Search Results (589)

Cinematic representations of mental health operate at the intersection of science, culture and visual meaning, while medical academic discourse plays an important role in shaping how such representations are conceptualized. This study examines how the PubMed-indexed literature (2010–2025) engages with mental health in relation to narrative film and related screen media, combining bibliometric mapping with interpretive analysis. Through a structured PubMed query and VOSviewer co-occurrence analysis, this study identifies 5292 unique terms, of which 530 meet the minimum frequency threshold. Comparison between low- and high-frequency maps reveals a shift from lexical diversity to a consolidated biomedical core centered on classification, diagnosis and measurable affect. Six clusters are identified (neuro-affective, educational stigma, media–behavioral, neuropharmacological–technological, perceptual–emotional and pandemic-related), which together structure the field’s dominant semantic orientations. The findings indicate three main patterns: the predominance of standardized biomedical language, the limited visibility of intersectional categories (e.g., gender, race, identity) at the level of indexed metadata, and a gap between visual processes and narrative meaning. While individual studies often engage with cinematic complexity, this dimension is only partially reflected in the dominant lexical structure. Building on these results, a cluster-informed conceptual framework for film-based medical education is proposed, in which narrative film can support complementary forms of clinical, social and interpretive learning. This study contributes to the field of Medical Humanities by demonstrating that medical discourse not only reflects but also structures the visibility of mental health in relation to screen media, while highlighting the need for more integrated approaches that connect biomedical knowledge with narrative and cultural understanding. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Skin cancer presents a significant global health burden with rising incidence. The side effects of current therapies and the emergence of drug resistance necessitate the exploration of alternative and complementary strategies. Natural products, with their long history of use in treating skin disorders, have emerged as a promising source of novel therapeutic agents. This review comprehensively elucidates the potential efficacy of natural bioactive compounds in both preventing and treating skin cancer. We summarize the molecular mechanisms through which key natural bioactive compounds exert their anti-skin cancer effects, including induction of apoptosis, inhibition of proliferation and metastasis, anti-inflammatory and antioxidant activities, DNA damage repair, and photoprotection. Furthermore, we discuss the biological barriers relevant to skin cancer therapy using natural bioactive compounds and link them to corresponding delivery strategies, while identifying key translational challenges. In conclusion, natural bioactive compounds offer a multi-targeted and synergistic approach against skin carcinogenesis, holding substantial promise as sources of adjuvant therapies and chemopreventive agents to improve patient outcomes. Full article

Patient-reported outcomes (PROs) are an integral component of contemporary oncology. They provide direct insight into symptom severity, treatment tolerability, and health-related quality of life. Despite their clinical relevance, routine implementation faces several hurdles. Key limitations include patient survey fatigue, challenges in real-time interpretation of complex symptom trajectories, and incomplete longitudinal data that limit reliable analysis. This narrative review summarizes recent advances (2020–2026) in applying artificial intelligence (AI) to structured questionnaires, including EORTC QLQ-C30, PROMIS, and PRO-CTCAE, as well as to unstructured clinical text. Machine learning and natural language processing may enhance the clinical utility of PROs through automated analysis, symptom extraction, and predictive modeling. Current studies suggest that AI-based approaches can support the prediction of symptom deterioration, treatment-related toxicity, and healthcare utilization, including unplanned hospitalizations and emergency department visits. Furthermore, NLP models can extract clinically meaningful information from free-text narratives. We also discuss emerging non-invasive digital biomarkers derived from speech and facial expressions. Multimodal approaches suggest that these features may provide complementary indicators of pain, fatigue, and affective state. Overall, AI has the potential to transform PROs from static assessment tools into dynamic clinical instruments. This shift may enable more continuous and proactive symptom monitoring and support the integration of multimodal patient data into oncology decision-making workflows. Full article

