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Cervical cancer remains a major cause of morbidity and mortality among women in Uganda. Although the Human Papillomavirus (HPV) vaccine is highly effective in preventing cervical cancer, completion of the recommended two-dose schedule remains low, particularly in rural settings. Rukiga District was selected for this study due to persistently low HPV second-dose (HPV2) completion rates compared with national targets. This study assessed health facility-level barriers influencing HPV vaccine completion among adolescent girls aged 9-14 years in rural Uganda. A mixed-methods cross-sectional study was conducted between June and September 2022 in selected Health Centre II (HC II), Health Centre III (HC III), and Health Centre IV (HC IV) facilities in Rukiga District. A household survey involving 292 caregivers of eligible adolescent girls was conducted using systematic random sampling. The primary outcome was completion of the two-dose HPV vaccination schedule (HPV2). Quantitative data were analysed using logistic regression to identify factors associated with vaccine completion. In addition, 21 key informant interviews involving 11 healthcare workers and 10 Village Health Team (VHT) members were conducted and analysed thematically to explore contextual barriers affecting HPV vaccine uptake and completion. The HPV vaccine completion rate was 23.49%, indicating low coverage. In multivariable analysis, vaccine stock-outs and cold-chain challenges (adjusted odds ratio [AOR] = 1.75, 95% confidence interval [CI]: 1.04-2.93; p = 0.004) and understaffing of healthcare workers (AOR = 1.97, 95% CI: 1.05-3.68; p = 0.006) were the only statistically significant predictors of HPV vaccine completion. Although limited healthcare worker knowledge (AOR = 0.94, 95% CI: 0.70-1.24) and absence of government programmes targeting out-of-school girls (AOR = 0.97, 95% CI: 0.73-1.29) were not statistically significant in the adjusted model, qualitative findings highlighted them as important contextual barriers. Additional challenges identified included weak outreach systems, transportation constraints, misconceptions about HPV vaccination, and limited community awareness. HPV vaccine completion in rural Uganda remains low and is strongly influenced by health system constraints, particularly vaccine supply-chain disruptions and human resource shortages. Strengthening vaccine logistics, improving staffing levels, enhancing healthcare worker capacity, and expanding outreach strategies targeting underserved populations are essential for improving vaccine completion and achieving national immunisation targets.

Convergence, defined as lineages evolving to be more similar to one another than their ancestors due to selective pressures is a hallmark of adaptive evolution. Yet, the extent of convergent evolution across different taxa and in different ecological contexts remains unclear. Snakes, with over 4,100 species worldwide, provide a unique model to explore how habitat use influences morphology in a group with an at first sight uniform body plan. We quantified body, head, and tail shape in over 400 species (∼10% of the global snake diversity) to assess the role of habitat use in shaping morphological variation. Our results reveal significant differences across habitats. Terrestrial species display the highest morphological diversity in contrast to other habitats which appear morphologically more specialized. For example, whereas arboreal and semi-arboreal species exhibit elongated heads and slender necks, aquatic and semi-aquatic snakes share streamlined bodies and narrow heads. Fossorial and semi-fossorial species, on the other hand, have compact bodies. Surprisingly, morphological similarity remains limited to arboreal, semi-arboreal, and terrestrial habitat use. Thus, despite strong functional constraints, evolutionary similarity in fossorial and aquatic species is weak, indicating multiple adaptive solutions rather than a single morphological trajectory, possibly due to the relatively homogenous body plan of snakes. Morphological disparity patterns show that non-specialist ecologies generally exhibit greater disparity than highly specialized ones, in accordance with the need of these species to move in different habitats. Our findings underscore the role of ecological constraints in shaping snake morphology and highlight the complexity of adaptation beyond strict convergent evolution. The way that species interact with their environment has been suggested to play a key role in shaping their physical appearance. Our study aimed to understand whether and how habitat use has influenced the evolution of external body and head shape in snakes. To do so, we gathered a comprehensive dataset of morphological traits from 432 snake species. Our findings reveal that species living in different habitats are generally morphologically distinct, with species living in similar habitats displaying similar traits despite the uniform morphology of snakes.

