Search Results (1,331)

Background: Food insecurity and water insecurity are increasingly recognized as interconnected social determinants of health; however, their co-occurrence remains underexplored in U.S. populations. SNAP households with children experience a high prevalence of both food and water insecurity. This study estimated the prevalence of food insecurity and water insecurity and examined their co-occurrence among SNAP households with children in the Southern U.S. Methods: A cross-sectional, web-based survey was conducted among 683 SNAP participants residing in households with children. Food insecurity was assessed using the 10-item USDA Adult Food Security Survey Module, and water insecurity was measured using the Household Water Insecurity Experiences (HWISE) scale. Descriptive statistics estimated prevalence, and regression analyses assessed associations. Results: Food insecurity (75.1%) and water insecurity (53.9%) were highly prevalent among surveyed SNAP households with children. Nearly half of households (48.6%) experienced both conditions, while 19.6% were secure in both. Food insecurity alone was reported by 26.5% of households, and water insecurity alone by 5.3%. Higher food insecurity scores were associated with increased odds of water insecurity (AOR = 1.33, 95% CI: 1.26–1.40, p < 0.001). Conclusions: Food insecurity and water insecurity frequently co-occur among SNAP households with children. Integrated public health strategies addressing both food and water access are needed to reduce disparities and improve household well-being. Full article

Natural product-based upconversion photosensitizers (PSs) have emerged as innovative agents in photodynamic therapy (PDT). Lanthanide ions such as Yb³⁺, Er³⁺, Nd³⁺, Gd³⁺, and Tm³⁺ have unique photophysical properties and biocompatibility, exhibiting sharp 4f–4f transitions and long-lived excited states involving the dual luminescence processes, upconversion and downshifting. Natural product photosensitizers (PSs), including coumarin, riboflavin, curcumin, chlorophyll derivatives, and hypocrellin, offer superior safety profiles compared with synthetic PSs. Recent advances in upconversion nanoparticles (UCNPs) and upconversion–downshifting nanoparticles (UDNPs) for the generation of ROS in PDT have been evaluated. This narrative review surveyed the literature published between 1995 and 2026 across multiple electronic databases, including WanFang Data, PubMed, ScienceDirect, Scopus, Web of Science, Springer Link, SciFinder, and the China National Knowledge Infrastructure (CNKI), without language restrictions. The search focused on studies related to photodynamic therapy, lanthanide photophysics, and natural product photosensitizers such as coumarin, riboflavin, curcumin, chlorophyll derivatives, and hypocrellin, as well as nanoplatforms involving upconversion (UCNPs) and upconversion–downshifting nanoparticles (UDNPs). Relevant publications were identified and synthesized to integrate advances in lanthanide photophysics, natural product PSs, and nanoplatform design into a conceptual framework. Natural product-based upconversion PSs for PDT have the advantages of low dark toxicity, biocompatibility, and multimodal actions. Lanthanide-enhanced systems overcome these issues, including shallow tissue penetration, photobleaching, and relatively low singlet oxygen quantum yields. Thus, natural product-based upconversion PSs in PDT are an innovative strategy, but bridging preclinical promise with clinical translation remains a critical future challenge. Full article

Background/Objectives: Mental Health First Aid (MHFA) is an internationally recognized training program designed to improve mental health literacy, reduce stigma, and train non-clinicians with supportive skills to assist individuals experiencing psychological distress. Although prior research has demonstrated trainees’ short-term improvements in knowledge and attitudes, less is known about their post-certification perceptions. Here, we assess the likelihood of trainees’ continued application of MHFA concepts several months after certification. The current study examined the participants’ self-reported perceptions of the behavioral and attitudinal outcomes three to six months following training, including, help-seeking behaviors, self-efficacy, stigma-related attitudes, and functional application of intervention skills. Methods: A mixed-methods, cross-sectional design was employed using a structured web-based survey administered to MHFA-trained participants. The survey included demographic items, Likert-scale measures of confidence, behavioral engagement, and stigma-related attitudes, as well as open-ended qualitative prompts. Quantitative analyses included descriptive statistics, reliability testing using Cronbach’s alpha, exploratory factor analysis, assumption diagnostics, and nonparametric hypothesis testing using Mann–Whitney U tests. Qualitative responses were analyzed using thematic analysis to identify recurring patterns related to long-term training impact. Results: Participants reported increases in self-confidence and behavioral engagement following MHFA training. More than 80% of participants indicated they had recommended professional mental health services to others after certification. Psychometric evaluation demonstrated strong internal consistency for the six-item self-stigma scale (α = 0.90), with a unidimensional factor structure explaining 60.4% of the variance, whereas the public stigma scale showed weaker internal reliability (α = 0.45). Qualitative themes included increased self-awareness, stigma reduction, advocacy behavior, professional empowerment, and institutional validation of mental health priorities. Conclusions: The findings suggest participants perceived continued confidence, reduced internalized stigma, and engagement in supportive mental health behaviors following MHFA training. These findings should be interpreted cautiously given the cross-sectional design, self-reported measures, and highly engaged sample. Full article

