Search Results (1,212)

Rare vascular diseases such as hereditary haemorrhagic telangiectasia (HHT) represent a big challenge in biomedicine: complex pathomechanisms, limited patient material, and fragmented research communities slow down therapeutic progress. We argue that two elements are pivotal to bypass this problem. First, genuine partnership with patients—ranging from biospecimen donation to agenda setting—can unlock critical resources and align research with real-world needs. Second, molecular intersections between HHT and related pathologies call for coordinated, cross-disease programmes rather than isolated efforts. Recent multi-stakeholder gatherings hosted by patient organisations in Germany and elsewhere, such as the Second Scientific Symposium by the German HHT self-help group (Morbus Osler Selbsthilfe e.V.) in May 2025, have shown that when clinicians, basic scientists from different disciplines, and affected families co-design research questions, novel in vitro models can be generated more accurately, and pragmatic clinical trials emerge. Here, we outline actual opportunities for patient-integrated cellular model systems, shared biobanking, and comparative approaches across vascular malformation syndromes. In our opinion, letting informed and well-organised patient communities assemble such meetings opens unique opportunities twofold: on the one hand, the field can finally break out of its disease-specific silos; on the other hand, the development of novel HHT therapies could be accelerated by learning from progress in related pathologies. Full article

The presence of subtle mucosal abnormalities makes small bowel Crohn’s disease (SBCD) and other gastrointestinal lesions difficult to detect, as these features are often very subtle and can closely resemble other disorders. Although the Kvasir and Esophageal Endoscopy datasets offer high-quality visual representations of various parts of the GI tract, their manual interpretation and analysis by clinicians remain labor-intensive, time-consuming, and prone to subjective variability. To address this, we propose a generalizable ensemble deep learning framework for gastrointestinal lesion detection, capable of identifying pathological patterns such as ulcers, polyps, and esophagitis that visually resemble SBCD-associated abnormalities. Further, the classical convolutional neural network (CNN) extracts shallow high-dimensional features; due to this, it may miss the edges and complex patterns of the gastrointestinal lesions. To mitigate these limitations, this study introduces a deep learning ensemble framework that combines the strengths of EfficientNetB5, MobileNetV2, and multi-head self-attention (MHSA). EfficientNetB5 extracts detailed hierarchical features that help distinguish fine-grained mucosal structures, while MobileNetV2 enhances spatial representation with low computational overhead. The MHSA module further improves the model’s global correlation of the spatial features. We evaluated the model on two publicly available DBE datasets and compared the results with four state-of-the-art methods. Our model achieved classification accuracies of 99.25% and 98.86% on the Kvasir and Kaither datasets. Full article

Background/Objectives: Inflammatory bowel disease (IBD), including Crohn’s Disease and Ulcerative Colitis, requires complex treatment and active patient participation. Health literacy (HL), defined as the ability to access, understand, and apply health information, is a key factor influencing adherence, self-management, and quality of life in chronic illness. However, evidence regarding HL in IBD remains limited and fragmented. This systematic review aimed to synthesize existing literature on HL in IBD, exploring its impact on outcomes, as well as its predictors and barriers. Methods: A systematic search of PubMed, Scopus, CINAHL, and Cochrane Library identified studies published between January 2000 and August 2025 in English or Italian. Eligible studies examined HL among adults with IBD and its associations with clinical, behavioral, or psychosocial outcomes. Methodological quality was assessed using the QuADS tool. Due to heterogeneity across studies, a narrative synthesis was conducted. Results: Seventy studies were included, comprising observational, qualitative, mixed-methods, and interventional designs. Higher HL was consistently associated with better treatment adherence, self-management, communication with healthcare providers, and quality of life. Conversely, low HL was linked to poor adherence, greater disease activity, and lower psychological well-being. Predictors of low HL included older age, lower education, minority status, and socioeconomic disadvantage. Barriers included inadequate communication, lack of tailored information, cultural and linguistic challenges, and the digital divide. Interventions such as structured education, telemedicine, and digital tools showed potential to improve HL and patient engagement. Conclusions: HL is a crucial determinant in IBD management. Enhancing HL through integrated clinical, educational, and digital strategies is essential to improve outcomes and reduce health disparities. Full article

