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.

Uneven Terrain Recognition Using Neuromorphic Haptic Feedback.

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot-ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb.

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions.

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications.