Electroactive biomaterials are widely explored as scaffolds for nerve tissue regeneration. Poly(3,4-ethylenedioxythiophene) (PEDOT) is a conductive polymer that has been chosen to construct tissue engineered scaffolds because of its excellent conductivity and non-cytotoxicity. In the present study, an electrically conductive scaffold was prepared by assembling PEDOT on a chitosan/gelatin (Cs/Gel) porous scaffold surface via in situ interfacial polymerization. The hydrophilic Cs/Gel hydrogel was used as a template, and PEDOT nanoparticles were uniformly assembled on the scaffold surface. The static polymerization of the 3,4-ethylenedioxythiophene (EDOT) monomer at the interface between the aqueous phase and the organic phase was accompanied by the formation of the PEDOT-assembled Cs/Gel scaffolds. PEDOT/Cs/Gel scaffolds were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. The results confirmed the deposition of PEDOT nanoparticles with the mean diameter of 50 nm on the Cs/Gel scaffold channel surface. Compared to the Cs/Gel scaffold, the incorporation of PEDOT on the scaffold increased the electrical conductivity, hydrophilicity, mechanical properties and thermal stability, whereas decreased the water absorption and biodegradation. For biocompatibility, PEDOT/Cs/Gel scaffolds, especially the 2PEDOT/Cs/Gel scaffold group, significantly promoted neuron-like rat pheochromocytoma (PC12) cell adhesion and proliferation. The results of both the gene expression and protein level assessments suggested that the PEDOT-assembled Cs/Gel scaffold enhanced the PC12 cellular neurite growth with higher protein and gene expression levels. This is the first report on the construction of a conductive PEDOT/Cs/Gel porous scaffold via an in situ interfacial polymerization method, and the results demonstrate that it may be a promising conductive scaffold for neural tissue engineering.

Tissue engineering scaffolds provide biological and physiochemical cures to guide tissue recovery, and electrical signals through the electroactive materials possess tremendous potential to modulate the cell fate. In this study, a novel electroactive hydrogel scaffold was fabricated by assembling poly(3,4-ethylenedioxythiophene) (PEDOT) nanoparticles on a carboxymethyl chitosan/gelatin (CMCS/Gel) composite hydrogel surface via in situ chemical polymerization. The chemical structure, morphology, conductivity, porosity, swelling rate, in vitro biodegradation, and mechanical properties of the prepared hydrogel samples were characterized. The adhesion, proliferation, and differentiation of neural stem cells (NSCs) on conductive hydrogels were investigated. The CMCS/Gel-PEDOT hydrogels exhibited high porosity, excellent water absorption, improved thermal stability, and adequate biodegradability. Importantly, the mechanical properties of the prepared hydrogels were similar to those of brain tissue, with electrical conductivity up to (1.52 ± 0.15) × 10⁻³ S/cm. Compared to the CMCS/Gel hydrogel, the incorporation of PEDOT nanoparticles significantly improved the adhesion of NSCs, and supported long-term cell growth and proliferation in a three-dimensional (3D) microenvironment. In addition, under the differentiation condition, the conductive hydrogel also significantly enhanced neuronal differentiation with the up-regulation of β-tubulin III expression. These results suggest that CMCS/Gel-PEDOT hydrogels may be an attractive conductive substrate for further studies on neural tissue repair and regeneration.

This study investigates the infinite-horizon optimal control (IHOC) problem for switched Boolean control networks with an average cost criterion. A primary challenge of this problem is the prohibitively high computational cost when dealing with large-scale networks. We attempt to develop a more efficient approach from a novel graph-theoretical perspective. First, a weighted directed graph structure called the optimal state transition graph (OSTG) is established, whose edges encode the optimal action for each admissible state transition between states reachable from a given initial state subject to various constraints. Then, we reduce the IHOC problem into a minimum-mean cycle (MMC) problem in the OSTG. Finally, we develop an algorithm that can quickly find a particular MMC by resorting to Karp's algorithm in the graph theory and construct an optimal switching control law based on state feedback. The time complexity analysis shows that our algorithm, albeit still running in exponential time, can outperform all the existing methods in terms of time efficiency. A 16-state-3-input signaling network in leukemia is used as a benchmark to test its effectiveness. Results show that the proposed graph-theoretical approach is much more computationally efficient and can reduce the running time dramatically: it runs hundreds or even thousands of times faster than the existing methods. The Python implementation of the algorithm is available at https://github.com/ShuhuaGao/sbcn_mmc.