Author Correction: Ex vivo validation of magnetically actuated intravascular untethered robots in a clinical setting
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Categorizing robots by performance fitness into the tree of robots
Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.
A gut-on-a-chip incorporating human faecal samples and peristalsis predicts responses to immune checkpoint inhibitors for melanoma
Patient responses to immune checkpoint inhibitors can be influenced by the gastrointestinal microbiome. Mouse models can be used to study microbiome–host crosstalk, yet their utility is constrained by substantial anatomical, functional, immunological and microbial differences between mice and humans. Here we show that a gut-on-a-chip system mimicking the architecture and functionality of the human intestine by including faecal microbiome and peristaltic-like movements recapitulates microbiome–host interactions and predicts responses to immune checkpoint inhibitors in patients with melanoma. The system is composed of a vascular channel seeded with human microvascular endothelial cells and an intestinal channel with intestinal organoids derived from human induced pluripotent stem cells, with the two channels separated by a collagen matrix. By incorporating faecal samples from patients with melanoma into the intestinal channel and by performing multiomic analyses, we uncovered epithelium-specific biomarkers and microbial factors that correlate with clinical outcomes in patients with melanoma and that the microbiome of non-responders has a reduced ability to buffer cellular stress and self-renew. The gut-on-a-chip model may help identify prognostic biomarkers and therapeutic targets.
In vivo surface-enhanced Raman scattering techniques: nanoprobes, instrumentation, and applications
Surface-enhanced Raman scattering (SERS) has emerged as a powerful tool in various biomedical applications, including in vivo imaging, diagnostics, and therapy, largely due to the development of near-infrared (NIR) active SERS substrates. This review provides a comprehensive overview of SERS-based applications in vivo, focusing on key aspects such as the design considerations for SERS nanoprobes and advancements in instrumentation. Topics covered include the development of NIR SERS substrates, Raman label compounds (RLCs), protective coatings, and the conjugation of bioligands for targeted imaging and therapy. The review also discusses microscope-based configurations such as scanning, widefield imaging, and fiber-optic setups. Recent advances in using SERS nanoprobes for in vivo sensing, diagnostics, biomolecule screening, multiplex imaging, intraoperative guidance, and multifunctional cancer therapy are highlighted. The review concludes by addressing challenges in the clinical translation of SERS nanoprobes and outlines future directions, emphasizing opportunities for advancing biomedical research and clinical applications.
Determinants of consumer intention to use autonomous delivery vehicles: based on the planned behavior theory and normative activation model
Autonomous delivery vehicles (ADVs) that provide contactless services have attracted much academic and practical attention in China in recent years. Despite this, there is a lack of in-depth research on what motivates customers to embrace ADVs. The study integrates the theory of planned behavior (TPB) and normative activation model (NAM) and explores how environmental factors, situational factors, and individual factors affect original TPB constructs and ultimately consumers’ intention to use ADVs. Structural equation modeling was performed on survey data of 561 Chinese consumers through an online sampling platform. The results show that among the factors affecting consumer intention, word-of-mouth recommendations have the greatest impact, followed by perceived enjoyment, COVID-19 risk, ascription of responsibility, subjective norm, attitude, and perceived behavioral control. The results not only make important theoretical contributions to the technology acceptance fields but also provide helpful references to logistics enterprises, ADVs technology providers, and policymakers.
User-specified inverse kinematics taught in virtual reality reduce time and effort to hand-guide redundant surgical robots
Medical robots should not collide with close by obstacles during medical procedures, such as lamps, screens, or medical personnel. Redundant robots have more degrees of freedom than needed for moving endoscopic tools during surgery and can be reshaped to avoid obstacles by moving purely in the space of these additional degrees of freedom (null space). Although state-of-the-art robots allow surgeons to hand-guide endoscopic tools, reshaping the robot in null space is not intuitive for surgeons. Here we propose a learned task space control that allows surgeons to intuitively teach preferred robot configurations (shapes) that avoid obstacles using a VR-based planner in simulation. Later during surgery, surgeons control both the endoscopic tool and robot configuration (shape) with one hand. In a user study, we found that learned task space control outperformed state-of-the-art naive task space control in all the measured performance metrics (time, effort, and user-perceived effort). Our solution allowed users to intuitively interact with robots in VR and reshape robots while moving tools in medical and industrial applications.
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