Printing GelMA bioinks: a strategy for buildingin vitromodel to study nanoparticle-based minocycline release and cellular protection under oxidative stress.

Owing to its thermoresponsive and photocrosslinking characteristics, gelatin methacryloyl (GelMA)-based biomaterials have gained widespread usage as a novel and promising bioink for three-dimensional bioprinting and diverse biomedical applications. However, the flow behaviors of GelMA during the sol-gel transition, which are dependent on time and temperature, present significant challenges in printing thick scaffolds while maintaining high printability and cell viability. Moreover, the tunable properties and photocrosslinking capabilities of GelMA underscore its potential for localized drug delivery applications. Previous research has demonstrated the successful incorporation of minocycline (MH) into GelMA scaffolds for therapeutic applications. However, achieving a prolonged and sustained release of concentrated MH remains a challenge, primarily due to its small molecular size. The primary aim of this study is to investigate an optimal extrusion printing method for GelMA bioink in extrusion bioprinting, emphasizing its flow behaviors that are influenced by time and temperature. Additionally, this research seeks to explore the potential of GelMA bioink as a carrier for the sustained release of MH, specifically targeting cellular protection against oxidative stress. The material properties of GelMA were assessed and further optimization of the printing process was conducted considering both printability and cell survival. To achieve sustained drug release within GelMA, the study employed a mechanism using metal ion mediation to facilitate the interaction between MH, dextran sulfate (DS), and magnesium, leading to the formation of nanoparticle complexes (MH-DS). Furthermore, a GelMA-basedin vitromodel was developed in order to investigate the cellular protective properties of MH against oxidative stress. The experimental results revealed that the printability and cell viability of GelMA are significantly influenced by the printing duration, nozzle temperature, and GelMA concentrations. Optimal printing conditions were identified based on a thorough assessment of both printability and cell viability. Scaffolds printed under these optimal conditions exhibited exceptional printability and sustained high cell viability. Notably, it was found that lower GelMA concentrations reduced the initial burst release of MH from the MH-dextran sulfate (MH-DS) complexes, thus favoring more controlled, sustained release profiles. Additionally, MH released under these conditions significantly enhanced fibroblast viability in anin vitromodel simulating oxidative stress.

Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine Learning Guided Parameter Recommendation.

Bioprinting is an emerging technology for the construction of complex three-dimensional (3D) constructs used in various biomedical applications. One of the challenges in this field is the delicate manipulation of material properties and various disparate printing parameters to create structures with high fidelity. Understanding the effects of certain parameters and identifying optimal parameters for creating highly accurate structures are therefore a worthwhile subject to investigate. The objective of this study is to investigate high-impact print parameters on the printing printability and develop a preliminary machine learning model to optimize printing parameters. The results of this study will lead to an exploration of machine learning applications in bioprinting and to an improved understanding between 3D printing parameters and structural printability. Reported results include the effects of rheological property, nozzle gauge, nozzle temperature, path height, and ink composition on the printability of Pluronic F127. The developed Support Vector Machine model generated a process map to assist the selection of optimal printing parameters to yield high quality prints with high probability (>75%). Future work with more generalized machine learning models in bioprinting is also discussed in this article. The finding of this study provides a simple tool to improve printability of extrusion-based bioprinting with minimum experimentations.

Printability in extrusion bioprinting.

Extrusion bioprinting has been widely used to extrude continuous filaments of bioink (or the mixture of biomaterial and living cells), layer-by-layer, to build three-dimensional constructs for biomedical applications. In extrusion bioprinting, printability is an important parameter used to measure the difference between the designed construct and the one actually printed. This difference could be caused by the extrudability of printed bioink and/or the structural formability and stability of printed constructs. Although studies have reported in characterizing printability based on the bioink properties and printing process, the concept of printability is often confusingly and, sometimes, conflictingly used in the literature. The objective of this perspective is to define the printability for extrusion bioprinting in terms of extrudability, filament fidelity, and structural integrity, as well as to review the effect of bioink properties, bioprinting process, and construct design on the printability. Challenges related to the printability of extrusion bioprinting are also discussed, along with recommendations for improvements.