The intestinal microbiome is increasingly recognized as an important determinant of canine gastrointestinal health. However, interpreting microbiome sequencing data remains challenging because most analytical approaches rely on taxonomic descriptions, alpha diversity indices, or dysbiosis indices derived generally from a limited number of microbial ecological interpretation targets. While shotgun metagenomic approaches increasingly allow the identification of microbial communities, such analyses remain costly and are not yet widely accessible in routine veterinary settings. The objective of this study was to develop an integrative interpretation framework based on widely accessible biomarkers combining fecal calprotectin and 16S rRNA gene sequencing data. These data enabled the generation of complementary ecological dimensions of gut microbiome organization: biological inflammation assessed through fecal calprotectin, microbiological inflammatory pressure estimated through a Microbiological Inflammatory Score (MIS), and microbiome stability measured by a Microbiome Resilience Score (MRS) derived from alpha diversity, functional balance, and dominance structure. Fecal microbiome profiles obtained by 16S rRNA gene sequencing were analyzed in a real-life cohort of privately owned dogs. Alpha diversity, taxonomic weighting, abundance-dependent dominance rules, beta diversity based on Bray–Curtis dissimilarity, distance to a reference microbiome core, and a 16S-derived dysbiosis score were integrated into a multidimensional interpretation model. Strong ecological associations were observed between resilience, microbial diversity, and dysbiosis-related metrics. Microbiome resilience strongly correlated with Shannon diversity (Spearman ρ = 0.98, p < 0.001), while the reconstructed 16S-derived dysbiosis score showed a more moderate positive correlation with MIS (Spearman ρ = 0.41, p = 0.004), supporting the partially independent ecological dimensions captured by the framework. The results revealed a continuum ranging from stable microbiomes to inflammatory dysbiosis. Most dogs clustered near a reference microbiome core characterized by low microbiological inflammatory pressure and high resilience, whereas a subset of microbiomes showed elevated MIS values, reduced resilience, increased compositional distance from the reference core, and higher dysbiosis index values. These findings support the value of a multidimensional experimental framework integrating inflammation, dysbiosis, and resilience to improve interpretation of canine microbiome profiles under real-life conditions. Full article

Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection, making early detection critical for improving outcomes in intensive care units (ICUs). This study presents a retrospective comparative evaluation of deep learning architectures for predicting sepsis up to 6 h before the PhysioNet/Computing in Cardiology 2019 Challenge onset label using hourly structured electronic health record (EHR) variables, including vital signs, laboratory measurements, and demographics. Evaluated architectures include Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (Bi-LSTM), Temporal Convolutional Network (TCN), Transformer, and hybrid Convolutional Neural Network–Vision Transformer (CNN-ViT) models. Median imputation and class-weighted loss were applied to address missing values and severe class imbalance, while Shapley Additive Explanations (SHAP) and attention analyses were used as complementary interpretability approaches. Among the evaluated models, CNN-ViT achieved the strongest overall minority-class performance, with 88.25% accuracy, 0.7480 recall, a 0.454 F1-score, and a 0.48 area under the precision–recall curve (AUPRC), although the numerical gains over other advanced temporal and hybrid architectures were modest. Leave-one-unit-out evaluation further demonstrated relatively stable performance under internal distribution shifts. The results suggest that combining local feature extraction with temporal and attention-based modeling can improve early sepsis prediction from structured ICU data. However, the study represents a retrospective computational benchmark using a public dataset and does not constitute prospective clinical validation or real-world deployment assessment. Full article

Background/Objectives: Urinary catheterization is common across care settings, but safe management at home and during care transitions often depends on caregivers who receive limited and inconsistent training. Mobile health (mHealth) applications may support caregiver education and decision-making. This review mapped and synthesized evidence on interactive mobile applications for caregiver training in urinary catheterization and developed a conceptual framework to inform nursing practice. Methods: A scoping review was conducted according to Joanna Briggs Institute guidance and reported following PRISMA-ScR. Searches were performed in PubMed/MEDLINE, Scopus, Web of Science, and LILACS, with complementary grey literature searches. Studies evaluating interactive mobile applications for caregiver training in urinary catheterization were included. Data were extracted and synthesized descriptively and narratively. Results: Five studies published between 2020 and 2025 were included. Most were early-stage studies with small samples and heterogeneous designs. Interventions generally combined educational content with interactive features, such as decision-support tools, and less often behavioral strategies, including reminders and feedback. Outcomes mainly addressed knowledge, skills, and self-efficacy, while clinical outcomes, such as infection reduction, were rarely assessed. A conceptual framework was developed showing how intervention components may influence caregiver competence and care outcomes, moderated by contextual factors such as health literacy and digital access. Conclusions: Interactive mobile applications may represent a promising approach to support caregiver training and improve the safety of urinary catheter management. However, current evidence remains preliminary and limited. Full article

The increasing demand for sustainable and health-oriented dairy and alternative products has enhanced interest in reducing or modifying fat in cheese systems. However, such modifications often lead to undesirable changes in texture and flavor, highlighting the multifunctional roles of milk fat within the cheese matrix. Rather than serving solely as a compositional component, milk fat contributes fundamentally to structure organization and flavor development through its physicochemical properties, interactions with the protein network, and lipid-derived pathways. This review examines these roles from a mechanistic perspective and evaluates emerging lipid structuring approaches for texture modulation, while also discussing complementary approaches with potential to address flavor attributes. Collectively, it provides insights for rational formulation and guides future research toward the design of improved dairy and alternative cheese products. Full article

