Most in vitro studies regarding new anticancer treatments are performed on 2D cultures, despite this approach imposes several limitations in recapitulating the real tumor behavior and in predicting the effects of therapy on both cancer and healthy tissues. Herein, advanced in vitro models based on scaffolds that support the 3D growth of glioma cells, further allowing the cocultures with healthy brain cells, are presented. These scaffolds, doped with superparamagnetic iron oxide nanoparticles and obtained through 2-photon polymerization, can be remotely manipulated thanks to an external magnet, thus obtaining biomimetic 3D organization recapitulating the brain cancer microenvironment. From a geometric point of view, the structure is functional to both cell culture on individual unit scaffolds and to tailored cocultures fostered by magnetic-driven unit assembly, also allowing for cell migration thanks to passages/fenestrations on adjacent structures. Leveraging magnetic dragging, for which a mathematical model is introduced, multiple cocultures are achieved, highlighting the high versatility and the user-friendly character of the proposed platform that can help overcome the current challenges in 3D cocultures handling, and open the way to the construction of increasingly biomimetic artificial systems.

For targeted transport in the body, biomedical microbots (μbots) must move effectively in three-dimensional (3D) microenvironments. Swimming μbots translate via asymmetric or screw-like motions while rolling ones use friction with available surfaces to generate propulsive forces. We have previously shown that planar rotating magnetic fields assemble μm-scale superparamagnetic beads into circular μbots that roll along surfaces. In this, gravity is required to pull μbots near the surface; however, this is not necessarily practical in complex geometries. Here we show that rotating magnetic fields, in tandem with directional magnetic gradient forces, can be used to roll μbots on surfaces regardless of orientation. Simplifying implementation, we use a spinning permanent magnet to generate differing ratios of rotating and gradient fields, optimizing control for different environments. This use of a single magnetic actuator sidesteps the need for complex electromagnet or tandem field setups, removes requisite gravitational load forces, and enables μbot targeting in complex 3D biomimetic microenvironments.

Metachronal motions are ubiquitous in terrestrial and aquatic organisms and have attracted substantial attention in engineering for their potential applications. Hard-magnetic soft materials are shown to provide new opportunities for metachronal wave-modulated robotic locomotion by multi-agent active morphing in response to external magnetic fields. However, the design and optimization of such magnetic soft robots can be complex, and the fabrication and magnetization processes are often delicate and time-consuming. Herein, a computational model is developed that integrates granular models into a magnetic-lattice model, both of which are implemented in the highly efficient parallel computing platform large-scale atomic/molecular massively parallel simulator (LAMMPS). The simulations accurately reproduce the deformation of single cilium, the metachronal wave motion of multiple cilia, and the crawling and rolling locomotion of magnetic cilia soft robots. Furthermore, the simulations provide insight into the spatial and temporal variation of friction forces and trajectories of cilia tips. The results contribute to the understanding of metachronal wave-modulated locomotion and potential applications in the field of soft robotics and biomimetic engineering. The developed model also provides a versatile computational framework for simulating the movement of magnetic soft robots in realistic environments and has the potential to guide the design, optimization, and customization of these systems.

While interest in soft robotics as surgical tools has grown due to their inherently safe interactions with the body, their feasibility is limited in the amount of force that can be transmitted during procedures. This is especially apparent in minimally invasive procedures where millimeter-scale devices are necessary for reaching the desired surgical site, such as in interventional bronchoscopy. To leverage the benefits of soft robotics in minimally invasive surgery, a soft robot with integrated tip steering, stabilization, and needle deployment capabilities is proposed for lung tissue biopsy procedures. Design, fabrication, and modeling of the force transmission of this soft robotic platform allows for integration into a system with a diameter of 3.5 mm. Characterizations of the soft robot are performed to analyze bending angle, force transmission, and expansion during needle deployment. In-vitro experiments of both the needle deployment mechanism and fully integrated soft robot validate the proposed workflow and capabilities in a simulated surgical setting.