Generative artificial intelligence (AI) is increasingly explored in counselor education, yet its pedagogical implications remain underexamined. This study investigated counselor trainees’ experiences using ChatGPT (GPT-4o) as a simulated client for role-play practice, aiming to assess its potential benefits and limitations as a supplemental training tool. Using qualitative content analysis, AI-simulated counseling session transcripts were coded based on dimensions such as authenticity, emotional expression, consistency, self-awareness, and cultural dynamics. Additionally, a focus group interview provided insights into trainees’ perceptions. Findings indicate that AI simulations offered a psychologically safe, flexible environment for practicing counseling skills, reducing performance anxiety, and fostering confidence before working with real clients. Participants emphasized the importance of detailed prompts to enhance realism and complexity, while noting limitations such as overly agreeable responses, lack of emotional nuance, and cultural neutrality unless explicitly prompted. Overall, trainees viewed AI as a valuable supplement rather than a replacement for live practice. These results suggest that generative AI can enhance experiential learning when integrated thoughtfully with structured guidance, ethical oversight, and culturally responsive design. Future research should explore strategies to improve authenticity and emotional depth in AI simulations to better support counselor competency development. Full article

China possesses abundant tight conglomerate oil resources. However, these reservoirs are typically characterized by low porosity and permeability, high clay mineral content, and complex pore structures, resulting in poor performance of conventional waterflooding development. Challenges including insufficient energy replenishment and high flow resistance ultimately lead to low oil recovery factors. This study systematically investigates surfactant-assisted CO₂ huff-n-puff (SA-CO₂-HnP) for enhanced oil recovery in tight conglomerate reservoirs. For a tight conglomerate reservoir in a Xinjiang block, a fully implicit, multiphase, multicomponent dual-porosity numerical model was established. By integrating pore–throat distributions acquired through high-pressure mercury intrusion with a self-developed MATLAB PVT package, nanoconfinement-induced shifts in the phase envelope were rigorously embedded into the simulation framework. The calibrated model was subsequently employed to conduct a comprehensive sensitivity analysis, quantitatively delineating the influence of petrophysical, completion, and operational variables on production performance. Simulation results demonstrate that compared to conventional CO₂ huff-n-puff, the addition of surfactants increases the cumulative recovery factor by 3.5 percentage points over a 20-year production period. The enhancement mechanisms primarily include reducing CO₂–oil interfacial tension (IFT) and minimum miscibility pressure (MMP), improving reservoir wettability, and promoting CO₂ dissolution and diffusion in crude oil. Sensitivity analysis reveals that injection duration, injection pressure, and injection rate significantly influence recovery efficiency, while soaking time exhibits relatively limited impact. Moreover, an optimal surfactant concentration (0.0003 mole fraction) exists; excessive concentrations lead to diminished enhancement effects due to competitive adsorption and pore blockage. This study demonstrates that SA-CO₂-HnP technology offers favorable economic viability and operational feasibility, providing theoretical foundation and parameter optimization guidance for efficient tight conglomerate oil reservoir development. Full article

Iron ore image classification is essential for achieving high production efficiency and classification precision in mineral processing. However, real industrial environments face classification challenges due to small samples, inter-class similarity, and on-site noise. Existing methods are limited by single-view approaches that provide insufficient representation, difficulty in achieving adaptive balance between performance and complexity through manual or fixed feature selection and fusion, and susceptibility to overfitting with poor robustness under small sample conditions. To address these issues, this paper proposes the evolutionary deep fusion framework EDF-NSDE. The framework introduces multi-view feature extraction that combines lightweight and classical convolutional neural networks to obtain complementary features. Additionally, it was utilized to design evolutionary fusion that utilizes NSGA-II and differential evolution for multi-objective search to adaptively balance accuracy and model complexity while reducing overfitting and enhancing robustness through a generalization penalty and adaptive mutation. Furthermore, to overcome data limitations, we constructed a six-class dataset including hematite, magnetite, ilmenite, limonite, pyrite, and rock based on real production scenarios. The experimental results show that on our self-built dataset, EDF-NSDE achieves 84.86%/88.38% on original/augmented test sets, respectively, comprehensively outperforming other models. On a public seven-class mineral dataset, it achieves 92.51%, validating its generalization capability across different mineral types and imaging conditions. In summary, EDF-NSDE provides an automated feature fusion solution that achieves automated upgrading of the mineral classification process, contributing to the development of intelligent manufacturing technology and the industrial internet ecosystem. Full article

