Search Results (959)

Background: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) remains among the most complex procedures in interventional cardiology, with variable technical success and heterogeneous long-term outcomes. Conventional angiographic scores such as J-CTO and PROGRESS-CTO provide only modest predictive accuracy and neglect critical patient and operator-related factors. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools, capable of integrating multimodal data and offering enhanced diagnostic, procedural, and prognostic insights. Methods: We performed a structured narrative review of the literature between January 2010 and September 2025 using PubMed, Scopus, and Web of Science. Eligible studies were peer-reviewed original research, reviews, or meta-analyses addressing AI/ML applications in CTO PCI across imaging, procedural planning, and prognostic modeling. A total of 330 records were screened, and 33 studies met the inclusion criteria for qualitative synthesis. Results: AI applications in diagnostic imaging achieved high accuracy, with deep learning on coronary CT angiography yielding AUCs up to 0.87 for CTO detection, and IVUS/OCT segmentation demonstrating reproducibility > 95% compared with expert analysis. In procedural prediction, ML algorithms (XGBoost, LightGBM, CatBoost) outperformed traditional scores, achieving AUCs of 0.73–0.82 versus 0.62–0.70 for J-CTO/PROGRESS-CTO. Prognostic models, particularly CatBoost and neural networks, achieved AUCs of 0.83–0.84 for 5-year mortality in large registries (n ≈ 3200), surpassing regression-based methods. Importantly, comorbidities and functional status emerged as stronger predictors than procedural strategy. Future Directions: AI integration holds promise for real-time guidance in the catheterization laboratory, robotics-assisted PCI, federated learning to overcome data privacy barriers, and multimodality fusion incorporating imaging, clinical, and patient-reported outcomes. However, clinical adoption requires prospective multicenter validation, harmonization of endpoints, bias mitigation, and regulatory oversight. Conclusions: AI represents a paradigm shift in CTO PCI, providing superior accuracy over conventional risk models and enabling patient-centered risk prediction. With continued advances in federated learning, multimodality integration, and explainable AI, translation from research to routine practice appears within reach. Full article

Organoids are self-organizing three-dimensional (3D) cellular structures derived from stem cells. They can mimic the anatomical and functional properties of real organs. They have transformed in vitro disease modeling by closely replicating the structural and functional characteristics of human tissues. The complexity and variability of organoid-derived data pose significant challenges for analysis and clinical translation. Artificial Intelligence (AI) has emerged as a crucial enabler, offering scalable and high-throughput tools for interpreting imaging data, integrating multi-omics profiles, and guiding experimental workflows. This review aims to discuss how AI is reshaping organoid-based research by enhancing morphological image analysis, enabling dynamic modeling of organoid development, and facilitating the integration of genomics, transcriptomics, and proteomics for disease classification. Moreover, AI is increasingly used to support drug screening and personalize therapeutic strategies by analyzing patient-derived organoids. The integration of AI with organoid-on-chip systems further allows for real-time feedback and physiologically relevant modeling. Drawing on peer-reviewed literature from the past decade, Furthermore, CNNs have been used to analyze colonoscopy and histopathological images in colorectal cancer with over 95% diagnostic accuracy. We examine key tools, innovations, and case studies that illustrate this evolving interface. As this interdisciplinary field matures, the future of AI-integrated organoid platforms depends on establishing open data standards, advancing algorithms, and addressing ethical and regulatory considerations to unlock their clinical and translational potential. Full article