While most deep learning approaches are developed for single images, in real world applications, images are often obtained as a series to inform decision making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. In this study, we present an approach that seamlessly integrates deep learning and traditional machine learning models, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, we integrate the multilevel interconnections across joints and damage types into the machine learning model and reveal the cross-regulation map of joint damages in rheumatoid arthritis.

The integration of an ingestible dosage form with sensing, actuation, and drug delivery capabilities can enable a broad range of surgical-free diagnostic and treatment strategies. However, the gastrointestinal (GI) tract is a highly constrained and complex luminal construct that fundamentally limits the size of an ingestible system. Recent advancements in mesoscale magnetic crawlers have demonstrated the ability to effectively traverse complex and confined systems by leveraging magnetic fields to induce contraction and bending-based locomotion. However, the integration of functional components (e.g., electronics) in the proposed ingestible system remains fundamentally challenging. Herein, the creation of a centralized compartment in a magnetic robot by imparting localized flexibility (MR-LF) is demonstrated. The centralized compartment enables MR-LF to be readily integrated with modular functional components and payloads, such as commercial off-the-shelf electronics and medication, while preserving its bidirectionality in an ingestible form factor. The ability of MR-LF to incorporate electronics, perform drug delivery, guide continuum devices such as catheters, and navigate air-water environments in confined lumens is demonstrated. The MR-LF enables functional integration to create a highly-integrated ingestible system that can ultimately address a broad range of unmet clinical needs.

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. This article reports the development of a smart helical magnetic hydrogel microrobot that uses the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allows the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies from both state vector input and raw images, and the control policies learned by the agent recapitulated the behavior of rationally designed controllers based on physical models of helical swimming microrobots. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots.

Complex environments, such as those found in surgical and search-and-rescue applications, require soft devices to adapt to minimal space conditions without sacrificing the ability to complete dexterous tasks. Stacked Balloon Actuators (SBAs) are capable of large deformations despite folding nearly flat when deflated, making them ideal candidates for such applications. This paper presents the design, fabrication, modeling, and characterization of monolithic, inflatable, soft SBAs. Modeling is presented using analytical principles based on geometry, and then using conventional and real-time finite element methods. Both one and three degree-of-freedom (DoF) SBAs are fully characterized with regards to stroke, force, and workspace. Finally, three representative demonstrations show the SBA's small-aperture navigation, bracing, and workspace-enhancing capabilities.

Artificial intelligence algorithms are being adopted to analyze medical data, promising faster interpretation to support doctors' diagnostics. The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed-loop solution is proposed, where a cellular neural network is used to detect abnormal wavefronts and wavebrakes in cardiac signals recorded in human tissue is trained to achieve >96% accuracy, >92% precision, >99% specificity, and >93% sensitivity, when floating point precision weights are assumed. Unfortunately, the current hardware technologies for floating point precision are too bulky or energy intensive for compact standalone applications in medical implants. Emerging device technologies, such as memristors, can provide the compact and energy-efficient hardware fabric to support these efforts and can be reliably embedded with existing sensor and actuator platforms in implantable devices. A distributed design that considers the hardware limitations in terms of overhead and limited bit precision is also discussed. The proposed distributed solution can be easily adapted to other medical technologies that require compact and efficient computing, like wearable devices and lab-on-chip platforms.

The field of magnetic robotics aims to obviate physical connections between the actuators and end-effectors. Such tetherless control may enable new ultra-minimally invasive surgical manipulations in clinical settings. While wireless actuation offers advantages in medical applications, the challenge of providing sufficient force to magnetic needles for tissue penetration remains a barrier to practical application. Applying sufficient force for tissue penetration is required for tasks such as biopsy, suturing, cutting, drug delivery, and accessing deep seated regions of complex structures in organs such as the eye. To expand the force landscape for such magnetic surgical tools, an impact-force based suture needle capable of penetrating and samples with 3-DOF planar motion is proposed. Using custom-built 14G and 25G needles, we demonstrate generation of 410 mN penetration force, a 22.7-fold force increase with more than 20 times smaller volume compared to similar magnetically guided needles. With the MPACT-Needle, suturing of a gauze mesh onto an agar gel is demonstrated. In addition, we have reduced the tip size to 25G, which is a typical needle size for interventions in the eye, to demonstrate penetration in a rabbit eye, mimicking procedures such as corneal injections and transscleral drug delivery.

