Local stimulation of osteocytes using a magnetically actuated oscillating beam

Spatial variations in the osteocyte lacuno-canalicular network density and analysis of the connectomic parameters

Osteocyte lacuno-canalicular network (LCN) is comprised of micrometre-sized pores and submicrometric wide channels in bone. Accumulating evidence suggests multiple functions of this network in material transportation, mechanobiological signalling, mineral homeostasis and bone remodelling. Combining rhodamine staining and confocal laser scanning microscopy, the longitudinal cross-sections of six mouse tibiae were imaged, and the connectome of the network was quantified with a focus on the spatial heterogeneities of network density, connectivity and length of canaliculi. In-vivo loading and double calcein labelling on these tibiae allowed differentiating the newly formed bone from the pre-existing regions. The canalicular density of the murine cortical bone varied between 0.174 and 0.243 μm/μm3, and therefore is three times larger than the corresponding value for human femoral midshaft osteons. The spatial heterogeneity of the network was found distinctly more pronounced across the cortex than along the cortex. We found that in regions with a dense network, the LCN conserves its largely tree-like character, but increases the density by including shorter canaliculi. The current study on healthy mice should serve as a motivating starting point to study the connectome of genetically modified mice, including models of bone diseases and of reduced mechanoresponse.

Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm

The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling.

Evolving from Laboratory Toys towards Life-Savers: Small-Scale Magnetic Robotic Systems with Medical Imaging Modalities

Small-scale magnetic robots are remotely actuated and controlled by an externally applied magnetic field. These robots have a characteristic size ranging from several millimetres down to a few nanometres. They are often untethered in order to access constrained and hard-to-reach space buried deep in human body. Thus, they promise to bring revolutionary improvement to minimally invasive diagnostics and therapeutics. However, existing research is still mostly limited to scenarios in over-simplified laboratory environment with unrealistic working conditions. Further advancement of this field demands researchers to consider complex unstructured biological workspace. In order to deliver its promised potentials, next-generation small-scale magnetic robotic systems need to address the constraints and meet the demands of real-world clinical tasks. In particular, integrating medical imaging modalities into the robotic systems is a critical step in their evolution from laboratory toys towards potential life-savers. This review discusses the recent efforts made in this direction to push small-scale magnetic robots towards genuine biomedical applications. This review examines the accomplishment achieved so far and sheds light on the open challenges. It is hoped that this review can offer a perspective on how next-generation robotic systems can not only effectively integrate medical imaging methods, but also take full advantage of the imaging equipments to enable additional functionalities.