Oral diseases remain a major public health challenge in Mexico. Despite the proven effectiveness of preventive measures, dentists do not consistently implement them in clinical practice. Understanding the factors underlying this gap is essential to strengthening prevention-oriented care. To identify perceived barriers to implementing preventive actions in dental practice. A qualitative study based on grounded theory was conducted using focus groups. Twenty-seven dental professionals from urban, semi-urban, and rural settings in Mexico participated in five virtual sessions. Data were recorded, transcribed, and analyzed using thematic coding. Barriers to implementing preventive actions in dental practice were grouped into three categories: patient-related (lack of preventive habits, misinformation, financial constraints, fear of dental visits); dentist-related (biomedical training, professional prestige linked to complex procedures, financial incentives, and time constraints); and structural (limited institutional support, unequal service distribution, and poor integration of oral health into health policies). A subgroup of dentists reported consistent implementation of preventive actions, characterized by a biopsychosocial approach, stronger communication skills, and a health promotion-oriented professional identity. Cultural, professional, and structural factors interact to shape the implementation of prevention in dental practice in Mexico. Strengthening communication skills, reorienting dental education toward prevention, and enhancing institutional support may facilitate the integration of preventive actions into routine care. Understanding multilevel barriers to prevention may help dentists integrate preventive actions into routine care by strengthening communication strategies, addressing patient expectations, and adapting clinical decision-making to social and structural contexts.

This paper introduces a deep learning-based framework for phase-only synthesis of cosecant-squared (csc²) radiation patterns in planar antenna arrays with high efficiency and accuracy. The proposed method employs a physics-informed deep neural network (PIDNN), where the training process is guided by a loss function that enforces consistency between the desired and generated radiation patterns. By embedding physical constraints into the learning procedure, the model effectively achieves two critical objectives: suppression of sidelobe level (SLL) and minimization of ripples within the shaped beam region. To meet the target csc² profile under sidelobe constraints, only the phase excitations of the array elements are optimized, reducing the complexity of the problem. The performance of the proposed approach is evaluated against established optimization methods, including genetic algorithms (GA) and particle swarm optimization (PSO). Numerical and statistical analyses demonstrate that the PIDNN provides superior results in terms of pattern fidelity, loss function convergence, and computation time, particularly for large-scale planar arrays.

This paper proposes a three-tier Stackelberg game-based hierarchical optimization framework for integrated electric vehicle (EV) battery swapping stations (BSS) and charging point operator (CPO) systems. The framework models the strategic interactions among three decision-making layers comprising grid operators, integrated CPO-BSS operators, and EV users within a multi-stakeholder energy management environment. A bi-level mixed-integer linear programming (MILP) formulation combined with backward-induction-based Subgame Perfect Nash Equilibrium (SPNE) analysis is developed to optimize dynamic electricity pricing, battery charging and swapping schedules, grid power utilization, and user service decisions under operational and grid constraints. The upper layer determines time-varying tariffs, demand-response incentives, and capacity charges to improve grid stability and social welfare, while the middle layer optimizes integrated charging-swapping operations and battery inventory management in response to grid signals and user behavior. The lower layer models EV users as rational followers responding to dynamic pricing through charging or swapping decisions. The proposed framework is validated using EV charging sessions from the publicly available ACN-Data corpus from which BSS swapping demand inputs were synthetically derived via a principled data-mapping procedure and Italian GME day-ahead electricity market price data. The results show that the proposed hierarchical framework reduces the operational cost of the system by 14.2-26.5% when compared with the unoptimized baseline system over the five-year simulation period (2020-2024), while reducing the peak grid demand by 26-28% (192-204 kW) compared with the unoptimized system and maintaining 96.8% service reliability. The coordinated strategy further enables effective load shifting toward low-price periods, enhances battery utilization efficiency, and improves demand elasticity through dynamic pricing mechanisms. Comparative analysis shows that the proposed framework captures 15-22% additional value over decentralized Nash equilibrium strategies while achieving near-optimal centralized social welfare performance under realistic institutional and operational constraints. Sensitivity and benchmarking studies confirm the robustness, computational tractability, and scalability of the proposed approach across varying tariff structures, battery inventories, and demand scenarios. The framework provides practical insights for EV infrastructure planning, grid-aware energy management, and regulatory policy design for future integrated charging and battery swapping ecosystems.

