Effects of Electrical Stimulation on Articular Cartilage Regeneration with a Focus on Piezoelectric Biomaterials for Articular Cartilage Tissue Repair and Engineering

Stimuli-responsive microcarriers and their application in tissue repair: A review of magnetic and electroactive microcarrier

Microcarrier applications have made great advances in tissue engineering in recent years, which can load cells, drugs, and bioactive factors. These microcarriers can be minimally injected into the defect to help reconstruct a good microenvironment for tissue repair. In order to achieve more ideal performance and face more complex tissue damage, an increasing amount of effort has been focused on microcarriers that can actively respond to external stimuli. These microcarriers have the functions of directional movement, targeted enrichment, material release control, and providing signals conducive to tissue repair. Given the high controllability and designability of magnetic and electroactive microcarriers, the research progress of these microcarriers is highlighted in this review. Their structure, function and applications, potential tissue repair mechanisms, and challenges are discussed. In summary, through the design with clinical translation ability, meaningful and comprehensive experimental characterization, and in-depth study and application of tissue repair mechanisms, stimuli-responsive microcarriers have great potential in tissue repair.

Ultrasound Stimulation of Piezoelectric Nanocomposite Hydrogels Boosts Chondrogenic Differentiation in Vitro, in Both a Normal and Inflammatory Milieu

The use of piezoelectric nanomaterials combined with ultrasound stimulation is emerging as a promising approach for wirelessly triggering the regeneration of different tissue types. However, it has never been explored for boosting chondrogenesis. Furthermore, the ultrasound stimulation parameters used are often not adequately controlled. In this study, we show that adipose-tissue-derived mesenchymal stromal cells embedded in a nanocomposite hydrogel containing piezoelectric barium titanate nanoparticles and graphene oxide nanoflakes and stimulated with ultrasound waves with precisely controlled parameters (1 MHz and 250 mW/cm2, for 5 min once every 2 days for 10 days) dramatically boost chondrogenic cell commitment in vitro. Moreover, fibrotic and catabolic factors are strongly down-modulated: proteomic analyses reveal that such stimulation influences biological processes involved in cytoskeleton and extracellular matrix organization, collagen fibril organization, and metabolic processes. The optimal stimulation regimen also has a considerable anti-inflammatory effect and keeps its ability to boost chondrogenesis in vitro, even in an inflammatory milieu. An analytical model to predict the voltage generated by piezoelectric nanoparticles invested by ultrasound waves is proposed, together with a computational tool that takes into consideration nanoparticle clustering within the cell vacuoles and predicts the electric field streamline distribution in the cell cytoplasm. The proposed nanocomposite hydrogel shows good injectability and adhesion to the cartilage tissue ex vivo, as well as excellent biocompatibility in vivo, according to ISO 10993. Future perspectives will involve preclinical testing of this paradigm for cartilage regeneration.

Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images

Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.

Hyalofast Cartilage Repair Surgery with a Full Load-Bearing Rehabilitation Program One Day after Operation Reduces the Time for Professional Athletes to Return to Play

Background and Objectives: This study evaluated the effectiveness of Hyalofast cartilage repair surgery with an early, full load-bearing rehabilitation program one day after the operation for reducing the time needed for professional athletes to return to play. Materials and Methods: This prospective study included 49 patients aged between 19 and 38 years who had undergone surgical reconstruction of cartilage using the microfracture technique combined with a Hyalofast scaffold. All patients were active professional athletes. Early rehabilitation was implemented from the first postoperative day, fully loading the operated limb. A clinical evaluation was based on the KOOS and SF-36 questionnaires used during subsequent follow-up visits. All patients underwent magnetic resonance imaging (MRI) to evaluate the effect of the surgery after one year. Results: The clinical results demonstrated a statistically significant improvement in the number of complaints about pain and in the quality of life of the patients, measured in all of the applied scales, with comparisons made between six months or one year post-surgery and pre-surgery. Importantly for athletes, the parameter related to sports and recreation improved from 14 ± 11.1 to 95 ± 7.7 6 months after surgery and to 99.8 ± 1.8 one year after surgery. The overall quality of life score improved from 30 ± 18 to 88 ± 8.8 one year after surgery. Conclusions: These results show that this approach significantly shortened the time needed for the athletes to return to sports at the same level as before the surgery (athletes returned to sports in approximately 2.5-3 months). The mean follow-up time was 19.75 months. This technique can be considered a viable option for the treatment of cartilage injuries in professional athletes, allowing them to return to play more quickly in a safe and healthy way.