Search Results (2,419)

The epicardium plays a pivotal role in heart development, regeneration, and disease response through its contributions to multiple cardiac lineages and its dynamic paracrine signaling. Recent advances in lineage tracing, single-cell technologies, and, particularly, human pluripotent stem cell (hPSC)-derived cardiac organoid models have illuminated the cellular heterogeneity, developmental plasticity, and intercellular crosstalk of epicardial cells with other cardiac cell types. These models have revealed conserved and divergent mechanisms of epicardial function across species, offering new insights into epicardial–myocardial–endothelial–immune interactions and the regulation of cardiac repair. This review highlights recent key findings from developmental and regenerative studies, integrating them with emerging data from human cardiac organoids to provide an updated framework for understanding epicardial biology and its therapeutic potential. Full article

Extended Reality (XR) technologies—including Virtual Reality, Augmented Reality, and Mixed Reality—are increasingly applied in the food industry to simulate sensory environments, support education, and influence consumer behavior, while robotics addresses labor shortages, hygiene, and efficiency in production. This review uniquely synthesizes their convergence through digital twin frameworks, combining XR’s immersive simulations with robotics’ precision and scalability. A systematic literature review and keyword co-occurrence analysis of over 800 titles revealed research clusters around consumer behavior, nutrition education, sensory experience, and system design. In parallel, robotics has expanded beyond traditional pick-and-place tasks into areas such as precision cleaning, chaotic mixing, and digital gastronomy. The integration of XR and robotics offers synergies including risk-free training, predictive task validation, and enhanced human–robot interaction but faces hurdles such as high hardware costs, motion sickness, and usability constraints. Future research should prioritize interoperability, ergonomic design, and cross-disciplinary collaboration to ensure that XR–robotics systems evolve not merely as tools, but as a paradigm shift in redefining the human–food–environment relationship. Full article

Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research. Full article

The female reproductive system represents a highly complex regulatory network governing critical physiological functions, encompassing reproductive capacity and endocrine regulation that maintains female physiological homeostasis. The in vitro simulation system provides a novel tool for biomedical research and can be used as physiological and pathological models to study the female reproductive system. Recent advances in this technology have evolved from 2D and 3D printing to organ-on-a-chip (OOC) and microfluidic systems, which has emerged as a transformative platform for modeling the female reproductive system. These microphysiological systems integrate microfluidics, 3D cell culture, and biomimetic scaffolds to replicate key functional aspects of reproductive organs and tissues. They have enabled precise simulation of hormonal regulation, embryo-endometrium interactions, and disease mechanisms such as endometriosis and gynecologic cancers. This review highlights the current state of female reproductive OOCs, including ovary-, uterus-, and fallopian tube-on-a-chip system, their applications in assisted reproduction and disease modeling, and the technological hurdles to their widespread application. Though significant barriers remain in scaling OOCs for high-throughput drug screening, standardizing protocols for clinical applications, and validating their predictive value against human patient outcomes, OOCs have emerged as a transformative platform to model complex pathologies, offering unprecedented insights into disease mechanisms and personalized therapeutic interventions. Future directions, including multi-organ integration for systemic reproductive modeling, incorporation of microbiome interactions, and clinical translation for mechanisms of drug action, will facilitate unprecedented insights into reproductive physiology and pathology. Full article

Multidimensional force sensors are key devices capable of simultaneously perceiving and analyzing force in multiple directions (normally triaxial forces). They are designed to provide intelligent systems with skin-like precision in environmental interaction, offering high sensitivity, spatial resolution, decoupling capability, and environmental adaptability. However, the inherent complexity of tactile information coupling, combined with stringent demands for miniaturization, robustness, and low cost in practical applications, makes high-performance and reliable multidimensional sensing and decoupling a major challenge. This drives ongoing innovation in sensor structural design and sensing mechanisms. Various structural strategies have demonstrated significant advantages in improving sensor performance, simplifying decoupling algorithms, and enhancing adaptability—attributes that are essential in scenarios requiring fine physical interactions. From this perspective, this article reviews recent advances in multidimensional force sensing technology, with a focus on the operating principles and performance characteristics of sensors with different structural designs. It also highlights emerging trends toward multimodal sensing and the growing integration with system architectures and artificial intelligence, which together enable higher-level intelligence. These developments support a wide range of applications, including intelligent robotic manipulation, natural human–computer interaction, wearable health monitoring, and precision automation in agriculture and industry. Finally, the article discusses remaining challenges and future opportunities in the development of multidimensional force sensors. Full article