Armed conflicts substantially disrupt health systems and undermine routine childhood immunization, increasing the risk of vaccine-preventable disease outbreaks. While declines in vaccination coverage in conflict settings are well documented, less is known about the multi-level determinants associated with childhood vaccination outcomes in African countries affected by armed conflict. This scoping review maps and synthesizes existing empirical evidence on factors associated with childhood vaccination in these settings. A scoping review was conducted in accordance with PRISMA-ScR guidelines. Systematic searches were performed in PubMed, Embase, and Scopus, with supplementary searches in Google Scholar. Peer-reviewed observational studies and systematic reviews published from January 2015 onwards were included if they examined determinants associated with childhood vaccination outcomes in African countries affected by armed conflict. Findings were synthesized narratively and grouped into thematic determinant domains encompassing caregiver characteristics, socioeconomic factors, geographic barriers, conflict-related determinants, and health-system constraints. Twenty-eight studies met the inclusion criteria. Evidence was geographically concentrated in a limited number of countries, particularly Ethiopia, Somalia/Somaliland, the Democratic Republic of Congo, and Nigeria. Maternal/caregiver education and empowerment, geographic access barriers/remoteness, and household/community poverty and wealth were the most frequently reported determinant categories. Across settings, maternal education, antenatal care attendance, and facility-based delivery were consistently associated with higher vaccination uptake. Conversely, poverty, rural residence, insecurity, displacement, and disruption of routine services were recurrent barriers to complete and timely immunization. Health-system constraints such as stock-outs, limited outreach services, and shortages of trained personnel further compounded inequities in vaccination access. Childhood vaccination in conflict-affected African countries is shaped by a complex interplay of socioeconomic vulnerability, caregiver characteristics, conflict dynamics, and health-system disruption. Armed conflict appears to amplify pre-existing inequities in access to routine immunization services. The current evidence base remains geographically uneven, highlighting important gaps in several conflict-affected settings. Strengthening context-specific research is essential to inform resilient, effective, and equitable immunization strategies in conflict-affected settings.

Externally triggered prodrug activation can improve therapeutic index by decoupling systemic distribution from pharmacological activity. This review examines prodrug uncaging strategies enabled by electromagnetic radiation through the lens of activation physics and clinical constraints, spanning UV-visible-near-infrared photochemistry and ionizing radiation. Differences in clinical applicability arise from fundamentally contrasting activation physics and irradiation geometry. While recent reviews have cataloged externally triggered prodrug systems across multiple modalities, we instead organize the field by activation regime and evaluate these systems under clinically realistic constraints. In photochemical activation, direct light-chromophore coupling enables predictable bond cleavage through defined excited-state pathways, but effective application is constrained by tissue optics and beam-sample geometry. X- and γ-rays penetrate deeply into tissues, but their uncaging pathway is indirect via diffusible water radiolysis products, resulting in stochastic rather than deterministic cleavage chemistry. We evaluate these platforms using deliverability, irradiation geometry, dose efficiency under clinically realistic conditions, microenvironment dependence, and functional group compatibility. We conclude with practical design rules and a decision framework that aligns targeted pathology and treatment objectives with the appropriate trigger chemistry, payload selection, and delivery strategy under the governing activation physics.

The dense deployment of Internet of Things (IoT) networks in smart cities poses severe challenges in spectral efficiency, energy consumption, and interference management. This paper addresses the joint optimization of three-dimensional (3D) beamforming, subcarrier assignment, and power allocation in a multi-carrier non-orthogonal multiple access (MC-NOMA) network supporting both device-to-infrastructure (D2I) and device-to-device (D2D) communications. A robust percentile-based channel model with spatial shadowing correlation is adopted to cope with urban propagation uncertainties, and an accurate elliptical footprint model derived from the 3-dB antenna pattern is used to evaluate coverage gaps and beam overlaps. The resulting mixed-integer nonlinear programming problem is solved by a three-layer memetic particle swarm optimization (Hybrid PSO) algorithm that combines a fixed-point Successive Interference Cancellation (SIC-aware) power solver, an iterative Hungarian method for subcarrier assignment, and an adaptive multi-phase local search. Simulation results demonstrate fast convergence, with the network power consumption stabilizing at 88 mW at a 600 MHz carrier frequency. The proposed MC-NOMA with 3D beamforming consistently outperforms baseline schemes that employ OFDMA with shared spectrum or uniform linear arrays, especially under high channel estimation errors, strong external interference, stringent coverage constraints, and increasing user densities. The findings confirm that the joint framework significantly enhances energy efficiency and robustness, making it a scalable solution for next-generation urban IoT networks.

