Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissection; however, decisions to intervene on ascending thoracic aortic patients are determined by the size of the ascending thoracic aorta based on its diameter. Diameter-based criteria fail to take into consideration the biomechanical properties of the aorta as well as other characteristics of the patient, and finite element analysis (in determining aortic wall stresses) would ideally address the issues associated with today’s diameter-based criteria. A novel computational solution, termed BioPINN-LM, integrates 2 computational methods for the rapid real-time prediction of aortic wall stress: a physics-informed neural network (PINN) trained with mechanical simulation data to predict wall stress and a multimodal large language model that uses output data from the PINN along with image-based geometry descriptors to provide interpretable risk assessments from both ends of the aorta. Comparison of the PINN to the reference database of finite element method results demonstrates that the PINN produces an average wall stress prediction error of 8.34 kPa (6.12% relative error) for a blood pressure of 120/80 mmHg, with an average prediction time of 0.83 s per geometry compared to 38.6 min for the traditional finite element method. The conversational component of BioPINN-LM achieves 87.4% agreement with board-certified specialist recommendations on a benchmark of 200 simulated clinical scenario vignettes. As a simulation-based proof of concept, these results suggest that integrating mechanistic simulations with language-based inquiry may complement biomechanical decision-making for ascending thoracic aortic aneurysms; clinical validation on real patient cohorts with longitudinal outcomes is deferred to future work.
Magnetically controlled continuum robots enable dexterous manipulation within compact dimensions. However, their deformations are typically limited to C-shaped configurations, which restricts their practical applications such as obstacle avoidance, stable tip orientation, and navigation through anatomical channels with minimal tissue trauma. Moreover, the fixed stiffness of these robots limit its adaptability. To address these limitations, a continuum catheter must possess both multishaped deformation capability and variable stiffness. In this work, we propose a magnetically steerable catheter incorporating 2 radially magnetized magnets and a thermosetting shape memory polymer. Two variable stiffness segments that achieve a 20.8-fold stiffness variation (from 30 to 70 °C) within a compact design through shape memory polymer actuation. The distal magnet can rotate axially, while a single uniform magnetic field generates differential torques on both magnets, enabling multishaped deformation, including C-, J-, and S-shaped configurations. Large C-shaped deformation is achieved under a 15-mT uniform magnetic field, with a maximum distal bending angle of 127°. Experimental validation on the highly realistic gastrointestinal phantoms and ex vivo stomach model demonstrates the potential for clinical application. Compared with existing magnetic catheters, the proposed design exhibits enhanced deformation diversity and operational flexibility, making it adaptable to a broader range of medical procedures.
Central venous pressure (CVP) is a key indicator of right ventricular preload, but measurement with a central venous catheter (CVC) is invasive, time-consuming, and unsuitable for bedside monitoring. We developed and clinically evaluated an artificial intelligence (AI)-enabled wearable ultrasound device for noninvasive estimation of elevated CVP by imaging the internal jugular vein (IJV) and common carotid artery (CCA) in acute and critical patients. In this prospective multi-center clinical study, 349 patients admitted to intensive care units (ICUs) at 2 tertiary hospitals underwent neck vascular imaging with the wearable ultrasound device, while CVP was measured simultaneously via CVC as the reference standard. The dual-decoder spatiotemporal attention network (DSTA-Net) segmentation model automatically quantified IJV and CCA cross-sectional areas, and the dual-modality multilayer perceptron (DM-MLP) integrated these vascular indices with basic clinical parameters to predict elevated CVP (≥8 mmHg). DSTA-Net showed excellent agreement and strong correlation with expert manual measurements while achieving superior segmentation accuracy compared with the baseline segmentation model. Meanwhile, the proposed DM-MLP improved prediction performance by 4% to 8% over conventional baseline architectures, achieving AUCs of 0.91 (internal test set) and 0.87 (external test set). These findings indicate that an AI-integrated wearable ultrasound device can provide accurate, noninvasive bedside assessment of CVP and may offer a promising alternative to invasive catheters for hemodynamic monitoring in acute and critical care settings.