Background: Game-based learning (GBL) and active engagement strategies have shown promise in health professions education; however, their application in foundational basic science courses, which serve as critical academic gatekeepers in professional pharmacy programs, remains underexplored. Objective: This study evaluated the effect of a multimodal suite of faculty-developed, web-based GBL activities and active engagement strategies on academic performance, learning management system (LMS) engagement, and student perceptions in a required first-year (P1) pharmacy biochemistry course. Methods: A cross-sectional, descriptive study was conducted with 117 P1 pharmacy students. Game-based activities (including Jeopardy, Rapid Fire, and Crossword Puzzle games) were developed using WiscOnline and deployed via Blackboard alongside pre-class activities, a Workbook, and a Padlet discussion board. Engagement was measured via LMS access metrics, academic performance via examination scores, and perceptions via an end-of-semester anonymous Poll Everywhere survey. Results: The cohort achieved a mean examination score of 81.59%, with 83.8% earning a passing grade. A positive association was observed between game access frequency and grade group performance (R² = 0.8241), though individual-level correlations did not reach statistical significance. Student perceptions were overwhelmingly positive, with over 90% of respondents agreeing that GBL activities were enjoyable, facilitated learning, and provided valuable practice opportunities. Conclusions: Low-cost, faculty-developed GBL tools can be successfully integrated into foundational pharmacy science courses, yielding high engagement and positive student perceptions. These findings underscore the importance of extending active learning research to foundational basic science courses and offer a replicable model for health professions programs seeking to enhance student engagement at this critical curricular stage. Full article

Mitochondria-targeting metal complexes (MTMCs) are a mechanistically distinct class of metallopharmaceuticals. Unlike first-generation platinum drugs that form nuclear DNA adducts, MTMCs exploit organelle-specific vulnerabilities such as hyperpolarised mitochondrial membrane potential (ΔΨm), elevated reactive oxygen species (ROS), limited mitochondrial DNA (mtDNA) repair capacity, and redox-dependent enzymes such as thioredoxin reductase (TrxR). We systematically searched PubMed, Web of Science, Scopus, and Google Scholar databases for studies published between 2016 and 2026, applying predefined inclusion criteria that included subcellular localization evidence and functional bioenergetic endpoints. The search identified 147 studies covering Pt(II/IV), Ru(II/III), Au(I/III), Ir(III), Os(II), Re(I), and V(IV/V) complexes and metal–organic framework nanoplatforms. Mechanistic evidence converges on four intramitochondrial target categories: inhibition of ETC (Electron Transport Chain) Complexes I/III with consequent ATP depletion; ROS overproduction, coupled with glutathione and TrxR depletion; outer mitochondrial membrane permeabilization and intrinsic apoptotic cascade activation; and mtDNA damage within a compartment limited to base excision repair. Multi-modal cell death—the co-occurrence of apoptosis, ferroptosis, necroptosis, and autophagic cell death—was a recurrent finding across the reviewed studies. This review thoroughly surveys the latest trends in MTMC drug design (metals, ligand structures, and mechanisms of action) and summarises analytical techniques for speciation, pharmacokinetics, safe monitoring, and resistance, while critically analysing translational barriers and clinical failures. To address the field’s inconsistent terminology, we introduce an explicit localization evidence hierarchy that distinguishes mitochondria-targeting complexes (through quantitative ICP-MS fractionation or co-localization with defined Pearson/Manders coefficients) from simply mitochondria-localising or mitochondria-perturbing agents, and we apply it throughout. We also point out that the idea of selectivity being purely driven by membrane voltage (ΔΨm) and thermodynamics is constrained by membrane and protein binding, as well as the transmembrane pH gradient, kinetic limitations, and demonstrated heterogeneity of cancer-cell membrane potential, and, as such, the functional mitochondrial effects must not be equated with mitochondrial accumulation. Since elemental quantification cannot distinguish intact complex from protein adducts and decomposition products, speciation-aware pharmacokinetics emerges as a prerequisite for a credible exposure–response interpretation. The translational progress will depend less on new chemotypes than on this analytical and pharmacokinetic rigour, together with organelle-level safety monitoring and biomarker-guided patient selection. Full article

Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment. Full article

Deep Neural Networks (DNNs) are increasingly being used in the design of industrial safety-critical autonomous applications such as autonomous vehicles, industrial robotics, and medical instrumentation and control systems. Ensuring reliable and robust operation of the DNN-based safety-critical systems is challenging because of the complex structure of DNN hardware accelerators utilized for inference that are susceptible to the effects of multi-faults, common-cause fault models, data uncertainties, and unpredictable erroneous behavior. Additionally, transient, permanent, and timing faults affect the accelerator design of processing elements, memory arrays, and datapaths, propagate through DNN computations, and potentially can cause catastrophic failures at the system level. The objective of this survey paper is to systematically evaluate the state-of-the-art fault-tolerant DNN accelerator architectures with particular emphasis on their applicability to safety-critical autonomous systems in industry. The survey investigates architectural perspective, fault modeling, and platform-level trade-offs, runtime resilience, validation practices, and certification readiness, following a PRISMA methodology with evidence-driven synthesis and unbiased study selection. Database searches across IEEE Xplore, Scopus, and Web of Science identified 200 records, of which 82 studies were included based on predefined inclusion and exclusion criteria emphasizing industrial safety-critical relevance, fault modeling at the hardware level, and the implementation at the architectural level. The results indicate that there was a clear shift from traditional redundancy-based approaches to cross-layer and adaptive approaches that provide better trade-offs between performance, reliability, and hardware overhead. The current studies presented are based on simplified fault models, incomplete validation- procedures, and limited consideration of system-level and certification needs, which often do not consider critical failure modes such as Silent Data Corruption (SDC). This has resulted in a significant gap between research-level solutions and industrial deployment requirements. This survey underscores the need for scalable, integrated, and certification-aware design approaches to help connect fault modeling, architectural resilience, validation, and safety assurance to develop reliable and deployable DNN accelerator systems for next-generation industrial safety-critical autonomous applications. Full article

Ancient and large old trees hold significant ecological, cultural, and landscape importance. While numerous studies have investigated the ecosystem services these trees provide—such as carbon sequestration, air purification, and microclimate regulation—the findings remain dispersed and fragmented. To address this gap, the present study employs ecosystem service theory alongside a combination of bibliometric analysis and systematic review, examining 217 articles indexed in the Web of Science Core Collection. This approach offers a comprehensive synthesis of global research on ancient trees. The bibliometric results reveal a rapid expansion of research since 2015, with a particularly notable surge after 2019. Research focus has progressively shifted from traditional resource surveys and conservation management toward regulating services, emphasizing ecosystem functioning, carbon storage, microclimate regulation, and biodiversity conservation. Concurrently, methodological approaches have diversified, incorporating GIS-based spatial analysis, remote sensing techniques, and carbon storage modeling. Despite these methodological advancements, current research faces several challenges, including insufficient integration across spatial and temporal scales, limited long-term dynamic monitoring, and a weak linkage between ecological functions and sociocultural values. To enhance the protection and revitalization of ancient trees, future investigations should adopt multiscale and interdisciplinary frameworks that integrate ecological functions, landscape spatial dynamics, and sociocultural dimensions. Such approaches will facilitate the sustainable management of ancient trees globally and ensure the enduring provision of their ecosystem services. Full article

Museum-oriented built heritage sits at the intersection of conservation, structural assessment, and visitor experience, yet the integration of digital twin (DT) and virtual reality (VR) technologies across these domains has not been mapped as a unified research field. Within this broader interface, stone-built museums are treated as an interpretive lens and application case rather than as the strict scope of the indexed material. This study presents a structured bibliometric science-mapping analysis of 465 peer-reviewed articles retrieved from the Web of Science (WoS) Core Collection (1999–2026) through a four-stage PRISMA-ScR-informed screening protocol, using bibliometrix-based keyword co-occurrence, thematic mapping, and co-citation analysis. Four conceptual clusters emerge: digital documentation and photogrammetric survey; VR, virtual museum, Heritage Building Information Modelling (HBIM), and emerging digital-twin applications; AR-based museum experience and interpretation; and sustainable heritage tourism and management. Italy (n = 90) and China (n = 85) lead national output; Universidad Politécnica de Valencia is the leading institution (n = 31). A persistent separation between documentation-focused and experience-focused communities is observed. A three-pillar framework linking DT-based structural documentation, immersive visitor experience, and sustainable museum management through Industry Foundation Classes (IFC) interoperability is proposed for empirical validation in stone-built museum case studies. Full article