Developing an accessible and scalable strategy for three-dimensional (3D) protein imaging with hard X-ray nanotomography at the single-cell level remains a major analytical challenge due to the lack of intrinsic molecular specificity. Here, we report a target-switchable nanoprobe system based on biotinylated metal nanoparticles (BioMNs) that enables dual-modal fluorescence and hard X-ray imaging of specific proteins in intact single cells. In this modular architecture, the metal nanoprobes serve as universal X-ray signal modules, while molecular specificity is introduced through streptavidin-mediated coupling to biotinylated antibodies, thereby decoupling molecular recognition from signal generation. Combined with synchrotron radiation hard X-ray nanotomography (SR-HXT), this platform allows 3D visualization of diverse protein targets by simply replacing the primary antibody, eliminating the need for customized probe synthesis for each new target. As a validation of its versatility, membrane-associated HER2 and nuclear Ki67 were imaged as representative targets, revealing distinct spatial distributions and nanoscale heterogeneity in intact cells. This target-switchable approach overcomes the constraints of conventional single-target probes and establishes a correlative fluorescence-X-ray workflow for scalable 3D molecular imaging at the single-cell level.

Multiple sclerosis (MS) imposes substantial clinical, economic, and social consequences, particularly in young adults during their most productive years. While the societal cost of MS has been extensively studied in high-income countries, data from low- and middle-income countries remain scarce. We aimed to provide the first comprehensive societal cost-of-illness analysis of MS in Morocco. We conducted a single-center, cross-sectional study including 100 adults with MS followed at a tertiary university hospital in Casablanca. Costs were assessed from a societal perspective and annualized at the individual level, including direct medical, direct non medical, and indirect costs (human capital approach). Disability was measured using the Expanded Disability Status Scale (EDSS). Health-related quality of life was evaluated using the EQ-5D-5L Moroccan value set to derive utility scores. The mean age was 37.9 years, and 71.0% were women. Median EDSS was 2.0 (IQR: 1.0-6.0; range: 0-10). The mean annual total cost per patient was 92,646 MAD (≈9,358 USD). Direct medical costs averaged 58,884 MAD (5,948 USD), predominantly driven by disease-modifying therapies (47,759 MAD). Direct non medical costs were modest (984 MAD). Indirect costs averaged 32,777 MAD (3,311 USD), largely reflecting productivity losses and informal caregiving. Cost distribution shifted with disability severity: direct medical costs predominated at lower EDSS levels, whereas indirect costs became the principal contributor with increasing disability. The mean EQ-5D-5L utility score was 0.688. MS in Morocco generates a substantial societal burden that extends far beyond medical expenditures. While early stage disease is mainly associated with pharmacological costs, advancing disability shifts the burden toward productivity loss and informal care. These findings underscore the necessity of context-specific pharmacoeconomic frameworks and policies that integrate clinical severity, social vulnerability, and healthcare system constraints in low and middle-income settings.

