An integrated design and fabrication strategy for entirely soft, autonomous robots.

Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.

Leaks after Left Atrial Appendage Closure: Ignored or Neglected?

Left atrial appendage (LAA) closure has recently been approved as an alternative management for stroke prevention in patients with chronic atrial fibrillation who have difficulties with long-term oral anticoagulation. The various shapes and sizes of LAA and orientation of the atrial ostium may contribute to the incomplete LAA closure from circular design devices and orientation of the non-steerable delivery catheter. Incomplete closure of LAA leads to a high-velocity blood flow through the peri-device gap, resulting in peri-device leak (PDL). Residual leaks are frequently diagnosed after LAA closure procedures, regardless of closure methods. There is a controversy in the clinical significance of the leaks, particularly about its association with thromboembolic events. PDL <5 mm was not associated with any increase of risk for thromboembolism. Current literatures with small study population have not been sufficient to clarify the role of the leaks after LAA closure. Nevertheless, management of severe leaks has been a challenging concern for cardiologists. Leaks after epicardial LAA closures are at the neck of the incompletely closed LAA and have shown to increase the risk of thromboembolism. Percutaneous closure of the leaks after LAA closure has been attempted with good safety and success, but long-term safety and efficacy remains unclear. Further large long-term studies which aim to assess the role of leaks or PDLs in predicting thromboembolic events and management strategies are warranted.

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives.

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials' properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

Paper-Based Electrical Respiration Sensor.

Current methods of monitoring breathing require cumbersome, inconvenient, and often expensive devices; this requirement sets practical limitations on the frequency and duration of measurements. This article describes a paper-based moisture sensor that uses the hygroscopic character of paper (i.e. the ability of paper to adsorb water reversibly from the surrounding environment) to measure patterns and rate of respiration by converting the changes in humidity caused by cycles of inhalation and exhalation to electrical signals. The changing level of humidity that occurs in a cycle causes a corresponding change in the ionic conductivity of the sensor, which can be measured electrically. By combining the paper sensor with conventional electronics, data concerning respiration can be transmitted to a nearby smartphone or tablet computer for post-processing, and subsequently to a cloud server. This means of sensing provides a new, practical method of recording and analyzing patterns of breathing.

Disulfide-Based Diblock Copolymer Worm Gels: A Wholly-Synthetic Thermoreversible 3D Matrix for Sheet-Based Cultures.

It is well-known that 3D in vitro cell cultures provide a much better model than 2D cell cultures for understanding the in vivo microenvironment of cells. However, significant technical challenges in handling and analyzing 3D cell cultures remain, which currently limits their widespread application. Herein, we demonstrate the application of wholly synthetic thermoresponsive block copolymer worms in sheet-based 3D cell culture. These worms form a soft, free-standing gel reversibly at 20-37 °C, which can be rapidly converted into a free-flowing dispersion of spheres on cooling to 5 °C. Functionalization of the worms with disulfide groups was found to be essential for ensuring sufficient mechanical stability of these hydrogels to enable long-term cell culture. These disulfide groups are conveniently introduced via statistical copolymerization of a disulfide-based dimethacrylate under conditions that favor intramolecular cyclization and subsequent thiol/disulfide exchange leads to the formation of reversible covalent bonds between adjacent worms within the gel. This new approach enables cells to be embedded within micrometer-thick slabs of gel with good viability, permits cell culture for at least 12 days, and facilitates recovery of viable cells from the gel simply by incubating the culture in buffer at 4 °C (thus, avoiding the enzymatic degradation required for cell harvesting when using commercial protein-based gels, such as Matrigel).

Folding analytical devices for electrochemical ELISA in hydrophobic R(H) paper.