Potato is the third most important food crop in the world after maize and rice. Its importance stems from its contribution to food security in most parts of the world. Although the crop is widely cultivated globally, it faces numerous biotic and abiotic challenges, among which plant-parasitic nematodes pose a significant threat. The objective of the study is to map the nematode species affecting potato crops while drawing links with their pervasiveness and outlining effective control strategies. The article selection process followed the PRISMA guidelines. A total of 41 articles were selected for the review from an initial 944 records retrieved from the Web of Science, Scopus, CAB Abstract, and reference list based on their relevance to the study criteria. The findings indicate that G. pallida, G. rostochiensis, M. incognita and M. javanica were the most reported nematodes globally. Chemical and biological control remain the most widely used management strategies, while incorporating resistant cultivars, abiotic inducers, organic fertilizers, and crop rotation offers greater potential to enhance the sustainability and resilience of farming systems. A significant global research gap persists in nematode surveillance and diagnostic surveys of potato-growing regions. Full article

Background/Objectives: Medical Malpractice Stress Syndrome (MMSS) describes the psychological and behavioral consequences of malpractice-related concerns and may contribute to defensive medicine practices. Emergency medicine physicians are particularly vulnerable to medicolegal risk because of the high-acuity and time-sensitive nature of their work. This study aimed to evaluate MMSS awareness, malpractice-related stress, and defensive medicine practices among emergency medicine specialists in Türkiye. Methods: A national cross-sectional web-based survey was conducted among emergency medicine specialists practicing in Türkiye between 1 April and 1 June 2026 and was distributed through professional emergency medicine networks and congress-related channels; because the survey was disseminated through multiple open professional platforms, an exact response rate could not be determined. The questionnaire assessed demographic characteristics, MMSS awareness, malpractice-related experiences and anxiety, and defensive medicine behaviors. Defensive medicine practices were evaluated using the validated Turkish version of the Defensive Medicine Attitude Scale. Associations between malpractice-related anxiety and defensive medicine behaviors were analyzed using Spearman correlation analysis. Results: A total of 128 emergency medicine specialists completed the survey. Malpractice-related anxiety was highly prevalent and frequently influenced professional attitudes and clinical decision-making. Defensive medicine practices were common, with assurance-type behaviors being substantially more frequent than avoidance-type behaviors. Malpractice-related anxiety demonstrated a significant positive correlation with assurance defensive medicine behaviors (r = 0.383, p < 0.001) but not with avoidance behaviors (r = 0.139, p = 0.118). Assurance and avoidance defensive medicine scores were positively correlated (r = 0.575, p < 0.001). Conclusions: MMSS and malpractice-related anxiety are highly prevalent among emergency medicine physicians in Türkiye and are associated with widespread defensive medicine practices. Malpractice-related anxiety appears to primarily promote assurance-type defensive medicine behaviors rather than avoidance behaviors. These findings suggest that medicolegal stress may influence clinical decision-making and contribute to increased healthcare utilization in emergency medicine. Full article

Background/Objectives: Age-related hearing loss (ARHL) is a common sensory impairment in the elderly, and its early prediction and intervention are crucial for improving the quality of life in older adults. This study aims to develop and validate an interpretable machine learning model based on routine blood biomarkers to predict the risk of ARHL occurrence. Methods: A total of 542 participants were selected from the National Health and Nutrition Examination Survey (NHANES) database, including 271 ARHL patients and 271 healthy controls. The samples were randomly divided into a training set (50%) and two independent internal validation sets (25% each). Through systematic comparison of 113 machine learning algorithm combinations, the optimal predictive model (glmBoost+Stepglm[forward]) was constructed, and the SHAP method was employed for feature interpretation. To evaluate the model’s generalization ability, external validation was further performed using a cohort of 92 cases from Chongqing People’s Hospital. Additionally, an openly accessible interactive prediction web page was developed based on the R Shiny framework, supporting real-time clinical risk assessment and visual interpretation. Results: The model achieved an AUC of 0.948 in the training set, with AUCs of 0.893 and 0.945 in two internal validation sets, respectively, and an overall accuracy rate of 86.3%. In the external validation cohort (albeit with a limited sample size of 92 from a single center), the model maintained good performance with an AUC of 0.839 (95% CI: 0.750–0.918) and an accuracy of 77.2%. The model identified nine key predictive features, with the top three being glycated hemoglobin (HbA1c), mean corpuscular volume (MCV), and blood glucose according to SHAP interpretability analysis. Conclusions: This study successfully developed and validated an interpretable machine learning model based on routine blood biomarkers for community-based risk stratification of age-related hearing loss. The model demonstrated robust performance in internal and external validations, including an age-matched elderly subgroup. An interactive web tool was developed to facilitate real-time risk assessment. While the model is intended as a prescreening tool for large-scale populations rather than a diagnostic test for age-matched individuals, it provides a novel approach for early identification of individuals at higher risk of ARHL and offers insights into its systemic pathogenesis. Full article

