings identify and validate six interrelated domains and demonstrate that Human–AI–Machine Synergy plays a central role in shaping design outcomes. More specifically, the results show that effective design management in Industry 5.0 depends on the coordinated interaction between cognitive processes, technological infrastructures, and organizational strategies, rather than on isolated technological advancement. Empirical applications further illustrate how the model supports ethically guided AI integration, enhances adaptive decision-making, and improves experience-oriented innovation across different contexts. By providing a validated structural framework that connects previously disjointed elements, this study contributes a clearer operational understanding of how human–AI collaboration can be embedded within design management practices in Industry 5.0. Full article

Large language models (LLMs) are increasingly being explored for integration into sensor-driven control systems across robotics, industrial automation, energy infrastructure, healthcare, smart environments, and other sensor-rich domains. This review synthesizes emerging research from the perspective of sensor-driven control systems, defined as systems in which sensing is substantively linked to monitoring, estimation, supervision, planning, decision-making, or actuation. Rather than treating LLMs as generic intelligent agents, the review examines their position within the sensing–decision–control chain and their interaction with state representations, supervisory logic, human operators, external tools, and classical control components. The paper develops a functional taxonomy of LLM roles based on proximity to actuation, grounding requirements, and deployment risk. This taxonomy reveals a clear maturity gradient: interpretive, supervisory, diagnostic, and engineering-support roles are currently the most credible and deployable, whereas runtime control participation remains the least mature and highest-risk form of integration. The analysis further shows that reliable implementations are predominantly hybrid. In such architectures, LLMs function as semantic and orchestration layers that augment, rather than replace, classical sensing, estimation, planning, and control. Key integration patterns include sensor-to-semantics pipelines, retrieval-augmented generation, tool use, agentic workflows, closed-loop refinement, and safety-aware mechanisms. Persistent challenges—including hallucination, weak physical grounding, latency, cybersecurity risks, and the lack of formal guarantees—highlight the need for rigorous operational evaluation and realistic benchmarks. The review concludes that LLMs are most credible as interpretive, supervisory, diagnostic, and human-facing intelligence layers embedded within hybrid architectures. Future progress will depend on deeper neuro-symbolic integration, efficient local deployment, human-centered autonomy, and stronger evaluation practices that preserve the strengths of classical control engineering while extending them with semantic reasoning and supervisory intelligence. Full article

Rat hepatitis E virus (rHEV) is an emerging zoonotic pathogen, increasingly detected in human, animal, and environmental samples globally. Although rHEV shares the same subfamily as human hepatitis E virus (HEV; Paslahepevirus balayani), which is endemic across Africa, little is known about rHEV ecology on the continent beyond an isolated travel-related case linked to the Democratic Republic of Congo and Gabon. As a rodent-borne virus, rHEV poses a public health risk due to the widespread presence of the synanthropic reservoir Rattus norvegicus. Limited data on hepatitis of unknown origin further emphasise the need for active surveillance to understand the distribution, epidemiology, and impact of rHEV on human health. This review summarises our current knowledge on the global distribution of rHEV in humans, animals, and the environment, highlighting the importance of ongoing surveillance through the One Health Approach. Monitoring the virus in countries such as Nigeria, a country with a significant pig population and documented circulation of hepatitis E virus (HEV), could improve healthcare for immunocompromised and at-risk populations. Furthermore, the viral evolution may facilitate host adaptation and transmission, thereby complicating control efforts. These factors underscore the necessity for targeted research and intervention strategies to address the emerging threat of rHEV effectively. Full article