Brucellosis is a globally prevalent zoonotic disease that imposes considerable economic burdens on the livestock industry and remains a significant threat to public health. Although lipopolysaccharide (LPS)-based serological assays are widely used in routine diagnosis, their inherent limitations—particularly cross-reactivity with other Gram-negative bacteria—underscore the need for the development of diagnostic approaches based on non-LPS antigens. In this study, we developed a trivalent nanobody–horseradish peroxidase (3Nbs-HRP) fusion protein targeting Brucella OMP16 and established a cELISA for the serological detection of bovine brucellosis. The diagnostic performance of the assay was assessed using 204 Brucella antibody-negative and 123 Brucella antibody-positive bovine serum samples. ROC curve analysis yielded a sensitivity of 87.7% and a specificity of 89.4%, with no significant cross-reactivity observed. By employing a recombinant antigen and a 3Nbs-HRP probe, this assay enhances biosafety and demonstrates strong potential for standardization and large-scale application, serving as a complementary tool to conventional LPS-based assays for the surveillance, diagnosis, and control of bovine brucellosis. Full article

Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health priority affecting approximately 30% of the population. It represents the hepatic manifestation of metabolic syndrome, potentially progressing from simple steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), cirrhosis, and hepatocellular carcinoma. This review aims to compare current knowledge of MASLD in mouse models and humans, focusing on pathophysiology, histological phenotypes, and the role of preclinical imaging as a non-invasive translational screening tool. Methods: A literature search was conducted in PubMed and Web of Science to identify English-language publications from January 2020 to March 2026 on murine models and imaging techniques for MASLD, using pertinent keywords. Attention was given to highlighting similarities and differences between human and murine approaches. Results: MASLD arises from complex interactions between genetics, sedentary lifestyles, and imbalanced diets. While mouse models have been refined to capture the multifactorial interplay driving disease progression and are still essential for drug development, no single model fully mirrors the human condition. Histological assessment remains an essential tool for MASLD staging, in both humans and mouse models. However, imaging is increasingly emerging as an important complementary technique to non-invasively investigate MASLD. Conclusions: Mouse models are essential to address specific mechanistic and therapeutic questions, but understanding of their limitations and strengths is crucial for translational research. Integrating phenotype-driven approaches in both humans and mice, combining traditional histology, quantitative imaging, and metabolic profiling, as well as longitudinal, combined, and humanized preclinical models, will enhance translational alignment and accelerate the development of therapies for MASLD. Full article

Healthcare quality systems have evolved from narrow inspection and compliance mechanisms into broader, multi-level architectures that combine standards, measurement, organizational learning, patient safety, equity and patient-reported outcomes. Yet the field remains fragmented, with substantial variation in how quality is defined, measured and operationalized across countries and healthcare settings. This narrative review synthesizes major international quality systems and frameworks used in healthcare delivery, examines principal methods for evaluating and improving quality, and critically discusses organizational and policy conditions associated with successful implementation. A purposive review of the seminal conceptual literature and authoritative documents from major international organizations was undertaken to identify cross-cutting themes relevant to hospitals, ambulatory care and health systems. The review shows that influential approaches—including the World Health Organization’s quality and patient safety frameworks, Joint Commission International accreditation, NCQA/HEDIS, the EFQM model, ISO-based management systems, AHRQ quality indicators and OECD performance initiatives such as PaRIS—should be viewed as complementary rather than competing models. Their effectiveness depends less on formal adoption alone than on leadership commitment, workforce engagement, data infrastructure, patient involvement and alignment with financing and regulation. Evidence is strongest for gains in standardization, safety processes, teamwork and selected efficiency outcomes; direct causal effects on patient outcomes remain less consistent, particularly when quality systems become compliance-driven or are insufficiently adapted to local context. Future healthcare quality systems should integrate equity, digital interoperability, AI-enabled learning capabilities, patient-reported measures and continuous improvement while reducing measurement burden and indicator proliferation. Full article