Extended reality (XR) technologies—including augmented reality (AR), virtual reality (VR), mixed reality (MR), and desktop virtual reality (dVR)—are rapidly advancing STEM education by providing immersive and interactive learning experiences. Despite their potential, many XR applications lack consistent design grounded in human–computer interaction (HCI), leading to challenges in usability, engagement, and learning outcomes. Through a comprehensive analysis of 50 peer-reviewed studies, this paper reveals both strengths and limitations in current implementations and suggest improvements for reducing cognitive load and enhancing engagement. To support this analysis, we draw briefly on a dual-phase learning model (L1–L2), which distinguishes between interface learning (L1) and conceptual or procedural learning (L2). By aligning theoretical insights with practical HCI strategies, the discussions from this study are intended to offer potentially actionable insights for educators and developers on XR design for STEM education. Based on a detailed analysis of the articles, this paper finally makes recommendations to educators and developers on important considerations and limitations concerning the optimal use of XR technologies in STEM education. The guidelines for design proposed by this review offer directions for developers intending to build XR frameworks that effectively improve presence, interaction, and immersion whilst considering affordability and accessibility. Full article

Non-thermal plasma has attracted strong interest in medicine and agriculture due to its ability to generate reactive oxygen and nitrogen species (RONS). These species can stimulate wound healing and seed germination, but at higher levels they induce DNA damage—useful in cancer therapy but harmful when healthy cells must be preserved. Direct study of DNA damage in cells is difficult because of repair processes and protective barriers. To address this, we applied a dual-model system combining plasmid DNA and human lymphocytes exposed to plasma from the RPS40 device. Using selective scavengers, we identified hydroxyl radicals, ozone, and reactive nitrogen species as key mediators of DNA strand breaks and structural changes. Our results support a mechanistic model in which long-lived plasma-derived species (NOx, ozone, acids) dissolve in water and subsequently generate short-lived radicals such as hydroxyl radicals and peroxynitrite. These reactive molecules then directly attack DNA. This integrated approach—linking plasmid and cellular assays with scavenger-based identification of RONS—offers a novel and cost-effective method for dissecting plasma–DNA interactions. The findings provide mechanistic insight into how plasma-activated water damages DNA, guiding the safer and more effective application of plasma technologies in biomedical and agricultural contexts. Full article

The sensory glove (also known as data or instrumented glove) plays a key role in measuring and tracking hand dexterity. It has been adopted in a variety of different domains, including medical, robotics, virtual reality, and human–computer interaction, to assess hand motor skills and to improve control accuracy. However, no particular technology has been established as the most suitable for all domains, so that different sensory gloves have been developed, adopting different sensors mainly based on optic, electric, magnetic, or mechanical properties. This work investigates the performances of the MANUS Quantum sensory glove that sources an electromagnetic field and measures its changing value at the fingertips during fingers’ flexion. Its performance is determined in terms of measurement repeatability, reproducibility, and reliability during both quasi-static and dynamic hand motor tests. Full article

Artificial intelligence (AI) is an innovation in science and technology designed to make computer systems capable of imitating human intellectual abilities. In the legal world, the advancement of artificial intelligence (AI) often causes debate, which has changed the way humans work, interact, and make decisions, one of which is whether AI can replace the role of judges. The purpose of this study is to determine the role of AI in the world of justice and whether AI-based court decisions can provide substantive justice for justice seekers. This study is based on normative legal research that uses a statute and conceptual approach. The results indicate that the use of AI must be carried out carefully, considering ethical aspects, and maintaining the role of judges in deciding cases based on deep legal and moral considerations. The system that uses AI in the decisions of the Panel of Judges must be able to balance efficiency and justice, ensuring that human rights, legal principles, and applicable social and cultural values are maintained. Full article

Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive capacity and translational relevance in infectious disease research. A structured literature search was conducted across PubMed, Scopus, and Web of Science (2010–2025), applying defined inclusion and exclusion criteria. Evidence synthesis highlights imaging modalities, AI-driven phenotyping, and standardization strategies, supported by comparative analyses and quality considerations. Persistent challenges include variability in engraftment, lack of harmonized scoring systems, and ethical governance. We propose recommendations for standardized protocols, risk-of-bias mitigation, and collaborative training frameworks to accelerate adoption of these technologies in translational medicine. Full article