Background/Objectives: Clomiphene citrate (CC) is widely used off-label for male infertility despite limited evidence and inconsistent guideline recommendations. Although previous studies suggest variability in clinical practice, real-world data on prescribing patterns, patient selection, monitoring, and treatment success definitions remain limited. This study assessed CC prescribing patterns among urologists and identified factors associated with its use. Methods: A national, cross-sectional, web-based survey was conducted among urologists in Türkiye between November and December 2025. Of 1558 invited participants, 421 responded (27.0%), and 402 were included in the final analysis. The questionnaire was based on European Association of Urology and American Urological Association guidelines, refined through expert consensus, and pilot-tested. Multivariable logistic regression identified factors independently associated with CC use. Results: CC was used by 39.3% of respondents and was independently associated with private practice (odds ratio [OR] = 2.90, p < 0.001), greater professional experience (OR = 2.18, p = 0.002), and higher infertility case volume (OR = 2.27, p = 0.001). Substantial heterogeneity was observed in patient selection, dosing, monitoring, and success definitions. Treatment goals and perceived success definitions most frequently focused on laboratory-based endpoints, including semen parameters and testosterone levels, which were more frequently selected than pregnancy-related endpoints. However, spontaneous pregnancy was also commonly reported as a perceived success definition, whereas live birth was not separately assessed. An apparent indication paradox was observed for hypogonadotropic hypogonadism, which may reflect differing interpretations of functional versus irreversible hypogonadotropic states, and 31.6% of clinicians reported not routinely providing risk counseling. Conclusions: CC prescribing for male infertility remains heterogeneous among responding urologists and was associated with clinician experience, practice setting, and infertility case volume rather than standardized protocols. The predominance of laboratory-based endpoints, together with the frequent inclusion of spontaneous pregnancy as a perceived success definition and the absence of separate live-birth assessment, underscores the need for clearer terminology, standardized prescribing frameworks, structured risk counseling, and future studies incorporating clinically meaningful reproductive endpoints. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Diadromous fish provide key ecological and socio-economic services in European Atlantic catchments, yet their marine phase remains poorly understood and weakly integrated into management. Involving nine partners from Portugal, Spain, France and Ireland, the DiadSea Interreg Atlantic Area initiative aims to fill these critical knowledge gaps on the marine and estuarine phases and to translate this information into coordinated conservation and fisheries management tools. To do so, the project combines historical and newly collected fishery-dependent and -independent data (landings, by-catch, cooperative surveys with commercial and recreational fishers) with advanced microchemical, genetic and environmental DNA (eDNA) analyses to characterize marine distributions, mixing areas and connectivity for shads, Atlantic salmon, sea trout, European eel, sea lamprey and other diadromous species. It also includes innovative case studies on lamprey tagging and intestinal metabarcoding, coastal habitat suitability mapping for shads using river plumes and environmental variables, and joint otolith microchemistry–genomics approaches to reassess European eel panmixia and maternal origin at the Atlantic scale. Species distribution models under present and future climate scenarios, specifically RCP4.5 and RCP8.5, are used to identify priority marine areas for conservation, zones of high temporal turnover and key interfaces ensuring longitudinal (river–sea) and latitudinal connectivity, which will feed into an updated interactive web Atlas of diadromous species. In parallel, DiadSea establishes a transnational observatory of stakeholders to harmonize legislation, co-develop adaptive fisheries management plans and produce climate-aware policy guidelines, while capacity-building actions include an origin-labeling scheme for sustainably harvested diadromous fish, educational games and a comic book to raise awareness among younger generations and the wider public. Together, these work packages will deliver the first integrated, marine-focused, evidence-based and decision-support framework for diadromous fishes in the North-Eastern Atlantic, strengthening conservation, sustainable fisheries and stakeholder engagement under ongoing climate change. Full article