Event-related potentials (ERPs) provide implicit feedback and error-correction signals that are valuable for brain-computer interfaces (BCIs). However, models trained on source-domain subject data are vulnerable to inter-subject variability and acquisition noise, which substantially degrades generalization to unseen subjects. We propose a multi-view contrastive learning domain generalization (MVCLDG) method to improve cross-subject generalization in ERP recognition by jointly exploiting discriminative feature extraction and domain-invariant representation learning. MVCLDG employs a multi-view feature-extraction module that fuses raw electroencephalography with phase information derived from the Hilbert transform via multi-scale inception blocks, thereby capturing both amplitude and phase features. The model then applies domain-alignment and contrastive-learning constraints to reduce distributional discrepancy across domains, compact within-class representations, and enlarge between-class separability. The approach was evaluated on a public Error-Related Negativity (ERN) dataset and a self-collected semantic-syntactic violation dataset; performance was assessed in cross-subject settings, and ablation and visualization analyses were conducted to probe the contributions of components and neurophysiological interpretability. MVCLDG outperformed baseline and representative domain generalization methods in cross-subject ERP recognition without requiring additional target-domain adaptation. Ablation experiments confirmed the effectiveness of each component. Eigen-Class Activation Maps visualizations indicate consistency between the model-attended electrodes and known neurophysiological scalp patterns, supporting both the model's generalization mechanism and its biological interpretability. MVCLDG offers an effective strategy for integrating phase-aware multi-view feature mining with contrastive domain generalization, yielding improved and interpretable cross-subject ERP recognition. The method advances the feasibility of ERP-based closed-loop BCIs that generalize across users.

Sulfur-siderite composite reactive fillers (SSCReFs) hold strong commercial and engineering potential for nutrient-rich wastewater treatment, yet their performance is intrinsically governed by nonlinear coupling among sulfur-driven denitrification, proton-mediated siderite dissolution, and irreversible material consumption. These interactions disrupt proportional relationships between filler composition, influent chemistry, and nutrient removal, rendering empirical tuning fundamentally unreliable. To overcome this limitation, we developed an interpretable, machine learning (ML)-integrated framework (SSCReF-MAOF) that captures nonlinear sulfur-iron-microbe-mineral interactions and enables multi-objective optimization of effluent quality, consumption-guided compositing (CGC), and treatment cost. Experiments showed that higher sulfur-to-siderite ratios or elevated alkalinity enhance denitrification, whereas siderite-rich formulations promote dephosphorization and strengthen CGC under alkalinity-limited conditions. SSCReF-MAOF integrates five optimized ML models with high predictive accuracy (R2 = 0.930-0.981) and identifies mechanistically meaningful features linked to both sulfur oxidation/iron dissociation and effluent quality dynamics. By combining ML predictions with stoichiometric constraints, the framework determines cost-minimized filler formulations that meet regulatory nutrient-removal compliance and material CGC requirements. Model interpretation further highlights the central role of dissociated iron in coordinating nitrogen-phosphorus removal, advancing understanding of sulfur-iron geochemistry in engineered biosystems. Finally, a paired graphical user interface, together with a site-specific case study further demonstrates practical deployability, providing wastewater treatment plants with an intelligent decision-support tool for tailored SSCReF design and advanced nutrient polishing.

Conformational isomerism is a fundamental aspect of molecular behavior, yet bond-angle inversion (akamptisomerism) remains a rare and poorly understood mechanism, with evidence largely limited to B-O-B-bridged macrocycles. In this study, the conformational landscapes of X-O-X systems (X = B, C, N, and O) were investigated to assess the generality of this process. Conformational searches and energy profiles were computed at the GFN2-xTB and B3LYP/def2-TZVP levels. The results indicate that akamptisomerism is not a viable pathway in these systems. A linear geometry was located but corresponds to higher-order saddle points rather than a true transition state and is associated with prohibitive energy costs (>70 kcal mol-1). In contrast, H2N-O-NH2 undergoes interconversion via rotamerism and, more favorably, trigonal pyramidal inversion, with barriers of ∼15 and ∼8 kcal mol-1, respectively. These findings indicate that akamptisomerism is not a general feature of X-O-X motifs and likely requires specific geometric constraints, such as those found in macrocyclic environments.