The principal challenge in the development of efficient porphyrin-based photosensitizers is the intrinsic aggregation-induced quenching effect, which significantly impairs the generation efficiency of singlet oxygen (¹O₂) in photodynamic therapy (PDT). This study addresses this limitation through a supramolecular approach grounded in host-guest chemistry. Partially methyl-substituted cucurbit[6]uril (HMeQ[6]) was selected as the macrocyclic host owing to its smaller portal size and larger outer diameter, features that facilitate both strong binding affinity and effective spatial isolation. A porphyrin derivative functionalized with two cationic arms (DPPY) was designed and synthesized as the guest molecule. The results derived from ¹H NMR titration and UV spectroscopy analyses demonstrate that, in aqueous solution, these components self-assemble via host-guest interactions to form a 2:1 stoichiometric supramolecular complex (DPPY@HMeQ[6]) with a binding constant of 2.11 × 10⁵ M⁻¹. TEM, AFM, and DLS analyses indicate that this complex further assembles into nanosheet structures with dimensions of approximately 100 nm. Spectroscopic analyses reveal that encapsulation by HMeQ[6] effectively inhibits π-π stacking aggregation of DPPY molecules, resulting in an approximate threefold increase in fluorescence intensity and an extension of fluorescence lifetime from 3.2 ns to 6.2 ns. Relative to free DPPY, the complex demonstrates a sixfold enhancement in ¹O₂ generation efficiency. Subsequently, 4T1 cells, derived from mouse triple-negative breast tumors, were selected as the experimental model. These cells exhibit high invasiveness and metastatic potential, thereby effectively recapitulating the pathological progression of human triple-negative breast cancer. In vitro cellular assays indicate efficient internalization of the complex by 4T1 cells, inducing a concentration-dependent increase in reactive oxygen species (ROS) and oxidative stress following light irradiation. The in vitro cytotoxicity of the supramolecular photosensitizer was assessed employing the CCK-8 assay and flow cytometry techniques. The half-maximal inhibitory concentration (IC₅₀) against cancer cells is 1.8 μM, with apoptosis rates reaching up to 65.3%, while exhibiting minimal dark toxicity. This study expands the potential applications of methyl-substituted cucurbiturils within functional supramolecular assemblies and proposes a viable approach for the development of efficient and activatable supramolecular photosensitizers. Full article

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

Deep Reinforcement Learning (DRL) has achieved remarkable success in optimizing complex control tasks; however, its opaque decision-making process limits accountability and erodes user trust in safety-critical domains such as autonomous driving and clinical decision support. To address this transparency gap, this study proposes a hybrid DRL framework that embeds explainability directly into the learning process rather than relying on post hoc interpretation. The model integrates symbolic reasoning, multi-head self-attention, and Layer-wise Relevance Propagation (LRP) to generate real-time, human-interpretable explanations while maintaining high control performance. Evaluated over 20,000 simulated episodes, the hybrid framework achieved a 91.9% task-completion rate, a 19.1% increase in user trust, and a 15.3% reduction in critical errors relative to baseline models. Human–AI interaction experiments with 120 participants demonstrated a 25.6% improvement in comprehension, a 22.7% faster response time, and a 17.4% lower cognitive load compared with non-explainable DRL systems. Despite a modest ≈4% performance trade-off, the integration of explainability as an intrinsic design principle significantly enhances accountability, transparency, and operational reliability. Overall, the findings confirm that embedding explainability within DRL enables real-time transparency without compromising performance, advancing the development of scalable, trustworthy AI architectures for high-stakes applications. Full article

