Systemic drug administration often causes off-target effects limiting the efficacy of advanced therapies. Targeted drug delivery approaches increase local drug concentrations at the diseased site while minimizing systemic drug exposure. We present a magnetically guided microrobotic drug delivery system capable of precise navigation under physiological conditions. This platform integrates a clinical electromagnetic navigation system, a custom-designed release catheter, and a dissolvable capsule for accurate therapeutic delivery. In vitro tests showed precise navigation in human vasculature models, and in vivo experiments confirmed tracking under fluoroscopy and successful navigation in large animal models. The microrobot balances magnetic material concentration, contrast agent loading, and therapeutic drug capacity, enabling effective hosting of therapeutics despite the integration complexity of its components, offering a promising solution for precise targeted drug delivery.

Fabian C. Landers, Lukas Hertle, Vitaly Pustovalov, Derick Sivakumaran, Oliver Brinkmann, Kirstin Meiners, Pascal Theiler, Valentin Gantenbein, Andrea Veciana, Michael Mattmann, Silas Riss, Simone Gervasoni, Christophe Chautems, Hao Ye, Semih Sevim, Andreas D. Flouris, Josep Puigmartí-Luis, Tiago Sotto Mayor, Pedro Alves, Tessa Lühmann, Xiangzhong Chen, Nicole Ochsenbein, Ueli Moehrlen, Philipp Gruber, Miriam Weisskopf, Quentin Boehler, Salvador Pané, Bradley J. Nelson https://doi.org/10.48550/arXiv.2501.11553

Interfacial strain engineering in ferroic nanomembranes can broaden the scope of ferroic nanomembrane assembly as well as facilitate the engineering of multiferroic-based devices with enhanced functionalities. Geometrical engineering in these material systems enables the realization of 3-D architectures with unconventional physical properties. Here, 3-D multiferroic architectures are introduced by incorporating barium titanate (BaTiO₃, BTO) and cobalt ferrite (CoFe₂O₄, CFO) bilayer nanomembranes. Using photolithography and substrate etching techniques, complex 3-D microarchitectures including helices, arcs, and kirigami-inspired frames are developed. These 3-D architectures exhibit remarkable mechanical deformation capabilities, which can be attributed to the superelastic behavior of the membranes and geometric configurations. It is also demonstrated that dynamic shape reconfiguration of these nanomembrane architectures under electron beam exposure showcases their potential as electrically actuated microgrippers and for other micromechanical applications. This research highlights the versatility and promise of multi-dimensional ferroic nanomembrane architectures in the fields of micro actuation, soft robotics, and adaptive structures, paving the way for incorporating these architectures into stimulus-responsive materials and devices.

M. Kim, D. Kim, M. Mirjolet, N. A. Shepelin, T. Lippert, H. Choi, J. Puigmartí-Luis, B. J. Nelson, X.-Z. Chen, S. Pané, Shape-Morphing in Oxide Ceramic Kirigami Nanomembranes. Adv. Mater. 2024, 2404825. https://doi.org/10.1002/adma.202404825

Iron oxide nanoparticles hold great potential for future biomedical applications but, to date, usually suffer from reduced magnetic properties compared to their bulk counterparts. The replacement of Fe(III) ions with Zn(II) ions can enhance their magnetic properties while keeping their biocompatibility characteristics. Yet, common synthesis methods for these highly magnetic particles require using environmentally harmful solvents, multiple steps, and postfunctionalization, all while being affected by poor scalability and high polydispersity. To address these challenges, in this study, a single-step coprecipitation-based method is developed to fabricate gelatin-coated, zinc-substituted, sub-10 nm-sized iron oxide nanoparticles exhibiting high saturation magnetization. This single-step synthesis benefits from simplicity and robustness, capable of yielding large amounts of highly magnetic nanoparticles without the utilization of environmentally harmful or highly toxic reagents. Furthermore, in situ gelatin coating during the synthesis ensures particle stability in aqueous solutions over a wide range of pH and enhances cell compatibility. Systematic investigations show a direct correlation between the particles’ magnetization and the concentrations of Zn(II) and NaOH, where particles with a zinc-to-iron ratio of Zn:Fe = 0.18:2.82 reach a maximum saturation magnetization of 91.2 emu g⁻¹. Thus, these particles are promising candidates for biomedical applications.

Pustovalov, V., Landers, F.C., Hertle, L., Ko, H., Llacer-Wintle, J., Ye, H., Veciana, A., Franco, C., Sanchis-Gual, R., Chen, X.-Z., Pellicer, E., Puigmartí-Luis, J., Nelson, B.J. and Pané, S. (2024), Single-Step Synthesis of Sub-10 nm Magnetic Nanoparticles with High Saturation Magnetization and Broad pH Stability. Adv. Eng. Mater., 26: 2400307. https://doi.org/10.1002/adma.202310701

Magnetic navigation systems are used to precisely manipulate magnetically responsive materials enabling the realization of new minimally invasive procedures using magnetic medical devices. Their widespread applicability has been constrained by high infrastructure demands and costs. The study reports on a portable electromagnetic navigation system, the Navion, which is capable of generating a large magnetic field over a large workspace. The system is easy to install in hospital operating rooms and transportable through health care facilities, aiding in the widespread adoption of magnetically responsive medical devices. First, the design and implementation approach for the system are introduced and its performance is characterized. Next, in vitro navigation of different microrobot structures is demonstrated using magnetic field gradients and rotating magnetic fields. Spherical permanent magnets, electroplated cylindrical microrobots, microparticle swarms, and magnetic composite bacteria-inspired helical structures are investigated. The navigation of magnetic catheters is also demonstrated in two challenging endovascular tasks: 1) an angiography procedure and 2) deep navigation within the circle of Willis. Catheter navigation is demonstrated in a porcine model in vivo to perform an angiography under magnetic guidance.

S. Gervasoni, N. Pedrini, T. Rifai, C. Fischer, F. C. Landers, M. Mattmann, R. Dreyfus, S. Viviani, A. Veciana, E. Masina, B. Aktas, J. Puigmartí-Luis, C. Chautems, S. Pané, Q. Boehler, P. Gruber, B. J. Nelson, A Human-Scale Clinically Ready Electromagnetic Navigation System for Magnetically Responsive Biomaterials and Medical Devices. Adv. Mater. 2024, 2310701 https://doi.org/10.1002/adma.202310701

A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.

Sarwin, G., Lussi, J., Gervasoni, S. et al. Patient-specific placental vessel segmentation with limited data. J Robotic Surg 18, 237 (2024). https://doi.org/10.1007/s11701-024-01981-z