The knees, as important parts in daily locomotion, carry the body weight and provide stability and flexibility. With the degradation of muscle and endurance, the elders get weak knee strength, which heavily affects their life quality. To address this issue, a single-source dual-drive flexible knee assistive exoskeleton is explored to assist the elders in their daily locomotion. Firstly, a single-source dual-drive architecture is designed. The left and right knee are controlled by single electric motor with clutch. It reduces the weight of exoskeleton and has the potential to enhance interaction safety and comfort with cable-driven and elastic elements. Secondly, a cyclic locomotion pattern recognition (LPR) methodology for daily walking situation is provided, adopted with a dual detection strategy fused with finite state machine and fuzzy control. Daily walking situations, e.g., level walking (LW), ramp ascending (RA), ramp descending (RD), stair ascending (SA), and stair descending (SD), could be recognized accurately and precisely. Finally, a time-shared assistive torque control strategy with finite state is conducted to effectively provide suitable auxiliary torque in various walking environments based on locomotion patterns. Three young participants and 3 elder participants participate in outdoor LPR experiments in real-world environment and indoor energy loss experiments. The initial experiment results indicate that the average precision of LPR reaches 98.89%, and the average metabolic cost and surface electromyography signals reduce as much as 28.20% and 67.33%, respectively, with the assistance of exoskeleton.
Endoluminal interventions are crucial for both the diagnosis and treatment of gastrointestinal diseases. However, conventional endoscopic systems are designed for visual inspection and diagnosis, limiting their capacity to assess critical tissue properties such as texture, stiffness, and structural integrity. These unobserved mechanical signatures may contribute to clinically relevant failure modes, including missed lesions, incomplete resection, and adverse events driven by excessive wall loading. Inspired by the tactile sensing mechanisms of rat whiskers, this study introduces a bionic multichannel whisker system, as an additional sensing modality for endoluminal interventions. The system integrates high-fidelity tactile sensing hardware, and advanced signal processing algorithms, enabling precise and reliable measurements of tissue properties, as well as providing real-time radial force feedback. Calibration via affine transformation ensures cross-channel consistency and compensates for manufacturing and installation variances. Validation across multiple experimental settings, including robotic and manual operation, demonstrates performance in texture discrimination, shape reconstruction, and radial force estimation. The presented whisker system is compact to support integration with endoscopic instruments, providing a practical basis for complementing endoluminal diagnostic workflows and for further development of tactile sensing in surgical robotics.
Load capacity and speed are 2 essential dimensions in the practical application of miniature robots. In recent years, numerous miniature robots with large load capacity or high speed have been developed. However, it is still a challenge to achieve high speed under large load. Inspired by mythological creatures such as dragon, qilin, and chimera that integrate the characteristics of different animals, an insect-scale tripedal piezoelectric robot incorporating multiple biomimetic features is proposed. By emulating the terrestrial flapping of fish tail, a single driving leg with 2 orthogonal bending vibrations is designed to achieve rapid and flexible motion. The tethered robot is 38 mm in length and weighs 8.6 g, exhibiting a maximum forward speed of 313.49 mm/s (8.25 body length per second), a maximum angular speed of 11.54 rad/s, and a minimum curvature radius of 12.64 mm. By emulating the functional roles of the forelimbs and hindlimbs in otariids galloping, a support scheme combining 2 passive wheels with a driving leg is proposed to realize high speed under large load. The forward speed achieves more than 300 mm/s under 200 g (23.26 times self-weight). Moreover, an untethered robot is fabricated. It exhibits a cost of transport of only 1.91 and can operate continuously for 70 min. The untethered robot demonstrates marked potential for operation in narrow spaces, owing to its small size, superior load characteristics, and high flexibility. We believe that this design method of integrating multiple bionic features can offer a new perspective for enhancing the performance of miniature robots.