This work describes a device for electrochemical enzyme-linked immunosorbent assay (ELISA) designed for low-resource settings and diagnostics at the point of care. The device is fabricated entirely in hydrophobic paper, produced by silanization of paper with decyl trichlorosilane, and comprises two zones separated by a central crease: an embossed microwell, on the surface of which the antigen or antibody immobilization and recognition events occur, and a detection zone where the electrodes are printed. The two zones are brought in contact by folding the device along this central crease; the analytical signal is recorded from the folded configuration. Two proof-of-concept applications, an electrochemical direct ELISA for the detection of rabbit IgG as a model antigen in buffer and an electrochemical sandwich ELISA for the detection of malarial histidine-rich protein from Plasmodium falciparum (Pf HRP2) in spiked human serum, show the versatility of this device. The limit of detection of the electrochemical sandwich ELISA for the quantification of Pf HRP2 in spiked human serum was 4 ng mL(-1) (10(2) pmol L(-1)), a value within the range of clinically relevant concentrations.

Distilling Knowledge From an Ensemble of Vision Transformers for Improved Classification of Breast Ultrasound.

RATIONALE AND OBJECTIVES: To develop a deep learning model for the automated classification of breast ultrasound images as benign or malignant. More specifically, the application of vision transformers, ensemble learning, and knowledge distillation is explored for breast ultrasound classification. MATERIALS AND METHODS: Single view, B-mode ultrasound images were curated from the publicly available Breast Ultrasound Image (BUSI) dataset, which has categorical ground truth labels (benign vs malignant) assigned by radiologists and malignant cases confirmed by biopsy. The performance of vision transformers (ViT) is compared to convolutional neural networks (CNN), followed by a comparison between supervised, self-supervised, and randomly initialized ViT. Subsequently, the ensemble of 10 independently trained ViT, where the ensemble model is the unweighted average of the output of each individual model is compared to the performance of each ViT alone. Finally, we train a single ViT to emulate the ensembled ViT using knowledge distillation. RESULTS: On this dataset that was trained using five-fold cross validation, ViT outperforms CNN, while self-supervised ViT outperform supervised and randomly initialized ViT. The ensemble model achieves an area under the receiver operating characteristics curve (AuROC) and area under the precision recall curve (AuPRC) of 0.977 and 0.965 on the test set, outperforming the average AuROC and AuPRC of the independently trained ViTs (0.958 ± 0.05 and 0.931 ± 0.016). The distilled ViT achieves an AuROC and AuPRC of 0.972 and 0.960. CONCLUSION: Both transfer learning and ensemble learning can each offer increased performance independently and can be sequentially combined to collectively improve the performance of the final model. Furthermore, a single vision transformer can be trained to match the performance of an ensemble of a set of vision transformers using knowledge distillation.

Application of Mixed/Augmented Reality in Interventional Cardiology.

This review explores the transformative applications of augmented reality (AR) and mixed reality (MR) technologies in interventional cardiology. The integration of these cutting-edge systems offers unprecedented potential to enhance visualization, guidance, and outcomes during complex cardiac interventional procedures. This review examines four key domains: (1) medical AR/MR systems and technological foundations; (2) clinical applications across procedures like TAVI, PCI, and electrophysiology mapping; (3) ongoing technology development and validation efforts; and (4) educational and training applications for fostering essential skills. By providing an in-depth analysis of the benefits, challenges, and future directions, this work elucidates the paradigm shift catalyzed by AR and MR in advancing interventional cardiology practices. Through meticulous exploration of technological, clinical, and educational implications, this review underscores the pivotal role of these innovative technologies in optimizing procedural guidance, improving patient outcomes, and driving innovation in cardiovascular care.

Toward Development of Novel Remote Ultrasound Robotic System Using Soft Robotics Technology.

This paper reports on the development of a novel soft robotic system for remote ultrasound applications. Direct contact of the ultrasound probe with the patient's body represents a safety risk and therefore control of the probe's positioning and applied force is a crucial task. The proposed robot uses a passive control system that provides safe interaction between the robot and the patient by leveraging soft robotics technology. The soft robot's structure can be considered as a nonlinear spring which can be designed to exert a safe force within the robot's workspace to guarantee the safety of human-robot interaction. The literature suggests that effective ultrasound imaging of both the heart and abdomen requires six degrees-of-freedom. These degrees-of-freedom consist of three translational motions, which are achieved using a novel hybrid soft cable-driven parallel robot, and three wrist motions, which is based on a universal joint design. The experimental results show that the robot can achieve all these six degrees-of-freedom, and its blocking force can be engineered to generate a uniform force within the workspace.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine.

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.