Dataset Information

The rheology of direct and suspended extrusion bioprinting.

ABSTRACT: Bioprinting is a tool increasingly used in tissue engineering laboratories around the world. As an extension to classic tissue engineering, it enables high levels of control over the spatial deposition of cells, materials, and other factors. It is a field with huge promise for the production of implantable tissues and even organs, but the availability of functional bioinks is a barrier to success. Extrusion bioprinting is the most commonly used technique, where high-viscosity solutions of materials and cells are required to ensure good shape fidelity of the printed tissue construct. This is contradictory to hydrogels used in tissue engineering, which are generally of low viscosity prior to cross-linking to ensure cell viability, making them not directly translatable to bioprinting. This review provides an overview of the important rheological parameters for bioinks and methods to assess printability, as well as the effect of bioink rheology on cell viability. Developments over the last five years in bioink formulations and the use of suspended printing to overcome rheological limitations are then discussed.

SUBMITTER: Cooke ME

PROVIDER: S-EPMC7864677 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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The rheology of direct and suspended extrusion bioprinting.

Cooke Megan E ME Rosenzweig Derek H DH

APL bioengineering 20210204 1

Bioprinting is a tool increasingly used in tissue engineering laboratories around the world. As an extension to classic tissue engineering, it enables high levels of control over the spatial deposition of cells, materials, and other factors. It is a field with huge promise for the production of implantable tissues and even organs, but the availability of functional bioinks is a barrier to success. Extrusion bioprinting is the most commonly used technique, where high-viscosity solutions of materi ...[more]

PMID: 33564740

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Project description:Purpose: Bioprinting is becoming an increasingly popular platform technology for engineering a variety of tissue types. Our aim was to identify biomaterials that have been found to be suitable for extrusion 3D bioprinting, outline their biomechanical properties and biocompatibility towards their application for bioprinting specific tissue types. This systematic review provides an in-depth overview of current biomaterials suitable for extrusion to aid bioink selection for specific research purposes and facilitate design of novel tailored bioinks. Methods: A systematic search was performed on EMBASE, PubMed, Scopus and Web of Science databases according to the PRISMA guidelines. References of relevant articles, between December 2006 to January 2018, on candidate bioinks used in extrusion 3D bioprinting were reviewed by two independent investigators against standardised inclusion and exclusion criteria. Data was extracted on bioprinter brand and model, printing technique and specifications (speed and resolution), bioink material and class of mechanical assessment, cell type, viability, and target tissue. Also noted were authors, study design (in vitro/in vivo), study duration and year of publication. Results: A total of 9,720 studies were identified, 123 of which met inclusion criteria, consisting of a total of 58 reports using natural biomaterials, 26 using synthetic biomaterials and 39 using a combination of biomaterials as bioinks. Alginate (n = 50) and PCL (n = 33) were the most commonly used bioinks, followed by gelatin (n = 18) and methacrylated gelatin (GelMA) (n = 16). Pneumatic extrusion bioprinting techniques were the most common (n = 78), followed by piston (n = 28). The majority of studies focus on the target tissue, most commonly bone and cartilage, and investigate only one bioink rather than assessing a range to identify those with the most promising printability and biocompatibility characteristics. The Bioscaffolder (GeSiM, Germany), 3D Discovery (regenHU, Switzerland), and Bioplotter (EnvisionTEC, Germany) were the most commonly used commercial bioprinters (n = 35 in total), but groups most often opted to create their own in-house devices (n = 20). Many studies also failed to specify whether the mechanical data reflected pre-, during or post-printing, pre- or post-crosslinking and with or without cells. Conclusions: Despite the continued increase in the variety of biocompatible synthetic materials available, there has been a shift change towards using natural rather than synthetic bioinks for extrusion bioprinting, dominated by alginate either alone or in combination with other biomaterials. On qualitative analysis, no link was demonstrated between the type of bioink or extrusion technique and the target tissue, indicating that bioprinting research is in its infancy with no established tissue specific bioinks or bioprinting techniques. Further research is needed on side-by-side characterisation of bioinks with standardisation of the type and timing of biomechanical assessment.

Project description:Extrusion-based bioprinting (EBB) represents one of the most used deposition technologies in the field of bioprinting, thanks to key advantages such as the easy-to-use hardware and the wide variety of materials that can be successfully printed. In recent years, research efforts have been focused on implementing a quality control loop for EBB, which can reduce the trial-and-error process necessary to optimize the printing parameters for a specific ink, standardize the results of a print across multiple laboratories, and so accelerate the translation of extrusion bioprinted products to more impactful clinical applications. Due to its capacity to acquire relevant features from a training dataset and generalize to unseen data, machine learning (ML) is currently being studied in literature as a relevant enabling technology for quality control in EBB. In this context, we propose a robust, deep learning-based control loop to automatically optimize the printing parameters and monitor the printing process online. We collected a comprehensive dataset of EBB prints by recording the process with a high-resolution webcam. To model multiple printing scenarios, each video represents a combination of multiple parameters, including printing set-up (either mechanical or pneumatic extrusion), material color, layer height, and infill density. After pre-processing, the collected dataset was used to thoroughly train and evaluate an ad hoc defined convolutional neural network by controlling over-fitting and optimizing the prediction time of the network. Finally, the ML model was used in a control loop to: (i) monitor the printing process and detect if a print with an error could be stopped before completion to save material and time and (ii) automatically optimize the printing parameters by combining the ML model with a previously published mathematical model of the EBB process. Altogether, we demonstrated for the first time how ML techniques can be used to automatize the EBB process, paving the way for a total quality control loop of the printing process.

Dataset Information

The rheology of direct and suspended extrusion bioprinting.

Publications

The rheology of direct and suspended extrusion bioprinting.

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