Heatwave (HW) exposure is increasing rapidly under climate change, yet its potential role in accelerating biological aging and the underlying mechanisms remain poorly understood. Leveraging data derived from the China Health and Retirement Longitudinal Study (CHARLS), a large population-based cohort in China, we examined whether exposure to HWs is linked to more rapid biological aging in adults of middle and advanced age. The Klemera–Doubal method (KDM) was applied to derive estimates of biological age (BA), and biological age acceleration (BAA) was calculated as biological age minus chronological age. HW exposure during the 12 months preceding BA assessments in 2011 and 2015 was quantified using 12 definitions based on different temperature threshold and duration. Longitudinal associations between HW exposure and BAA were evaluated using a difference-in-differences design. Among 2,318 participants (mean age, 58.7 years; 46.9% men), greater HW exposure was significantly associated with higher BAA. Under the most stringent (HW12; ≥4 consecutive days above the 97.5th percentile), each additional HW event and day increased BAA by 0.531 years [95% confidence interval (CI), 0.341 to 0.722] and 0.057 years (95% CI, 0.037 to 0.076). Stronger associations were observed among participants with body mass index ≥ 23 kg/m2, urban residents, and those living in southern or subtropical regions. HW exposure was also additionally associated with higher levels of total cholesterol and glycated hemoglobin A1c (HbA1c) levels. To explore potential biological mechanisms, transcriptomic profiling was performed in aged mice exposed to HW conditions. HW exposure induced 29 differentially expressed genes enriched in lipid metabolism and insulin resistance pathways, providing biological plausibility for the observed epidemiological associations. These results suggest that recurrent HW exposure may contribute to accelerated biological aging, potentially through metabolic disruption, and highlighting the vulnerability of aging populations to climate-related thermal stress and the need for targeted climate-adaptation strategies.
Older adults frequently face difficulties in activities of daily living (ADLs) due to age-related declines in strength, coordination, and perception. Myoelectric control provides an intuitive human–robot interface by translating muscle activity into assistive commands. However, its practical application is still challenged by signal annotation, multijoint coordination, and cross-task generalization. This study proposes a 3-level intelligent framework for multijoint upper-limb assistance based on electromyography (EMG) to support the daily living activities of older adults. At the physiological level, situation-aware labeling protocols matched to different EMG conditions are proposed to reduce annotation ambiguity and improve robustness to signal changes. At the functional level, focusing on elemental joint activities, a deep backbone model is designed to infer both single-joint movements and coordinated multijoint patterns with an accuracy of 95.34%. At the behavioral level, the model is further distilled to support complex ADL tasks with human–robot interactions while continually incorporating new knowledge without catastrophic forgetting. The framework is implemented in real time on an EMG-controlled multijoint robotic system, providing smooth and coordinated assistance in daily activities. Overall, the proposed framework provides a systematic solution for EMG-based multijoint coordination, encompassing the entire pathway from physiological signal processing to functional intent decoding and behavioral adaptation during daily activities. It offers a technical approach to coordinated upper-limb assistance and lays a broader foundation for the design of practical and adaptive assistive systems, contributing to improved autonomy for older adults and supporting the broader societal goal of healthy aging.
The clinical efficacy of many conventional passive drug delivery systems is frequently constrained by their low targeting efficiency, important off-target toxicity, and inadequate capacity for traversing biological barriers. Autonomous microrobots, as miniature intelligent platforms capable of active navigation and on-demand responsiveness, offer an active delivery strategy for achieving spatiotemporally precise targeted therapy. This review aims to systematically consolidate and critique the theoretical foundations, key technologies, cutting-edge applications, and future challenges of this emergent interdisciplinary field. We first provide an in-depth analysis of the technological frameworks underpinning the 2 core functionalities: targeted delivery and on-demand release. This encompasses a diverse array of propulsion and navigation strategies-from chemical and physical fields to biohybrid systems-as well as programmed drug release mechanisms responsive to endogenous and exogenous stimuli. Building on this, we introduce a hierarchical paradigm organized by biological-barrier traversal capability to review the preclinical progress of microrobots, from localized delivery in accessible body cavities to deep-tissue and trans-barrier applications. This function-oriented framework more directly links microrobot design to the progressive physiological constraints encountered in vivo, thereby providing a more integrated and translationally relevant perspective on biomedical applications and clinical potential. Concurrently, this paper examines the bottlenecks impeding their clinical translation, including biosafety, systemic controllability, and regulatory science. Looking forward, the deep integration of microrobotics with smart materials, artificial intelligence, and theranostic systems is poised to cultivate a new generation of intelligent medical robots capable of personalized treatment via closed-loop manners.