Colonoscopies allow surgeons to detect common diseases i.e. colorectal cancer, ulcers and other ailments. However, there is a risk of bleeding in the lower gastrointestinal (GI) tract while maneuvering endoscopes. This may be due to perforations, hemorrhaging, polyps, diverticuli or post-biopsy complications. Thus, it is essential for the surgeon to be able to detect bleeding at the site and evaluate the severity of blood leakage. This paper presents a soft sensor that can detect the presence of blood at the bleeding site during colonoscopies. The sensor consists of optical waveguides that interface with a microfluidic channel. Blood flow causes absorption and scattering of incident light that can be picked up by the optical sensing apparatus via light transmission through the waveguide. The surgeon can be alerted when bleeding occurs through a graphical user interface. The device is compact and measures only 1 mm thick. This allows the sensor to be circumferentially mounted onto a colonoscope at different locations. The sensor is able to record the presence of blood as an optical loss, rapidly detect the presence of blood in under 100 milliseconds as it enters the microchannel, and differentiate between gastric fluid and blood through changes in measured optical loss.

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D-printed three-linked-sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model-free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.

Emerging bottom-up fabrication methods have enabled the assembly of synthetic colloids, microrobots, living cells, and organoids to create intricate structures with unique properties that transcend their individual components. This review provides an access point to the latest developments in externally driven assembly of synthetic and biological components. In particular, we emphasize reversibility, which enables the fabrication of multiscale systems that would not be possible under traditional techniques. Magnetic, acoustic, optical, and electric fields are the most promising methods for controlling the reversible assembly of biological and synthetic subunits since they can reprogram their assembly by switching on/off the external field or shaping these fields. We feature capabilities to dynamically actuate the assembly configuration by modulating the properties of the external stimuli, including frequency and amplitude. We describe the design principles which enable the assembly of reconfigurable structures. Finally, we foresee that the high degree of control capabilities offered by externally driven assembly will enable broad access to increasingly robust design principles towards building advanced dynamic intelligent systems.

Developing small-scale soft continuum robots with large-angle steering capacity and high-precision manipulation offers broad opportunities in various biomedical settings. However, existing continuum robots reach the bottleneck in actuation on account of the contradiction among small size, compliance actuation, large tender range, high precision, and small dynamic error. Herein, a 3D-printed millimeter-scale soft continuum robot with an ultrathin hollow skeleton wall (300 μm) and a large inner-to-outer ratio (0.8) is reported. After coating a thin ferromagnetic elastomer layer (≈100-150 μm), the proposed soft continuum robot equipped with hybrid actuation (tendon- and magnetic-driven mode) achieves large-angle (up to 100°) steering and high-precision (low to 2 μm for static positioning) micromanipulation simultaneously. Specifically, the robot implements an ultralow dynamic tracking error of ≈10 μm, which is ≈30-fold improved than the state of art. Combined with a microneedle/knife or nasopharyngeal swab, the robot reveals the potential for versatile biomedical applications, such as drug injection on the target tissue, diseased tissue ablation, and COVID-19 nasopharyngeal sampling. The proposed millimeter-scale soft continuum robot presents remarkable advances in large-range and high-precise actuation, which provides a new method for miniature continuum robot design and finds broad applications in biomedical engineering.

