Search Results (909)

Organoid and organ-on-chip technologies are rapidly evolving platforms for viral research that integrate stem cell biology, tissue engineering, and microfluidics to recapitulate key structural, mechanical, biochemical, and cellular features of human and animal physiology. By incorporating multicellular organoids into perfused microfluidic systems, these models can provide complex, dynamic, and physiologically relevant micro-environments for investigating virus–host interactions that are difficult to capture in conventional two-dimensional cultures and static organoids. Controlled flow, shear stress, extracellular matrix organization, tissue–tissue interfaces, and multicellular signaling enable mechanistic investigation of viral infectivity, dissemination, tissue injury and immune activation. Integration of real-time imaging and biosensors further permits longitudinal monitoring of viral replication, host responses, and tissue integrity, expanding the potential of these platforms for antiviral drug discovery. Recent organoid-on-chip studies using brain, skin, vaginal, respiratory, and intestinal models have demonstrated how tissue architecture, mechanical forces, glycocalyx dynamics, and immune–stromal interactions influence viral tropism and pathogenesis. In this review, we provide a mechanistic and translational overview of organoid and organ-on-chip technologies for studying viral infections, with particular emphasis on models of herpes simplex virus (HSV)-mediated disease. We further examine advances in immune integration, multi-organ systems, biosensing, and computational approaches that are expanding the complexity and predictive potential of these models. Importantly, patient-derived organoids and organ-on-chip platforms can capture interindividual differences in viral susceptibility, host responses, and therapeutic efficacy, providing pharmaceutical research with more precise, patient-relevant data to support drug prioritization and precision antiviral medicine. Finally, we discuss key barriers to broader adoption, including organoid maturation, biological and technical variability, reproducibility, scalability, biosafety, cost, standardization, and regulatory validation. Collectively, these advances position organoid and organ-on-chip technologies as powerful human-relevant models that bridge reductionist in vitro systems and human disease, while continued optimization, standardization, and validation will be essential to realize their full potential for mechanistically informed antiviral discovery, therapeutic development, and precision medicine. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

Just-in-time adaptive interventions require timely and low-burden state estimation, while olfaction offers a programmable output channel with limited attentional demand. We describe a prototype architecture that links electroencephalography (EEG)-based emotion estimation to a six-channel odorant device and evaluate only the EEG sensing and decoding module. Forty EEG sessions from 39 adults were recorded with a 14-channel Emotiv EPOC X headset (128 Hz) during six standardized emotion-induction conditions. No odor was administered. Band-power, frontal alpha asymmetry (FAA) and global field power (GFP) were analyzed with rank-based repeated-measures tests and explicit multiple-comparison correction. Emotion decoding used subject-aware cross-validation. Frontal beta power, the beta/alpha ratio and GFP differed across conditions after false-discovery-rate correction, although effect sizes were small (Kendall’s W = 0.089–0.155). On-line affective metrics showed larger effects (W = 0.130–0.365). Six-class accuracy was 45.1% ± 13.2% within participants (n = 29; chance 16.7%; p < 10⁻⁸) and 23.1% across participants after per-subject normalization (macro-F1 = 0.23; permutation p = 0.005). FAA did not differ. Consumer-headset EEG contained person-specific information about laboratory-induced states, but performance was not sufficient to establish a clinically usable regulator. The results validate neither a complete closed loop nor olfactory efficacy; end-to-end latency, artifact and temporal robustness, chemical characterization and controlled odor-regulation effects require prospective evaluation. Full article

