Biofilm colonies are typically resistant to general antibiotic treatment and require targeted methods for their removal. One of these methods include the use of nanoparticles as carriers for antibiotic delivery, where they randomly circulate in fluid until they make contact with the infected areas. However, the required proximity of the particles to the biofilm results in only moderate efficacy. We demonstrate here that the non-pathogenic magnetotactic bacteria, Magnetosopirrillum gryphiswalense (MSR-1), can be integrated with drug-loaded mesoporous silica microtubes (MSMs) to build controllable microswimmers (biohybrids) capable of antibiotic delivery to target an infectious biofilm. Applying external magnetic guidance capability and swimming power of the MSR-1 cells, the biohybrids are directed to and forcefully pushed into matured Escherichia coli (E. coli) biofilms. Release of the antibiotic, ciprofloxacin (CFX), is triggered by the acidic microenvironment of the biofilm ensuring an efficient drug delivery system. The results reveal the capabilities of a non-pathogenic bacteria species to target and dismantle harmful biofilms, indicating biohybrid systems have great potential for anti-biofilm applications.

It has been shown that magnetic particle imaging (MPI), an imaging method suggested in 2005, is capable of measuring the spatial distribution of magnetic nanoparticles. Since the particles can be administered as biocompatible suspensions, this method promises to perform well as a tracer-based medical imaging technique. It is capable of generating real-time images, which will be useful in interventional procedures, without utilizing any harmful radiation. To obtain a signal from the administered superparamagnetic iron oxide (SPIO) particles, a sinusoidal changing external homogeneous magnetic field is applied. To achieve spatial encoding, a gradient field is superimposed. Conventional MPI works with a spatial encoding field that features a field free point (FFP). To increase sensitivity, an improved spatial encoding field, featuring a field free line (FFL) can be used. Previous FFL scanners, featuring a 1-D excitation, could demonstrate the feasibility of the FFL-based MPI imaging process. In this work, an FFL-based MPI scanner is presented that features a 2-D excitation field and, for the first time, an electronic rotation of the spatial encoding field. Furthermore, the role of relaxation effects in MPI is starting to move to the center of interest. Nevertheless, no reconstruction schemes presented thus far include a dynamical particle model for image reconstruction. A first application of a model that accounts for relaxation effects in the reconstruction of MPI images is presented here in the form of a simplified, but well performing strategy for signal deconvolution. The results demonstrate the high impact of relaxation deconvolution on the MPI imaging process.

Bacteria propel and change direction by rotating long, helical filaments, called flagella. The number of flagella, their arrangement on the cell body and their sense of rotation hypothetically determine the locomotion characteristics of a species. The movement of the most rapid microorganisms has in particular remained unexplored because of additional experimental limitations. We show that magnetotactic cocci with two flagella bundles on one pole swim faster than 500 µm·s⁻¹ along a double helical path, making them one of the fastest natural microswimmers. We additionally reveal that the cells reorient in less than 5 ms, an order of magnitude faster than reported so far for any other bacteria. Using hydrodynamic modeling, we demonstrate that a mode where a pushing and a pulling bundle cooperate is the only possibility to enable both helical tracks and fast reorientations. The advantage of sheathed flagella bundles is the high rigidity, making high swimming speeds possible.

Micro- and nanomotors have seen substantial progress in recent years for biomedical applications. However, three grand challenges remain: (i) high velocities to overcome the blood flow, (ii) spatially selective control to enable complex navigation, and (iii) integration of a medical, tomographic real-time imaging method to acquire feedback information. Here, we report the combination of active magnetic matter and a medical imaging technique, namely magnetic particle imaging (MPI), which addresses these needs. We synthesize ∼200 nm magnetic nanoparticles and observe a macroscopic, collective effect in a homogeneous magnetic field with a rotating field vector. The nanoparticles form a millimeter-sized cloud and reach speeds of 8 mm s^–1. This cloud is imaged and selectively steered with an MPI scanner. Our experimental results are supported by a model that highlights the role of the Mason number, the particle’s volume fraction, and the height of the cloud. The successful introduction of a fast swarm of microscopic units and the spatial selectivity of the technique suggest an effective approach to translate the use of micro- and nanobots into a clinical application.

In magnetic particle imaging, scanners use different spatial sampling techniques to cover the field of view (FOV). As spatial encoding is realized by a selective low field region (a field-free-point, or field-free-line), this region has to be moved through the FOV on specific sampling trajectories. To achieve these trajectories complex time dependent magnetic fields are necessary. Due to the superposition of the selection field and the homogeneous time dependent fields, particles at different spatial positions experience different field sequences. As a result, the dynamic behaviour of those particles can be strongly spatially dependent. So far, simulation studies that determined the trajectory quality have used the Langevin function to model the particle response. This however, neglects the dynamic relaxation of the particles, which is highly affected by magnetic anisotropy. More sophisticated models based on stochastic differential equations that include these effects were only used for one dimensional excitation. In this work, a model based on stochastic differential equations is applied to two-dimensional trajectory field sequences, and the effects of these field sequences on the particle response are investigated. The results show that the signal of anisotropic particles is not based on particle parameters such as size and shape alone, but is also determined by the field sequence that a particle ensemble experiences at its spatial position. It is concluded, that the particle parameters can be optimized in terms of the used trajectory.