Dataset Information

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.

ABSTRACT: Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.

SUBMITTER: Kim J

PROVIDER: S-EPMC6967031 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.

Kim Joohyun J McKee Jane A JA Fontenot Jake J JJ Jung Jangwook P JP

Frontiers in bioengineering and biotechnology 20200110

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images ...[more]

PMID: 31998708

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Project description:Periodontitis is a chronic infectious disease and is the major cause of tooth loss and other oral health issues around the world. Periodontal tissue regeneration has therefore always been the ultimate goal of dentists and researchers. Existing fabrication methods mainly focused on a top-down tissue engineering strategy in which several drawbacks remain, including low throughput and limited diffusion properties resulting from a large sample size. Gelatin methacrylate (GelMA) is a kind of photocrosslinkable and biocompatible hydrogel, with the capacities of enabling cell encapsulation and regeneration of functional tissues. Here, we developed a novel method to fabricate GelMA/nanohydroxylapatite (nHA) microgel arrays using a photocrosslinkable strategy. The viability, proliferation, and osteogenic differentiation and in vivo osteogenesis of human periodontal ligament stem cells (hPDLSCs) encapsulated in microgels were evaluated. The results suggested that such microgels provide great potential for periodontal tissue repair and regeneration.Microgel arrays were fabricated by blending different weight ratios of GelMA and nHA. hPDLSCs were encapsulated in GelMA/nHA microgels of various ratios for a systematic evaluation of cell viability, proliferation, and osteogenic differentiation. In vivo osteogenesis in nude mice was also studied.The GelMA/nHA microgels exhibited appropriate microarchitecture, mechanical strength, and surface roughness, thus enabling cell adhesion and proliferation. Additionally, the GelMA/nHA microgels (10%/2% w/v) enhanced the osteogenic differentiation of hPDLSCs by elevating the expression levels of osteogenic biomarker genes, such as ALP, BSP, OCN, and RUNX2. In vivo ectopic transplantation results showed that GelMA/nHA microgels (10%/2% w/v) increased mineralized tissue formation with abundant vascularization, compared with the 1%, 3%, and the pure GelMA group.The GelMA/nHA microgels (10%/2% w/v) facilitated hPDLSCs viability, proliferation, and osteogenic differentiation in vitro and further promoted new bone formation in vivo, suggesting that the GelMA/nHA microgels (10%/2% w/v) provide great potential for periodontal tissue regeneration.

Dataset Information

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.

Publications

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.

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