To address the challenges of complex multi-objective trade-offs, tightly coupled physical constraints in reservoir dam safety dispatching, and fulfill the significant cognitive gaps in human–machine interaction, a framework with four deep cognitive layers and a physical computation layer is proposed which integrates large language models (LLMs) with multi-agent collaboration. The framework stratifies cognitive intelligence into interface translation, strategic cognition, tactical reasoning, and operational understanding layers; performs computation in the physical computation layer; and achieves deep coupling among agents in different layers through the Blackboard information sharing mechanism. The physical computation layer consists of the gate-opening discharge, water-level storage capacity, runoff and inflow, downstream risk calculation agents and a Pareto multi-objective optimizer to realize non-dominated sorting of multi-dimensional objectives encompassing dam safety, ecological loss, downstream risk, and operational complexity. Illustrative case analysis indicates that this framework can effectively parse user requirements expressed in natural language, generate dispatching schemes conforming to physical constraints, achieve error control and quantify the downstream risk. This research provides a scalable framework for the implementation of intelligent reservoir dispatching and can enhance the intelligence of digital twins of river basins. Full article

Population aging and the rising prevalence of motor disorders are driving demand for assistive lower-limb robotic systems capable of decoding user intention rather than merely providing mechanical support. We present CM-FuseNet, an attention-augmented hybrid Brain–Computer–Muscle Interface (BCMI) that simultaneously fuses cortical concentration indices extracted from electroencephalography (EEG) and lower-limb intention patterns derived from electromyography (EMG) to adaptively control a 4-DOF assistive lower-limb exoskeleton. To eliminate the burden of human-subject ethics review and to ensure reproducibility of the proposed methodology, all validation is performed exclusively on (i) permissively licensed open-access biomedical datasets, (ii) high-fidelity OpenSim 4.5 and MuJoCo 3.1 musculoskeletal–exoskeleton co-simulation, and (iii) limited self-experimentation by the corresponding author with non-invasive consumer-grade devices. Three components are introduced: (i) a log-tanh normalized concentration index CI in (0, 1) derived from the $(P_{SMR}+P_{Mid-Beta})/P_{Theta}$ ratio; (ii) a bidirectional Cross-Modal Transformer (CMT) with eight-head self- and cross-attention; and (iii) a Soft Actor-Critic (SAC) reinforcement-learning controller that adaptively tunes four servo PID gains using a concentration-weighted state. Experiments on the PhysioNet EEGMMIDB, Ninapro DB2/DB7, HuMoD and WAY-EEG-GAL datasets (combining N = 162 trial sessions, 47,520 windows, and five-fold cross-validation) yield a gait-phase classification accuracy of 96.84 ± 1.18%, torque-tracking RMSE of 0.072 ± 0.008 N·m, information transfer rate of 38.6 bits/min, end-to-end latency of 9.4 ms, and a 27.4% reduction in simulated metabolic cost over an EMG-only PID baseline (one-way ANOVA: F(4, 75) = 47.83, p < 0.001; Tukey HSD: p < 0.01 against all baselines). Under high cognitive load, CM-FuseNet preserves accuracy with only a 4.63 percentage-point degradation versus 13.22 percentage points for the EMG-only baseline. Full article

Motor imagery (MI) classification serves as the backbone to brain–computer interfaces (BCIs) by strengthening the communication bridge between the human brain and external peripheral devices. The past two decades have witnessed unprecedented success in MI-BCIs, with applications not only in medical fields but also in several other domains, such as gaming and robotic control. Initially, MI classification primarily relied on classical signal processing techniques that were heavily impacted by signal variations; however, recent trends in deep learning (DL), specifically in sequence-oriented, attention-based, hybrid, and generative architectures such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers have significantly improved the efficiency and robustness of MI classification. This study presents a comprehensive review of these sequence-oriented, attention-based, hybrid, and generative architectures including RNNs, VAEs, GANs, and transformers, comparing their robustness across various public MI datasets, highlighting their challenges, such as inter-subject variation, low signal-to-noise ratio (SNR), and the obstacles in real-time signal classification. We perform an in-depth analysis on the strengths and limitations of traditional models such as RNNs and LSTMs as well as emergent models such as VAEs and transformers, which have demonstrated superior performance in extracting the intricate patterns of the EEG data with low latency. Moreover, we critically examine the future potential of such models in overcoming current bottlenecks, such as weak generalization on unseen data and high computational load. This study aims to assist researchers in attaining significant insights into the state-of-the-art sequence, attention-based, hybrid, and generative models used in MI classification, thus offering a direction for future innovation. Full article

