One-Step Bioprinting of Multi-Channel Hydrogel Filaments Using Chaotic Advection: Fabrication of Pre-Vascularized Muscle-Like Tissues

The biofabrication of living constructs containing hollow channels is critical for manufacturing thick tissues. However, current technologies are limited in their effectiveness in the fabrication of channels with diameters smaller than hundreds of micrometers. It is demonstrated that the co-extrusion of cell-laden hydrogels and sacrificial materials through printheads containing Kenics static mixing elements enables the continuous and one-step fabrication of thin hydrogel filaments (1 mm in diameter) containing dozens of hollow microchannels with widths as small as a single cell. Pre-vascularized skeletal muscle-like filaments are bioprinted by loading murine myoblasts (C2C12 cells) in gelatin methacryloyl - alginate hydrogels and using hydroxyethyl cellulose as a sacrificial material. Higher viability and metabolic activity are observed in filaments with hollow multi-channels than in solid constructs. The presence of hollow channels promotes the expression of Ki67 (a proliferation biomarker), mitigates the expression of hypoxia-inducible factor 1-alpha , and markedly enhances cell alignment (i.e., 82% of muscle myofibrils aligned (in ±10°) to the main direction of the microchannels after seven days of culture). The emergence of sarcomeric α-actin is verified through immunofluorescence and gene expression. Overall, this work presents an effective and practical tool for the fabrication of pre-vascularized engineered tissues.

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg-Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.