Developmental neurotoxicity (DNT) arises from disruption of key neurodevelopmental processes-including neural stem cell proliferation, neuronal and glial differentiation, radial migration, and synaptogenesis-that collectively shape corticogenesis. Traditional in vivo guideline studies are costly, low-throughput, and provide limited mechanistic insight, prompting OECD and EFSA to promote new approach methodologies (NAMs) such as the neurosphere assay (NSA). We refined and characterized a mouse cortical NSA over a three-week differentiation period using a multiparametric endpoint battery encompassing proliferation, neuronal and astrocytic differentiation, radial migration, synaptogenesis, and astrocytic maturation. Baseline differentiation was defined by flow cytometry, confocal immunofluorescence, and qPCR. Two chronic exposure scenarios were implemented: (i) during the 7-day proliferation phase, and (ii) from the onset of differentiation and migration throughout the three-week maturation period. Chlorpyrifos (CPF) was used as a DNT-positive reference compound, and a biomonitoring-informed PFAS mixture of PFOS, PFOA and PFUnDA was designed using French Esteban data to reflect low-nM, environmentally relevant exposure levels; due to availability constraints, PFHxS was not included and 4:2 fluorotelomer sulfonic acid (4:2 FTSA) was used as the short-chain sulfonate component. Baseline analyses showed progressive acquisition of neuronal (TUBB3, MAP2, SATB2) and astrocytic (GFAP) phenotypes, emergence of SYP⁺/PSD95⁺ synaptic structures, and dependence on mitogenic signaling, consistent with key features of mid-gestational corticogenesis. CPF exerted biphasic effects, with early neurosphere enlargement followed by growth arrest and impaired radial migration; at 250µM, CPF induced overt cytotoxicity and was associated with reduced GFAP expression. In contrast, PFAS mixture produced only modest effects on bulk viability yet consistently reduced radial migration and significantly downregulated Gfap and Syp at low-nM concentrations, in line with epidemiological and experimental evidence implicating PFAS in neurodevelopmental disorders. Within this proof-of-concept dataset, radial migration emerged as a more sensitive endpoint than bulk viability, revealing functional impairments below overt toxicity thresholds. The cortical NSA captures key cellular and functional features of mid-gestational corticogenesis and discriminates compound-specific DNT liabilities, with CPF linked to reduced Gfap expression under overtly toxic conditions and PFAS mixture disrupting astrocytic and synaptic programs at biomonitoring-relevant levels. By integrating complementary endpoints under chronic, developmentally targeted exposures, this work advances the NSA as a mechanistic, regulatory-relevant NAM and a strong candidate for inclusion in the OECD DNT in vitro battery.

Artificial intelligence (AI) is transforming drug discovery and development, fields historically constrained by long timelines, high costs, and substantial attrition. Recent advances, particularly in generative modeling, enable an accelerated and increasingly systematic exploration of vast chemical and biological spaces, improving molecular interaction modeling and streamlining the identification and optimization of therapeutic candidates. However, the true utility of this expanded search space remains strictly bounded by the quality of upstream data and the logistical constraints of downstream experimental validation. Emerging platforms, including scaffold-aware and 3D molecular design tools (e.g., AlphaFold, MoleR, and PocketCrafter), single-cell foundation models, and large language models (LLMs), are expanding AI's applicability across the research and development pipeline, spanning target identification, drug discovery, lead optimization, phenotypic screening, and precision biology.AI is also increasingly integrated into preclinical and clinical research workflows, informing adaptive trial design, enabling AI-driven drug repurposing, and supporting the development of safer and more personalized therapies. While the U.S. FDA has approved numerous AI-enabled medical devices and software tools, no fully AI-discovered and AI-designed drug has yet received marketing approval. Nonetheless, several AI-originated candidates have progressed into clinical development, underscoring AI's growing translational impact. Collectively, these advances position AI as a collaborative "lab partner," capable of uncovering non-intuitive molecular designs, accelerating target and lead optimization, and enabling exploration of previously inaccessible chemical and biological space to inform downstream development and clinical decision-making. Despite gains in efficiency, scalability, and cost reduction, the broader impact of AI depends on access to high-quality multimodal data, robust regulatory and ethical frameworks, and careful recognition of methodological limitations. This review critically examines the evolution of AI approaches, highlighting key challenges and opportunities that shape the future of data-driven therapeutic innovation.