Selective algorithmic auditing requires deciding which process evidence should receive attention when exhaustive review is infeasible. This Review introduces a four-layer framework that connects process representation, learning, inspection allocation, and governance within a single budgeted sequential decision problem over event streams. Unlike prior reviews centered on predictive process monitoring, explainability, cost analysis, or bibliometric structure, the proposed framework examines how these functions interact when human review, computation, latency, and documentation capacity are constrained. A structured and targeted survey of 89 unique publication families is used to illustrate and critically examine event-log, Petri-net, graph, object-centric, neural, uncertainty-aware, sequential, bandit, reinforcement learning, and audit architecture approaches. The reviewed evidence indicates that substantial bodies of work address the individual layers, but cross-layer evaluation remains fragmented and uses heterogeneous datasets, objectives, and validation protocols. The synthesis identifies five priorities: audit-ready benchmarks, explicit inspection budget protocols, calibrated uncertainty, transfer across organizational contexts, and reproducible governance interfaces. The main contribution is a computational framework and a corpus-bounded research agenda that connects representation, learning, inspection allocation, and governance for selective algorithmic auditing. Full article

Anterior segment optical coherence tomography (AS-OCT) is essential for structural assessment of the anterior eye, yet automated landmark localization remains challenged by pervasive speckle noise, indistinct tissue interfaces, and labor-intensive manual annotation with notable inter-observer variability. This study presents NSE YOLO, an enhanced YOLOv11 framework for high-precision landmark localization in AS-OCT images after implantable collamer lens (ICL) implantation. It integrates a dual-branch NewConv module for multi-scale feature extraction, a dual-additive residual self-attention block (SABlock) to suppress background interference, and a Mamba-based EfficientViMBlock embedded in the C3k2 module to balance global contextual modeling and computational efficiency. Validated on 672 expert-annotated postoperative ICL images from 60 patients, NSE YOLO achieved an mAP@0.5 of 90.7% and mAP@0.5:0.95 of 85.1%, outperforming the YOLOv11 baseline by 5.2% and 6.6% with only 2.86 million parameters. Bland–Altman analysis showed negligible systematic bias and narrow limits of agreement for anterior chamber depth. For iridocorneal angle measurements, directional deviations and wider limits of agreement were observed, with performance approaching the level of inter-observer variability among human annotators. NSE YOLO enables automated quantification of anterior chamber depth and bilateral iridocorneal angles for post-ICL follow-up assessment, providing preliminary technical validation supporting further external and device-level evaluation. Full article

Engineered brain models, including brain organoids and brain-on-a-chip systems, are generally assessed in terms of their physiological relevance. Although this language is useful for emphasizing the need to better approximate human biology, it can also obscure important differences among context of use, required validation strategy and ethical considerations. In this Opinion, we argue that physiological relevance should not be treated as a universal measure of model quality. Instead, its meaning should be defined relative to its application domain, including animal-model comparison, interpretation of single-cell atlases, clinical translation, donor representation, and emerging functional applications such as synthetic biological intelligence. For some applications, particularly patient-specific disease modeling and therapeutic screening, greater human physiological relevance may be required. For others, including biohybrid computing, controllable neural interfaces, interpretability, and ethical considerations may be more important. Moving beyond simplistic terminology will help improve scientific interpretation, prevent overstating findings, and support more responsible development of engineered brain models. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article

