Three-dimensional bio-printing equipment technologies for tissue engineering and regenerative medicine.
Ontology highlight
ABSTRACT:
SUBMITTER: Ahn SH
PROVIDER: S-EPMC6170866 | biostudies-other | 2016 Dec
REPOSITORIES: biostudies-other
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Project description:Organ and tissue shortage are known as a crucially important public health problem as unfortunately a small percentage of patients receive transplants. In the context of emerging regenerative medicine, researchers are trying to regenerate and replace different organs and tissues such as the liver, heart, skin, and kidney. Liver tissue engineering (TE) enables us to reproduce and restore liver functions, fully or partially, which could be used in the treatment of acute or chronic liver disorders and/or generate an appropriate functional organ which can be transplanted or employed as an extracorporeal device. In this regard, a variety of techniques (e.g., fabrication technologies, cell-based technologies, microfluidic systems and, extracorporeal liver devices) could be applied in tissue engineering in liver regenerative medicine. Common TE techniques are based on allocating stem cell-derived hepatocyte-like cells or primary hepatocytes within a three-dimensional structure which leads to the improvement of their survival rate and functional phenotype. Taken together, new findings indicated that developing liver tissue engineering-based techniques could pave the way for better treatment of liver-related disorders. Herein, we summarized novel technologies used in liver regenerative medicine and their future applications in clinical settings.
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Project description:Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing configurations that are likely to result in low-quality prints and printing configurations that are more promising as a first step toward the development of a recommendation system for identifying suitable printing conditions. The ML-based framework takes as input the printing conditions regarding the material composition and the printing parameters and predicts the quality of the resulting print as either "low" or "high." We investigate two ML-based approaches: a direct classification-based approach that trains a classifier to distinguish between low- and high-quality prints and an indirect approach that uses a regression ML model that approximates the values of a printing quality metric. Both modes are built upon Random Forests. We trained and evaluated the models on a dataset that was generated in a previous study, which investigated fabrication of porous polymer scaffolds by means of extrusion-based 3D printing with a full-factorial design. Our results show that both models were able to correctly label the majority of the tested configurations while a simpler linear ML model was not effective. Additionally, our analysis showed that a full factorial design for data collection can lead to redundancies in the data, in the context of ML, and we propose a more efficient data collection strategy.
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Project description:The fabrication of scaffolds for tissue engineering requires elements of customization depending on the application and is often limited due to the flexibility of the processing technique. This investigation seeks to address this obstacle by utilizing an open-source three-dimensional printing (3DP) system that allows vast customizability and facilitates reproduction of experiments. The effects of processing parameters on printed poly(?-caprolactone) scaffolds with uniform and gradient pore architectures have been characterized with respect to fiber and pore morphology and mechanical properties. The results demonstrate the ability to tailor the fiber diameter, pore size, and porosity through modification of pressure, printing speed, and programmed fiber spacing. A model was also used to predict the compressive mechanical properties of uniform and gradient scaffolds, and it was found that modulus and yield strength declined with increasing porosity. The use of open-source 3DP technologies for printing tissue-engineering scaffolds provides a flexible system that can be readily modified at a low cost and is supported by community documentation. In this manner, the 3DP system is more accessible to the scientific community, which further facilitates the translation of these technologies toward successful tissue-engineering strategies.
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