Quantitative Biofabrication Platform for Collagen-Based Peripheral Nerve Grafts with Structural and Chemical Guidance

Owing to its crucial role in the human body, collagen has immense potential as a material for the biofabrication of tissues and organs. However, highly refined fabrication using collagen remains difficult, primarily because of its notably soft properties. A quantitative biofabrication platform to construct collagen-based peripheral nerve grafts, incorporating bionic structural and chemical guidance cues, is introduced. A viscoelastic model for collagen, which facilitates simulating material relaxation and fabricating collagen-based neural grafts, achieving a maximum channel density similar to that of the native nerve structure of longitudinal microchannel arrays, is established. For axonal regeneration over considerable distances, a gradient printing control model and quantitative method are developed to realize the high-precision gradient distribution of nerve growth factor required to obtain nerve grafts through one-step bioprinting. Experiments verify that the bioprinted graft effectively guides linear axonal growth in vitro and in vivo. This study should advance biofabrication methods for a variety of human tissue-engineering applications requiring tailored cues.

Cartilage Lacuna-Inspired Microcarriers Drive Hyaline Neocartilage Regeneration

Cartilage equivalents from hydrogels containing chondrocytes exhibit excellent potential in hyaline cartilage regeneration, yet current approaches have limited success at reconstituting the architecture to culture nondifferentiated chondrocytes in vitro. In this study, specially designed lacunar hyaluronic acid microcarriers (LHAMCs) with mechanotransductive conditions that rapidly form stable hyaluronic acid (HA) N-hydroxy succinimide ester (NHS-ester) are reported. Specifically, carboxyl-functionalized HA is linked to collagen type I via amide-crosslinking, and gas foaming produced by ammonium bicarbonate forms concave surface of the microcarriers. The temporal 3D culture of chondrocytes on LHAMCs uniquely remodels the extracellular matrix to induce hyaline cartilaginous microtissue regeneration and prevents an anaerobic-to-aerobic metabolism transition in response to the geometric constraints. Furthermore, by inhibiting the canonical Wnt pathway, LHAMCs prevent β-catenin translocation to the nucleus, repressing chondrocyte dedifferentiation. Additionally, the subcutaneous implantation model indicates that LHAMCs display favorable cytocompatibility and drive robust hyaline chondrocyte-derived neocartilage formation. These findings reveal a novel strategy for regulating chondrocyte dedifferentiation. The current study paves the way for a better understanding of geometrical insight clues into mechanotransduction interaction in regulating cell fate, opening new avenues for advancing tissue engineering.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.