Medicinal plants have attracted increasing scientific interest due to the diversity of bioactive compounds reported across different species. They may represent complementary sources of bioactive compounds alongside conventional antimicrobials, which may pose risks to animal health and compromise treatment efficacy. Considering the importance of alternative compounds, we aimed to evaluate the antimicrobial activity in vitro of medicinal plants Stryphnodendron adstringens (Mart.) Coville, known as barbatimão, Baccharis crispa Spreng, known as carqueja and Azadirachta indica, known as neem. S. adstringens (Mart.) Coville and B. crispa Spreng were used as extract and obtained from plants collected in the municipality of Bambuí, state of Minas Gerais, Brazil. A. indica was evaluated as extract and oil, and the crushed leaves and oil were purchased from a commercial company. Antimicrobial activity was determined by the minimum bactericidal concentration (MBC) test-against Staphylococcus aureus, Streptococcus agalactiae, Streptococcus uberis, Escherichia coli, and Salmonella spp., isolated from bovine mastitis. The bacteria were submitted to the MBC test at concentrations of 100, 50, 25, 12.5, 6.25, 3.12, 1.56, 0.78, 0.39, 0.19 and 0.09 mg/mL. The bacteria evaluated were sensitive to most plant extracts for at least one of the concentrations evaluated, except for Gram-negative bacteria, Escherichia coli, and Salmonella spp. There was no activity of B. crispa Spreng extract and A. indica against E. coli and neither of B. crispa Spreng extract against Salmonella spp. even at the highest concentration evaluated. S. adstringens (Mart.) Coville was considered the extract with the highest activity against the bacteria evaluated and S. uberis the most susceptible to antimicrobial action. The results indicate detectable antimicrobial activity of the evaluated extracts and oil, suggesting their potential relevance as complementary sources of bioactive compounds for further investigation, rather than as direct alternatives to conventional antibiotic therapies. Paper spray mass spectrometry (PS-MS) was employed as an exploratory phytochemical screening approach, and all metabolite assignments reported herein should be regarded as tentative or putative annotations under the analytical conditions used, consistent with MSI Level 3 confidence. Full article

Background: Effective postprandial glycemic regulation is essential for preventing metabolic disorders such as type 2 diabetes. While pharmacological interventions like GLP-1 (Glucagon-Like Peptide-1) receptor agonists are effective, dietary strategies using low-digestible carbohydrates (LDCs) may offer a sustainable and complementary approach. Methods: Two human physiological investigations were conducted to evaluate the acute metabolic responses to allulose, 1-kestose, resistant maltodextrin (RD), and fructo-oligosaccharide powder (FOP), administered both in isolation and in conjunction with a reference meal (RM). Results: In Study 1, all tested LDCs elicited minimal plasma glucose responses when consumed alone. In Study 2, distinct metabolic benefits were observed depending on the type of LDCs. Allulose exhibited the strongest effects, significantly reducing postprandial glucose and insulin levels while increasing plasma GLP-1 concentrations. 1-Kestose exhibited significantly lower plasma glucose and insulin incremental area under the curve (iAUC) compared to RM alone, indicating improved glycemic regulation. RD significantly enhanced subjective satiety between 30 and 180 min post-consumption. These findings highlight that each LDC exerts unique physiological effects. Conclusions: Collectively, these results demonstrate that acute LDCs consumption distinctly regulates metabolic responses, supporting their application as functional ingredients in targeted nutritional strategies for managing glycemic and metabolic health. Full article

Accurate quantification of soil organic matter (SOM) and carbon content is critical for understanding climate change, evaluating soil health, supporting agricultural sustainability, and implementing carbon sequestration policies. For decades, classical analytical and statistical approaches have underpinned soil carbon assessment, but the emergence of machine learning (ML) techniques offers new opportunities to improve prediction accuracy, scalability, and efficiency. This review summarises the current knowledge on classical and ML-based approaches for analysing SOM and carbon content. We examine the strengths, limitations, and practical applications of conventional methods, including wet chemistry, dry combustion analysis, and geostatistical techniques, alongside modern ML approaches such as random forests (RFs), gradient boosting machines, neural networks, deep learning, and hybrid ML-geostatistical frameworks. Special emphasis is placed on comparative analysis across dimensions, including prediction accuracy, computational requirements, data availability needs, interpretability, uncertainty quantification, and scalability. Soil carbon stocks and dynamics are tightly regulated by indigenous soil microbial communities and their management-driven alterations, creating substantial biologically driven variation that remains difficult to capture with current modelling approaches. We therefore explore hybrid approaches that integrate classical pedological knowledge with ML capabilities. Finally, we discuss emerging challenges, future research directions, and the complementary role these approaches play in advancing soil carbon science. This review concludes that neither classical nor ML approaches alone are sufficient for accurate carbon assessment across diverse scales and environments. Instead, their strategic integration, combining classical mechanistic grounding alongside machine learning’s scalability, represents the most promising path toward realistic soil carbon evaluation for climate change mitigation and agricultural sustainability. Full article