With the rapid development of wearable devices and intelligent human–machine interaction technologies, the demand for high-precision pressure sensors has soared. Piezoresistive pressure sensors excel due to their simple structure, low cost, and high sensitivity, among which flexible piezoresistive pressure sensors based on porous polymers have become a research focus, thanks to their unique 3D porous structure and excellent performance. This review summarizes recent advances: it introduces key performance metrics and the piezoresistive sensing mechanism; outlines porous structure preparation methods (phase separation, 3D printing, electrospinning) with their principles, advantages, and limitations; examines conductive fillers (carbon-based, polymer, metal, MXene) with their properties and applications; and highlights flexible substrates (silicone, polyurethane, polyimide, natural polymers) in ensuring mechanical compliance and device integration. Studies show material innovation, structural optimization, and process improvement can significantly enhance sensor accuracy, stability, and durability, helping break traditional performance bottlenecks. Future prospects are broad in tactile sensing, biomedical monitoring, and human–machine interaction, providing references for related research and industrial development. Full article

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs. Full article

The increasing intersection of psychotherapy, cognitive science, neuroscience, and systems engineering beckons us to rethink what it means to talk the language of the human mind in the clinical setting. This position paper proposes the idea of entangled autopoiesis, a metatheoretical paradigm that addresses the mind and therapy not as linear processes but as self-organizing, adaptive processes enfolded across neural, cognitive, relational, and cultural domains. Psychotherapy, from this viewpoint, is less a corrective technique and more a zone of systemic integration, wherein resilience and meaning are co-created in the interaction of embodied brains, lived stories, and relational fields. Neuroscience informs us about plasticity and regulation; cognitive science emphasizes the embodied and extended nature of cognition; and systems engineering sheds light on feedback, emergence, and adaptive dynamics. Artificial intelligence appears as a double presence: as a metaphor for complexity and as a practical tool able to chart patterns below human sensibility. By adopting a complexity-aware epistemology, we advocate a relocation in clinical thinking—one recognizing the psyche as an autopoietic network, entangled with culture and technology and able to renew itself in therapeutic encounters. The implications for clinical methodology, therapist training, and future interdisciplinary research are discussed. Full article

Background/Objectives: With the rapid development of smart healthcare, medical records have shifted from a disease-centered to a patient-centered approach. However, traditional formats, such as narratives and tables, often make it challenging for physicians to quickly grasp a patient’s condition within a limited timeframe, potentially leading to diagnostic errors and a decline in the quality of care. Recently, advances in information visualization and 3D technology have led many medical institutions to employ charts and graphs or use 3D simulations of organs to support clinical practice and education. However, few have integrated 3D models into medical records for use during physician consultations. Methods: This study presents the development and evaluation of a novel web-based 3D EMR system that integrates real-time ICD-10 diagnostic code mapping with interactive 3D human body models, enabling physicians to visualize patient-specific anatomical and diagnostic information in a dynamic and context-aware manner. Results: We employed the System Usability Scale (SUS) to evaluate the system’s usability, conducting a satisfaction survey. Results from the survey indicate that participants rated the system highly in terms of ease of use, satisfaction, and efficiency, with an average SUS score of 70.42, reflecting usability between moderate and good. Comparative evaluations and future expansion plans are also discussed. Conclusions: These findings demonstrate that integrating a 3D human model into the medical record retrieval process significantly improves visualization and interactivity, meeting the needs of healthcare professionals and enhancing both their efficiency and patient satisfaction. Full article

Eye tracking, a fundamental process in gaze analysis, involves measuring the point of gaze or eye motion. It is crucial in numerous applications, including human–computer interaction (HCI), education, health care, and virtual reality. This study delves into eye-tracking concepts, terminology, performance parameters, applications, and techniques, focusing on modern and efficient approaches such as video-oculography (VOG)-based systems, deep learning models for gaze estimation, wearable and cost-effective devices, and integration with virtual/augmented reality and assistive technologies. These contemporary methods, prevalent for over two decades, significantly contribute to developing cutting-edge eye-tracking applications. The findings underscore the significance of diverse eye-tracking techniques in advancing eye-tracking applications. They leverage machine learning to glean insights from existing data, enhance decision-making, and minimize the need for manual calibration during tracking. Furthermore, the study explores and recommends strategies to address limitations/challenges inherent in specific eye-tracking methods and applications. Finally, the study outlines future directions for leveraging eye tracking across various developed applications, highlighting its potential to continue evolving and enriching user experiences. Full article