Circulating nucleic acids, particularly those associated with extracellular vesicles (EVs), represent a promising class of molecular biomarkers in liquid biopsy for ‘non-invasive’ disease diagnostics, for better prognosis, and for therapeutic monitoring. However, the translation of this new circulating biomarker source into clinical practice is mostly hindered by methodological variability and a lack of standardization across the analytical workflow. This article highlights the implementation of international academic guidelines, such as Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) and Minimal Information for Studies of Extracellular Vesicles (MISEV), in the entire analytical procedure in promoting the integrity, reproducibility, and validity of EV-associated nucleic acid markers in molecular diagnostics. By standardizing the liquid biopsy workflow from tissue sampling up to data analysis and statistics, these established guidelines lay the necessary scientific basis for a robust, reproducible, reliable, and valid RNA and DNA biomarker discovery in EVs. The ultimate goal is the successful implementation of the developed biomarker signature into the clinical diagnostic routine, but this requires further rounds of rigorous validation. The regularly updated guidelines should not be seen as optional recommendations, but more like an essential pillars of scientific rigor and standardization in order to achieve better and biological meaningful biomarker results in liquid biopsy. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) enables the large-scale collection of ecological data, yet translating this dynamic sensor data into engaging, personalized public experiences remains a significant challenge. Existing solutions fall short: static exhibitions lack adaptability, while general-purpose LLM agents struggle with real-time responsiveness and reliability. To address this, we introduce CurationAgent, a novel intelligent agent built upon the State-Gated Agent Architecture (SGAA). Its core innovation is an advanced hybrid curation pipeline that synergizes Retrieval-Augmented Generation (RAG) for broad semantic recall with an Intent-Driven Curation (IDC) Funnel for precise intent formalization and narrative synthesis. This hybrid model robustly translates user intent into a curated, multi-modal narrative. We validate this framework in a proof-of-concept virtual exhibition of the Lalu Wetland’s biodiversity. Our comprehensive evaluation demonstrates that CurationAgent is significantly more responsive (1512 ms vs. 4301 ms), reliable (95% vs. 57% task success), and precise (85.5% vs. 52.7% query precision) than standard agent architectures. Furthermore, a user study with 27 participants confirmed our system leads to measurably higher user engagement. This work contributes a robust and responsive agent architecture that validates a new paradigm for interactive systems, shifting from passive information retrieval to active, partnered experience curation. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

Chronic kidney disease (CKD) affects nearly 850 million people worldwide, and most patients with kidney failure are treated with kidney replacement therapy. Despite technological progress, venous congestion remains a major determinant of morbidity and mortality, and is often underdetected by conventional tools such as clinical evaluation, weight changes, blood pressure measurement, or bioimpedance. Point-of-care ultrasonography (PoCUS) has transformed this diagnostic landscape by providing real-time, physiology-based insights into both left- and right-sided filling pressures. In dialysis care, multiple or confluent B-lines and subtle pleural irregularities suggest elevated pulmonary capillary wedge pressure, while a dilated inferior vena cava (IVC) with reduced collapsibility and increased portal vein pulsatility indicate elevated right atrial pressures. Integrating these sonographic findings into a multiparametric assessment that also includes clinical assessment, bioimpedance, and biosensor feedback enhances diagnostic sensitivity and refines fluid management. Advanced practice nurses (APNs) trained in PoCUS can perform focused examinations of the lungs, IVC, portal venous system, arteriovenous access, and skeletal muscle, translating ultrasound findings into physiological interpretations that guide individualized ultrafiltration strategies and patient care. Nutritional ultrasound (NUS) further complements congestion assessment by quantifying muscle mass and quality, linking nutritional reserve and functional status with hemodynamic tolerance. The implementation of structured education, competency-based training, and standardized scanning protocols allows nurses to incorporate these techniques safely and reproducibly into daily dialysis workflows. By integrating PoCUS and NUS within interdisciplinary decision-making, nursing practice evolves from procedural to diagnostic, supporting early identification of congestion, protection of vascular access, and detection of malnutrition. This multiparametric, physiology-guided approach exemplifies the concept of precision nursing, where patient evaluation becomes continuous, individualized, and grounded in real-time pathophysiological insight. Full article

The complexity of stroke pathophysiology, involving intricate neurovascular interactions and dynamic cellular responses, has long challenged the development of effective preclinical models. Traditional 2D cultures and animal models often fail to fully recapitulate human-specific features, limiting translational success. Emerging 3D systems, particularly brain assembloids and organ-on-a-chip platforms, are offering new opportunities to create more physiologically relevant stroke models. Assembloids, which integrate multiple brain-region-specific organoids, enable the study of interregional connectivity and complex cellular responses under ischemic conditions. Organ-on-a-chip platforms, by mimicking key tissue interfaces such as the blood–brain barrier and incorporating controlled fluid dynamics, enable a dynamic and highly customizable microenvironment with real-time monitoring capabilities. This review introduces and characterizes these two cutting-edge platforms (assembloids and organ-on-chip technologies), exploring their potential in stroke research while also discussing current challenges that need to be addressed for their broader adoption in translational applications. Full article