Cold-formed thin-walled steel (CFS) members were widely used in steel structures but faced challenges in meeting bearing capacity and assembly efficiency requirements as single-limb members. To overcome the above limitations, a promising fold-fastened multi-cellular steel panel (FMSP) was proposed. The FMSP eliminated the need for discrete self-drilling screws, instead utilizing a continuous mechanical fold-fastened connection, which enhanced structural integrity and assembly efficiency. This approach also provided greater flexibility to meet the design requirements of complex structural configurations. This study investigated the flexural behaviors of panels—a key mechanical property governing their structural behavior. A bearing capacity test was conducted on five FMSP specimens, focusing on the failure modes, bending moment–deflection curves, deflection distributions under representative loading levels, and flexural bearing capacities of the specimens. Refined finite element models (FEMs) of the specimens were established, and the stress and deformation distributions were further studied. The comparison results showed that the numerical results were in good agreement with the experimental results. Finally, the parametric analysis was carried out, and the influence of key parameters on the flexural behavior was revealed. Analysis results demonstrated that doubling the steel plate thickness increased the flexural capacity by 207%, while a twofold increase in panel thickness resulted in a 123% improvement. In contrast, increasing the steel strength from 235 MPa to 460 MPa yielded only a 61% enhancement. This research laid a solid foundation for promoting the application and investigation of FMSPs, thus achieving high industrialization and meeting the requirements of complex structural design. Full article

For unmanned aerial vehicle (UAV) ground crew marshalling tasks, the accuracy of skeleton-based action recognition is often limited by the high similarity of motion patterns across action categories as well as variations in individual performance. To address this issue, we propose an adaptive refined graph convolutional network with enhanced features for action recognition. First, a multi-order and motion feature modeling module is constructed, which integrates joint positions, skeletal structures, and angular encodings for multi-granularity representation. Static-domain and dynamic-domain features are then fused to enhance the diversity and expressiveness of the input representations. Second, a data-driven adaptive graph convolution module is designed, where inter-joint interactions are dynamically modeled through a learnable topology. Furthermore, an adaptive refinement feature activation mechanism is introduced to optimize information flow between nodes, enabling fine-grained modeling of skeletal spatial information. Finally, a frame-index semantic temporal modeling module is incorporated, where joint-type semantics and frame-index semantics are introduced in the spatial and temporal dimensions, respectively, to capture the temporal evolution of actions and comprehensively exploit spatio-temporal semantic correlations. On the NTU-RGB+D 60 and NTU-RGB+D 120 benchmark datasets, the proposed method achieves accuracies of 89.4% and 94.2% under X-Sub and X-View settings, respectively, as well as 81.7% and 83.3% on the respective benchmarks. On the self-constructed UAV Airfield Ground Crew Dataset, the proposed method attains accuracies of 90.71% and 96.09% under X-Sub and HO settings, respectively. Environmental robustness experiments demonstrate that under complex environmental conditions including illumination variations, haze, rain, shadows, and occlusions, the adoption of the Test + Train strategy reduces the maximum performance degradation from 3.1 percentage points to within 1 percentage point. Real-time performance testing shows that the system achieves an end-to-end inference latency of 24.5 ms (40.8 FPS) on the edge device NVIDIA Jetson Xavier NX, meeting real-time processing requirements and validating the efficiency and practicality of the proposed method on edge computing platforms. Full article