Generative artificial intelligence is increasingly used to produce user interface designs, yet the usability and task-performance implications of AI-generated interfaces remain insufficiently quantified. This study proposes a reproducible evaluation framework combining computational visual complexity metrics with human-centered interface assessment. Twelve user interfaces were evaluated across four scenarios: a mobile health dashboard, a learning management system dashboard, an e-commerce shopping cart, and a university student information portal. Each scenario included three design conditions: human-designed reference interfaces, raw AI-generated interfaces, and prompt-optimized AI-generated interfaces. Computational metrics included grayscale Shannon entropy, spatial edge density, RGB color entropy, RMS contrast, robust contrast, and white-space ratio. A within-subject user study with 62 participants measured perceived cognitive load, perceived visual complexity, task ease, interface evaluation time, and task accuracy. Friedman tests revealed significant differences among the interface conditions for all five user-centered outcomes, with very large effect sizes. Raw AI-generated interfaces were associated with the highest perceived cognitive load, highest perceived visual complexity, lowest task ease, and longest interface evaluation times. Within the tested stimulus set, prompt-optimized AI-generated interfaces showed more favorable user-centered outcomes than raw AI interfaces but remained statistically distinct from human-designed references in perceived cognitive load, perceived visual complexity, task ease, and interface evaluation time. Task accuracy reached 100% for both human-designed and prompt-optimized AI interfaces, whereas raw AI interfaces achieved 63.10%. Interface-level correlations between computational visual metrics and user outcomes were weak to moderate, suggesting that pixel-level visual complexity measures capture only part of the perceived usability burden. Overall, these findings support the potential value of prompt-based HCI constraints in AI-generated interface design, while robust evaluation should integrate computational metrics with human-centered testing. Full article

Functional cure of chronic hepatitis B virus (HBV) infection remains a significant challenge, making viral-entry-blocking antibodies a promising antiviral strategy. Here, we developed a structure-guided computational workflow for affinity-enhancing candidate mutations at the interface between the humanized neutralizing antibody HzKR127 and the HBV preS1 peptide epitope. Based on the crystal structure of the HzKR127–preS1 complex, we performed single-site saturation mutagenesis across the paratope, evaluating variants with a consensus effect score integrated from seven computational models. Benchmarking against published alanine scanning data showed that our consensus score effectively identified major-affinity-loss residues, achieving ROC AUC values of 0.81 and 0.84 for residue-level and site-level predictions, respectively. Mutational profiling revealed distinct asymmetric mutational responses, with broad intolerance on the preS1 side and localized favorable substitutions within antibody CDRs. Multilevel prioritization identified 26 antibody-side candidates, 17 of which showed improved HADDOCK refinement scores compared to the wild type. In particular, the H:D97W/F/Y substitutions presented the strongest structural rationale for enhancing improved interfacial packing through aromatic hydrophobic contacts with preS1 Phe10. These findings provide a prioritized list of candidates for experimental validation and a practical framework for the rational optimization of antibodies targeting functionally constrained viral epitopes. Full article

Advanced dental implant systems are clinically successful when their material properties, surface topography, abutment geometry and implant–abutment connection design support both mechanical stability and biological integration. In Morse-tapered implant–abutment systems and conometric prosthetic retention systems, the precision of conical interfaces has traditionally been discussed mainly in biomechanical terms; however, their clinical performance also depends on the formation and maintenance of a stable peri-implant soft tissue seal. This review examines micro-computed tomography (µCT), with emphasis on synchrotron radiation-based phase-contrast micro-computed tomography (SR-PhC-µCT), as an advanced characterization strategy for evaluating how implant and abutment design influence the three-dimensional architecture of peri-implant connective tissues. The review summarizes the biological organization of the peri-implant mucosa, the technical basis of absorption- and phase-contrast microtomography, sample preparation protocols, segmentation workflows, artificial intelligence-assisted image analysis and quantitative morphometric descriptors of collagen organization. Particular attention is given to human retrieval and biopsy studies of Morse-tapered/conometric systems, where three-dimensional imaging has revealed interwoven circumferential and longitudinal collagen bundles around the transmucosal implant component. These microarchitectural features have been directly visualized by SR-PhC-µCT and histology and may represent a biomechanically plausible basis for mucosal sealing and force distribution. However, their direct relationship with marginal bone preservation and long-term clinical outcomes remains to be demonstrated in longitudinal studies. Within the scope of advanced dental materials, the novelty of this review lies in framing SR-PhC-µCT, correlative histology and AI-assisted segmentation as enabling tools within a materials-design framework for implant–abutment optimization. In this framework, peri-implant collagen architecture is interpreted as an exploratory structural readout of the interaction among connection design, abutment geometry, surface topography and biological response. This approach does not yet establish validated clinical biomarkers, but it may generate testable hypotheses for future studies aimed at improving peri-implant soft tissue stability and implant–abutment system design. Full article