Installation in sand is sensitive to its evolving pore structure, yet design models rarely update permeability for real-time fabric changes. This study tracks the stress-dependent pore size distribution of coarse sand under laterally constrained compression using high-resolution X-ray nano-CT. Scans taken at six axial stress levels show that the distribution shifts toward smaller radii while keeping its log-normal shape. A single shifting factor, defined as the current median radius normalized by the initial value, captures this translation. The factor decays with axial stress according to a power law, and the exponent as well as the reference pressure are calibrated from void ratio data. The resulting closed-form expression links mean effective stress to pore radius statistics without extra fitting once the compressibility constants are known. This quantitative relation between effective stress and pore size distribution has great potential to be embedded into coupled hydro-mechanical solvers, enabling engineers to refresh hydraulic permeability at every computation step, improving predictions of excess pore pressure and soil resistance during suction anchor penetration for floating wind foundations. Full article

Financial time series are heterogeneous, nonstationary, and dispersed across institutions that cannot share raw data. While federated learning enables collaborative modeling under privacy constraints, fixed architectures struggle to accommodate cross-market drift and device-resource diversity; conversely, existing neural architecture search techniques presume centralized data and typically ignore communication, latency, and privacy budgets. This paper introduces FedRegNAS, a regime-aware federated NAS framework that jointly optimizes forecasting accuracy, communication cost, and on-device latency under user-level $(\epsilon ,\delta )$-differential privacy. FedRegNAS trains a shared temporal supernet composed of candidate operators (dilated temporal convolutions, gated recurrent units, and attention blocks) with regime-conditioned gating and lightweight market-aware personalization. Clients perform differentiable architecture updates locally via Gumbel-Softmax and mirror descent; the server aggregates architecture distributions through Dirichlet barycenters with participation-weighted trust, while model weights are combined by adaptive, staleness-robust federated averaging. A risk-sensitive objective emphasizes downside errors and integrates transaction-cost-aware profit terms. We further inject calibrated noise into architecture gradients to decouple privacy leakage from weight updates and schedule search-to-train phases to reduce communication. Across three real-world equity datasets, FedRegNAS improves directional accuracy by 3–7 percentage points and Sharpe ratio by 18–32%. Ablations highlight the importance of regime gating and barycentric aggregation, and analyses outline convergence of the architecture mirror-descent under standard smoothness assumptions. FedRegNAS yields adaptive, privacy-aware architectures that translate into materially better trading-relevant forecasts without centralizing data. Full article

Industrial deliverables in the AEC/FM sector are increasingly specified, validated, and governed by open standards. However, the machine-readable delivery specifications rarely propagate intact into the real-time collaborative 3D scene descriptions required by digital twins, XR, large-scale simulation, and visualization. This paper proposes a pipeline that transforms industrial deliverables into semantically faithful, queryable, and render-ready open scene descriptions. Unlike existing workflows that focus on geometric translation via connectors or intermediate formats, the proposed pipeline aligns defined delivery specifications with schema-aware USD composition so that contractual semantics remain executable in the scene. The pipeline comprises delivery specification, which records required objects, attributes, and provenance as versioned rule sets; semantically bound scene realization, which builds an open scene graph that preserves spatial hierarchy and identifiers, while linking rich properties through lightweight references; and interactive sustainment, which lets multiple engines render, analyze, and update the scene while allowing rules to be re-applied at any time. It presents a prototype and roadmap that make open scene description a streaming-ready execution layer for building deliverables, enabling consistent semantics, and reuse across diverse 3D engines. Full article

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain’s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen’s kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment. Full article

With the rapid development of medical imaging technology, the early diagnosis and treatment of heart disease have been significantly improved. Cardiac CT (Computed Tomography) and ultrasound images are often used in combination to provide more comprehensive information on cardiac structure and function due to their respective advantages and limitations. However, due to the significant differences in imaging principles, resolutions, and viewing angles between these two imaging modalities, how to effectively register cardiac CT and ultrasound images has become an important research topic in imaging and clinical applications. This article summarizes the research progress of cardiac CT and ultrasound image registration, and analyzes the existing registration methods and their advantages and disadvantages. Firstly, this article summarizes traditional registration methods based on image intensity, feature points, and regions, and explores the application of rigid and non-rigid registration algorithms. Secondly, in view of common challenges in cardiac CT and ultrasound image registration, such as image noise, deformation, and differences in imaging time, this article discusses the recent advances in multimodal registration technology in cardiac imaging and forecasts the potential of deep learning methods in registration. In addition, this article also evaluates the application effects and limitations of these methods in clinical practice, and finally looks forward to the future development direction of cardiac image registration technology, especially its potential applications in personalized medicine and real-time monitoring. Through a comprehensive review of the current research status of cardiac CT and ultrasound image registration, this article provides a systematic theoretical framework for researchers in related fields and provides a reference for future technological breakthroughs and clinical translation. Full article

Smart vesicle therapeutics represent a transformative frontier in nanomedicine, offering precise, biocompatible, and adaptable platforms for drug delivery and theranostic applications. This review explores recent advances in the design and engineering of liposomes, niosomes, polymersomes, and extracellular vesicles (EVs), emphasizing their capacity to integrate therapeutic and diagnostic functions within a single nanoscale system. By tailoring vesicle size, composition, and surface chemistry, researchers have achieved improved pharmacokinetics, reduced immunogenicity, and fine-tuned control of drug release. Stimuli-responsive vesicles activated by pH, temperature, and redox gradients, or external fields enable spatiotemporal regulation of therapeutic action, while hybrid bio-inspired systems merge synthetic stability with natural targeting and biocompatibility. Theranostic vesicles further enhance precision medicine by allowing real-time imaging, monitoring, and adaptive control of treatment efficacy. Despite these advances, challenges in large-scale production, reproducibility, and regulatory standardization still limit clinical translation. Emerging solutions—such as microfluidic manufacturing, artificial intelligence-guided optimization, and multimodal imaging integration—are accelerating the development of personalized, high-performance vesicular therapeutics. Altogether, smart vesicle platforms exemplify the convergence of nanotechnology, biotechnology, and clinical science, driving the next generation of precision therapies that are safer, more effective, and tailored to individual patient needs. Full article

High-voltage direct-current (HVDC) systems are essential for large-scale renewable integration and asynchronous interconnection, yet their complex topologies and multi-type faults expose the limits of threshold- and signal-based diagnostics. These methods degrade under noisy, heterogeneous measurements acquired under dynamic operating conditions, resulting in poor adaptability, reduced accuracy, and high latency. To overcome these shortcomings, the synergistic use of knowledge graphs (KGs) and pre-trained models (PTMs) is emerging as a next-generation paradigm. KGs encode equipment parameters, protection logic, and fault propagation paths in an explicit, human-readable structure, while PTMs provide transferable representations that remain effective under label scarcity and data diversity. Coupled within a perception–cognition–decision loop, PTMs first extract latent fault signatures from multi-modal records; KGs then enable interpretable causal inference, yielding both precise localization and transparent explanations. This work systematically reviews the theoretical foundations, fusion strategies, and implementation pipelines of KG-PTM frameworks tailored to HVDC systems, benchmarking them against traditional diagnostic schemes. The paradigm demonstrates superior noise robustness, few-shot generalization, and decision explainability. However, open challenges remain, such as automated, conflict-free knowledge updating; principled integration of electro-magnetic physical constraints; real-time, resource-constrained deployment; and quantifiable trustworthiness. Future research should therefore advance autonomous knowledge engineering, physics-informed pre-training, lightweight model compression, and standardized evaluation platforms to translate KG-PTM prototypes into dependable industrial tools for intelligent HVDC operation and maintenance. Full article

Contemporary biomedical diagnostics increasingly demand high sensitivity for pathogen detection and real-time health monitoring. In response to these requirements, screen-printed electrochemical sensors (SPEs) have emerged as a practical analytical platform owing to their low cost, portability, and compatibility with point-of-care and wearable systems. In the recent past, nanomaterials in two-dimensional format, especially MXenes, have gained much interest due to their high electrical conductivity, controllable surface chemistry, and biocompatibility, which can improve the performance and applicability of SPEs. The current review concentrates on the latest developments between 2020 and 2025, providing a critical assessment of research employing MXene-based nanomaterials for the modification and development of screen-printed electrode platforms. We provide an overview of fabrication techniques, printing methods, and surface modification methods, and proceed with an analysis of the electrochemical performance of MXenes and MXene-based heterostructures. Lastly, contemporary issues are considered, and opinions are suggested to facilitate the translation of MXene-functionalized SPEs to real biomedical diagnosis solutions. Full article