Background/Objectives: General-self efficacy (GSE) is a substantial element during pregnancy that promotes healthy decision-making and prevents complications. Information on predictive factors of GSE among pregnant women is limited. This study aimed to assess the GSE among pregnant women and identify its relationship with family quality of life (FQOL) domains in a lower-middle-income community. Methods: This cross-sectional analytical study was conducted in nine antenatal care centers from July 2024 to March 2025. Pregnant women were enrolled through the multiple stage sampling method. GSE in pregnant women was assessed using the General Self-efficacy Scale. FQOL was assessed using the Beach Center Family Quality of Life Scale. Univariable and multivariable linear regression analyses were performed to assess predictors of GSE among pregnant women. Results: A total of 417 pregnant women participated in the study. The prevalence of low GSE was 12.2%. Multivariable linear regression showed that older age (p = 0.02), rural area (p = 0.007), and planned pregnancy (p = 0.03) were predictors of GSE among pregnant women. The total score of FQOL (p = 0.0001), parenting (p = 0.004), and material well-being (p = 0.043) were positive determinant factors of GSE in pregnant women who have at least one child. Conclusions: The prevalence of low general self-efficacy (GSE) among pregnant women was notably high, particularly among those with at least one child. Education regarding family planning, parenting, and financial management through multidisciplinary, family-centered care teams is essential to address the complex needs of expectant families. Full article

Multi-target drug design represents a paradigm shift in tackling the complexity and heterogeneity of diseases such as cancer. Conventional single-target therapies frequently face limitations due to network redundancy, pathway compensation, and adaptive resistance mechanisms. In contrast, deep generative models, empowered by advanced artificial intelligence algorithms, provide scalable and versatile platforms for the de novo generation and optimization of small molecules with activity across multiple therapeutic targets. This review provides a comprehensive overview of the recent landscape of AI-driven deep generative modeling for multi-target drug discovery, highlighting breakthroughs in model architectures, molecular representations, and goal-directed optimization strategies. We also examine the emergence of self-improving learning systems, closed-loop frameworks that iteratively refine molecular candidates through integrated feedback, as a transformative approach to adaptive drug design. Finally, key challenges, current limitations, and emerging trends are discussed to guide the evolution of next-generation intelligent and autonomous drug discovery pipelines for multi-target therapeutics. Full article

Neuronal firing behaviors are fundamental to brain information processing, and their abnormalities are closely associated with neurological disorders. This study conducts a comprehensive bifurcation and firing-behavior analysis of an improved Tabu Learning neuron model using a semi-analytical discrete implicit mapping framework. First, a discrete implicit mapping is constructed for the Tabu Learning neuron, enabling high-precision localization of stable and unstable periodic orbits within chaotic regimes and overcoming the limitations of conventional time-domain integration. Second, an eigenvalue-centered analysis is used to classify bifurcation types and stability, summarized in explicit bifurcation tables that reveal self-similar offset bifurcation routes, coexisting periodic and chaotic attractors, and chaotic bubbling firing patterns. Third, the proposed neuron model and its discrete mapping are implemented on an FPGA platform, where hardware experiments faithfully reproduce the analytically predicted stable and unstable motions, thereby tightly linking theoretical analysis and digital neuromorphic hardware. Overall, this work establishes a unified analytical–numerical–hardware framework for exploring complex neuronal dynamics and provides a potential basis for neuromodulation strategies and neuromorphic computing system design. Full article

In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate knowledge extraction and generate optimized, resilient, operational plans. A synergistic multi-agent framework is introduced that transforms unstructured documents into a structured knowledge base using a self-validating pipeline. This validated knowledge feeds a scheduling engine that combines multi-objective optimization with discrete-event simulation to generate robust, capacity-aware plans. The framework was validated on a complex maritime case study. The system successfully constructed a high-fidelity knowledge base from unstructured manuals and the scheduling engine produced a viable, capacity-aware operational plan for 118 interventions. The optimized plan respected all daily (6) and weekly (28) task limits, executing 64 tasks on their nominal date, bringing 8 forward, and deferring 46 by an average of only 2.0 days (95th percentile 4.8 days) to smooth the workload and avoid bottlenecks. An interactive user interface with a chatbot and planning calendar provides verifiable “plan-to-page” traceability, demonstrating a novel, end-to-end synthesis of document intelligence, agentic AI, and simulation to unlock strategic value from legacy documentation in high-stakes environments. Full article