A physicochemical-sensing electronic skin for stress response monitoring.

Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine, and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of over 100 hours with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0%, and quantify psychological stress responses with a confidence level of 98.7%.

Multimodal Three-Dimensional Printing for Micro-Modulation of Scaffold Stiffness Through Machine Learning.

The ability to precisely control a scaffold's microstructure and geometry with light-based three-dimensional (3D) printing has been widely demonstrated. However, the modulation of scaffold's mechanical properties through prescribed printing parameters is still underexplored. This study demonstrates a novel 3D-printing workflow to create a complex, elastomeric scaffold with precision-engineered stiffness control by utilizing machine learning. Various printing parameters, including the exposure time, light intensity, printing infill, laser pump current, and printing speed were modulated to print poly (glycerol sebacate) acrylate (PGSA) scaffolds with mechanical properties ranging from 49.3 ± 3.3 kPa to 2.8 ± 0.3 MPa. This enables flexibility in spatial stiffness modulation in addition to high-resolution scaffold fabrication. Then, a neural network-based machine learning model was developed and validated to optimize printing parameters to yield scaffolds with user-defined stiffness modulation for two different vat photopolymerization methods: a digital light processing (DLP)-based 3D printer was utilized to rapidly fabricate stiffness-modulated scaffolds with features on the hundreds of micron scale and a two-photon polymerization (2PP) 3D printer was utilized to print fine structures on the submicron scale. A novel 3D-printing workflow was designed to utilize both DLP-based and 2PP 3D printers to create multiscale scaffolds with precision-tuned stiffness control over both gross and fine geometric features. The described workflow can be used to fabricate scaffolds for a variety of tissue engineering applications, specifically for interfacial tissue engineering for which adjacent tissues possess heterogeneous mechanical properties (e.g., muscle-tendon).

3D printing a biocompatible elastomer for modeling muscle regeneration after volumetric muscle loss.

Volumetric muscle loss (VML) injuries due to trauma, tumor ablation, or other degenerative muscle diseases are debilitating and currently have limited options for self-repair. Advancements in 3D printing allow for the rapid fabrication of biocompatible scaffolds with designer patterns. However, the materials chosen are often stiff or brittle, which is not optimal for muscle tissue engineering. This study utilized a photopolymerizable biocompatible elastomer - poly (glycerol sebacate) acrylate (PGSA) - to develop an in vitro model of muscle regeneration and proliferation into an acellular scaffold after VML injury. Mechanical properties of the scaffold were tuned by controlling light intensity during the 3D printing process to match the specific tension of skeletal muscle. The effect of both geometric (channel sizes between 300 and 600 µm) and biologic (decellularized muscle extracellular matrix (dECM)) cues on muscle progenitor cell infiltration, proliferation, organization, and maturation was evaluated in vitro using a near-infrared fluorescent protein (iRFP) transfected cell line to assess cells in the 3D scaffold. Larger channel sizes and dECM coating were found to enhance cell proliferation and maturation, while no discernable effect on cell alignment was observed. In addition, a pilot experiment was carried out to evaluate the regenerative capacity of this scaffold in vivo after a VML injury. Overall, this platform demonstrates a simple model to study muscle progenitor recruitment and differentiation into acellular scaffolds after VML repair.