Critical-size segmental bone defects remain a major challenge in orthopedic surgery, often requiring complex reconstruction strategies associated with significant morbidity. This study evaluated the regenerative potential of a self-crosslinking bone substitute (SCBS) composed of biphasic calcium phosphate (BCP) granules suspended in a silanized hydroxypropyl methylcellulose (Si-HPMC) hydrogel, with or without recombinant human bone morphogenetic protein-2 (rhBMP-2), in a canine load-bearing defect model. Bilateral 2-cm segmental defects were created in the ulnae of five adult beagle dogs. Defects were filled with SCBS alone or SCBS loaded with rhBMP-2. Bone regeneration and biomaterial remodeling were assessed after 20 weeks using micro-computed tomography (micro-CT), scanning electron microscopy (SEM), histomorphometry, elemental analysis, and histology. SCBS loaded with rhBMP-2 resulted in complete defect bridging, with 35% newly formed bone and only 5% residual BCP granules. In contrast, SCBS alone induced limited bone formation (10%), primarily at host interfaces, with substantial persistence of BCP (33%). Newly formed bone in the rhBMP-2 group exhibited a dense lamellar structure with Haversian organization and direct contact with residual biomaterial. Elemental analysis revealed a lower Ca/P ratio compared with control, suggesting ongoing remodeling. These findings demonstrate that controlled delivery of rhBMP-2 from a self-crosslinking CaP/hydrogel composite enhances both bone formation and biomaterial resorption, supporting a coupled regeneration process. This approach represents a promising strategy for the treatment of segmental bone defects and non-unions in orthopedic applications. Full article

Haptic technology restores the sense of touch to robotic systems and has become increasingly important for safe and intuitive human–robot interaction in healthcare. Despite substantial advances over the past two decades, widespread clinical adoption remains limited, highlighting a persistent gap between laboratory research and real-world medical deployment. This review synthesizes research from robotics, human–computer interaction, neuroscience, and clinical medicine based on a systematic literature search conducted in IEEE Xplore, PubMed, and Scopus (2000–2025). The review adopts an information-centric perspective, focusing on the clinically relevant information conveyed through haptic feedback rather than force reproduction alone. The review examines tactile, kinesthetic, and hybrid feedback modalities; summarizes key principles of haptic rendering, stability, and control; and evaluates applications in surgical robotics, teleoperation, rehabilitation, prosthetics, and medical training. Evidence indicates that haptic feedback can improve performance, reduce excessive forces, and enhance situational awareness, although benefits remain task-dependent. Clinical translation continues to be constrained by sensing limitations, miniaturization challenges, stability requirements, human factors, and regulatory considerations. Current research is increasingly directed toward sensorless force estimation, artificial intelligence-assisted haptic rendering, wearable and soft haptic interfaces, and neurohaptic technologies, reflecting a shift toward task-oriented and information-centric feedback. Future progress will depend less on maximizing physical realism and more on delivering clinically meaningful information through stable, interpretable, and user-centered haptic systems. This review provides a roadmap for advancing clinically deployable haptic technologies in healthcare. Full article