Single-cell studies offer rich insights into the cellular heterogeneity and function, providing a promising approach for precise diagnostics and personalized treatments. Single-cell research involves initial screening and in-depth analysis. Among available techniques, optoelectronic tweezers (OETs) stand out, offering a noncontact, gentle method that enables flexible, directional control of single cells through dynamic optical patterns. OET is effective in both screening and analysis. This review first introduces the sorting and analysis techniques used in single-cell research and then delves into the principles and development of OET technology, followed by the current achievements of OET in advanced single-cell research. Finally, it discusses and prospects the potential development of OET in the context of single-cell research applications.
Slender medical continuum robots with flexibility and highly redundant degrees of freedom are widely used in various minimally invasive surgery. However, when interacting with anatomical structures, the continuum robot adopts diverse shapes, posing challenges for operation and control. To achieve real-time intraoperative shape sensing and provide online guidance for manipulation, most existing methods rely on optical fibers embedded within the robot, which often require specialized robot designs and come with high costs. Here, we present a novel approach utilizing thin and flexible carbon nanotube piezoresistive fibers as a bandage, helically integrated on the surface of existing slender medical continuum robots for shape sensing. The spatial configuration of the robot is effectively inferred by downsampling the resistance changes along the robot’s body and applying a learning-based method. The results demonstrate that the proposed helically arranged carbon nanotube piezoresistive fibers, combined with a data-driven approach, are capable of reconstructing the robot’s spatial shape. In vitro and ex vivo experiments on animal tissues further highlight its promising potential for enhancing the shape-sensing ability of existing medical continuum robots.
Alzheimer disease (AD), a devastating neurodegenerative disorder, is pathologically defined by amyloid-β (Aβ) deposition and neurofibrillary tangles. Critically, concomitant cerebrovascular dysfunction compromises neuronal homeostasis and significantly accelerates AD progression by impairing the neurovascular unit. However, effective strategies to modulate this complex neurovascular pathology remain unclear. Here, we applied transcranial photobiomodulation (tPBM) with continuous-wave (CW) and 40-Hz pulsed light to target neurovascular pathology in 5xFAD mice. The results showed that both tPBM modalities comparably ameliorated cognitive dysfunction through distinct glial-mediated mechanisms. Specifically, CW light primarily enhanced astrocyte–vascular coupling, which ameliorated vascular dysfunction and protected synapses. In contrast, 40-Hz light predominantly drove spatial redistribution of microglia toward Aβ plaques, thereby enhancing localized amyloid clearance. These findings reveal complementary pathways for tPBM in AD intervention, highlighting that CW and 40-Hz light offer modality-specific therapeutic advantages: The former targets cerebrovascular dysfunction, while the latter addresses Aβ plaque deposition. Collectively, our study provides critical mechanistic insights for optimizing tPBM protocols, establishing a foundation for more precise and comprehensive AD interventions.
Digestive tract cancers, including hepatobiliary and gastrointestinal malignancies, remain a major oncological burden globally. Immunotherapy efficacy rates are low, with only 15% to 30% of patients experiencing responses following treatment. Tumor-associated macrophages, which change phenotype between a pro-inflammatory and an immunosuppressive state, play a key role in determining the response to therapy, and current static biomarkers are inadequate for capturing the spatial–temporal changes associated with the immune response. We developed a bioinspired digital twin platform integrating variational representation learning with causal sequence modeling. The platform incorporates heterogeneous biological data (1.2 million single-cell transcriptomes, spatial immunophenotyping, and clinical trajectories) from 2,847 individuals across 5 digestive cancer types. Graph-based attention mechanisms encode intercellular interactions, while transformer-based temporal modules simulate immunological state transitions. A model-predictive optimization layer identifies patient-specific interventions maximizing repolarization potential. The biomimetic model predicted the outcome of therapy response better than conventional biomarker models did (area under the receiver operating characteristic curve: 0.847 compared to 0.692 with a statistically significant difference at P below 0.001). In an exploratory, nonrandomized analysis of discordant cases (n = 156) where model and physician recommendations differed, model-guided treatment was associated with higher response rates (47.4% versus 28.2%) and longer median progression-free survival (9.8 months versus 6.0 months; P = 0.003); however, selection bias cannot be excluded. This study provides preliminary evidence for the feasibility of a computational framework for immunotherapy optimization; prospective randomized trials are required to establish clinical utility.