Social isolation is a major social determinant of health, yet little is known about how institutionally shaped isolation influences preventive healthcare engagement among public assistance recipients. This study examined how social isolation affects preventive health check-up attendance among public assistance recipients in Japan. We employed a qualitative-dominant convergent mixed-methods design in an urban municipality in Japan. Quantitative survey data were collected from 444 recipients, including indicators of social contact and support. Preventive health check-up attendance during the current fiscal year was verified through claims-based medical assistance records. In-depth semi-structured interviews were conducted with 25 recipients and analyzed thematically using Braun and Clarke's approach. Quantitative and qualitative findings were integrated side-by-side. Absence of weekly face-to-face contact was highly prevalent (79.7%). In adjusted logistic regression models controlling for sex, age, household size, and employment status, participants with weekly face-to-face contact had significantly higher odds of attending preventive health check-ups (aOR = 3.59, 95% CI: 1.85-6.94). Qualitative analysis identified four interrelated themes: (1) institutional and economic constraints producing isolation, (2) stigma-driven avoidance of healthcare, (3) small ties within shared welfare spaces enabling engagement, and (4) the limits of individualized prevention and the need for structurally supported pathways, including social prescribing. Preventive health check-up attendance among public assistance recipients is not solely an individual behavioral responsibility but is socially and institutionally mediated through welfare-related displacement, stigma, and everyday interpersonal ties. Policies should incorporate socially supported preventive pathways to reduce isolation and promote health equity among marginalized welfare populations.

Moral distress is a relevant occupational health issue in hospital settings, particularly in high-complexity environments such as surgical units, where organizational constraints, ethical dilemmas, and interprofessional dynamics shape professionals' experiences. Comparative evidence is needed to understand how moral distress manifests across professional groups working within the same clinical context. This cross-sectional comparative study examined moral distress among physicians and nursing staff working in surgical settings at a public university hospital in southern Brazil. Data were collected from 245 healthcare professionals (167 physicians and 78 nursing staff) using the Brazilian Moral Distress Scale - Short Version (EDME-BR-VR). Group differences in moral distress dimensions were assessed using descriptive statistics and Mann-Whitney U tests. Comparative structural analyses were conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM), with separate models estimated for each professional group to examine similarities and differences in structural relationships. Significant differences between physicians and nursing staff were observed in selected moral distress dimensions, particularly Safe and Qualified Care, Defense of Values and Rights, and Working Conditions. Across both groups, Recognition, Power, and Professional Identity emerged as a central antecedent of moral distress, showing significant associations with teamwork, working conditions, safe and qualified care, and ethical violations. Comparative PLS-SEM results indicated similar overall relational patterns across groups, with differences in the magnitude of specific structural paths reflecting profession-specific dynamics. Moral distress among physicians and nursing staff in surgical settings is shaped by shared organizational factors as well as profession-specific ethical pressures. Comparative analysis highlights the central role of professional recognition and identity while underscoring the need for differentiated organizational strategies to address moral distress across professional groups. These findings provide actionable insights for healthcare managers seeking to promote ethical practice, psychological safety, and sustainable work environments in surgical care.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide and a major contributor to reduced quality of life. The disproportionately high risk of CVD faced by Saudi women compared with Saudi men is driven by rising rates of obesity, physical inactivity, and lifestyle-related risk factors. Despite the importance of dietary modification in secondary prevention, adherence to a heart-healthy diet remains challenging for many Saudi women due to gendered caregiving responsibilities, limited autonomy in food choices, and sociocultural expectations to prioritize family needs over their health. This study was designed to explore the barriers and motivators related to healthy diet adherence among Saudi women living with CVD, with a particular analytical focus on how gendered household roles and sociocultural expectations shape dietary behaviors. A qualitative descriptive design was employed, involving individual semistructured interviews with 19 Saudi women. Thematic analysis was conducted in accordance with Braun and Clarke's six-phase approach. The factors identified in the analysis were grouped into four themes: individual, social, institutional, and environmental. Gender roles, particularly the responsibilities of women as primary caregivers and meal preparers, consistently shaped the ability of the participants to adhere to healthy diets. Disease severity and family support were identified as key motivators, while the identified barriers included prioritizing family preferences over personal health, lack of tailored dietary counseling, and limited access to healthy food options. The findings underscore the multifaceted challenges Saudi women face in adhering to heart-healthy diets, particularly those rooted in gendered caregiving roles, social obligations, institutional gaps in dietary support, and environmental barriers to food access. Effective dietary interventions must go beyond individual education and be purposefully embedded within culturally sensitive, gender-responsive strategies that address these broader structural and contextual constraints.