The procurement and maintenance cost of high-end ventilators preclude their stockpiles sufficient for the mass emergency situations. Therefore, there is a significant demand for mechanical ventilators in such situations. Herein, a low-cost, portable, yet high-performance design for a volume-controlled mechanical ventilator is proposed. Pneumatic artificial muscles, such as air cylinders, are used in the inverse mode of operation to achieve mechanical ventilation. With the current design, the two fundamental modes of operation (controlled mode and assisted mode) are demonstrated. Unlike most intensive care unit ventilators, the proposed device does not need a high-pressure air pipeline to operate. The device is capable of mechanical ventilation for respiration rate ranging from 10 to 30 b min with a tidal volume () range of 150-1000 mL and the ratio of 1:1-1:5. A total cost of less than $400 USD is achieved to make one device. The cost to produce the device in larger volumes can be estimated to be less than $250 USD.

Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethy- lolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 μm long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200 Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low- magnitude actuation fields of 5-10 mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.

The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges are highlighted.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that led to the COVID-19 (Coronavirus Disease 2019) pandemic, has resulted in substantial overburdening of healthcare systems as well as an economic crisis on a global scale. This has in turn resulted in widespread efforts to identify suitable therapies to address this aggressive pathogen. Therapeutic antibody and vaccine development are being actively explored, and a phase I clinical trial of mRNA-1273 which is developed in collaboration between the National Institute of Allergy and Infectious Diseases and Moderna, Inc. is currently underway. Timelines for the broad deployment of a vaccine and antibody therapies have been estimated to be 12-18 months or longer. These are promising approaches that may lead to sustained efficacy in treating COVID-19. However, its emergence has also led to a large number of clinical trials evaluating drug combinations composed of repurposed therapies. As study results of these combinations continue to be evaluated, there is a need to move beyond traditional drug screening and repurposing by harnessing artificial intelligence (AI) to optimize combination therapy design. This may lead to the rapid identification of regimens that mediate unexpected and markedly enhanced treatment outcomes.

Haptic devices are in general more adept at mimicking the bulk properties of materials than they are at mimicking the surface properties. This paper describes a haptic glove capable of producing sensations reminiscent of three types of near-surface properties: hardness, temperature, and roughness. To accomplish this mixed mode of stimulation, three types of haptic actuators were combined: vibrotactile motors, thermoelectric devices, and electrotactile electrodes made from a stretchable conductive polymer synthesized in our laboratory. This polymer consisted of a stretchable polyanion which served as a scaffold for the polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT). The scaffold was synthesized using controlled radical polymerization to afford material of low dispersity, relatively high conductivity (0.1 S cm), and low impedance relative to metals. The glove was equipped with flex sensors to make it possible to control a robotic hand and a hand in virtual reality (VR). In psychophysical experiments, human participants were able to discern combinations of electrotactile, vibrotactile, and thermal stimulation in VR. Participants trained to associate these sensations with roughness, hardness, and temperature had an overall accuracy of 98%, while untrained participants had an accuracy of 85%. Sensations could similarly be conveyed using a robotic hand equipped with sensors for pressure and temperature.

Controlled/living radical polymerization (CLRP) techniques are widely utilized to synthesize advanced and controlled synthetic polymers for chemical and biological applications. While automation has long stood as a high-throughput (HTP) research tool to increase productivity as well as synthetic/analytical reliability and precision, oxygen intolerance of CLRP has limited the widespread adoption of these systems. Recently, however, oxygen-tolerant CLRP techniques, such as oxygen-tolerant photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer (PET-RAFT), enzyme degassing of RAFT (Enz-RAFT), and atom-transfer radical polymerization (ATRP), have emerged. Herein, the use of a Hamilton MLSTARlet liquid handling robot for automating CLRP reactions is demonstrated. Synthesis processes are developed using Python and used to automate reagent handling, dispensing sequences, and synthesis steps required to create homopolymers, random heteropolymers, and block copolymers in 96-well plates, as well as postpolymerization modifications. Using this approach, the synergy between highly customizable liquid handling robotics and oxygen-tolerant CLRP to automate advanced polymer synthesis for HTP and combinatorial polymer research is demonstrated.