Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization

Freeman, Sebastian; Calabrò, Stefano; Williams, Roma D.; Jin, Sha; Ye, Kaiming

doi:10.3389/fbioe.2022.913579

Cited by 16 publications

(8 citation statements)

References 137 publications

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“…Another important property of biomaterials is shear thinning. Under the high shear rate inside nozzle during printing, shear thinning causes a decrease in viscosity to avoid clogging, as well as avoid damaging cells by high shear stress [ 57 ]. The reduction of shear rate after printing in turn increases the viscosity of bioinks, thus ensuring the fidelity of the printed structure.…”

Section: Bioinks For Skin Bioprintingmentioning

confidence: 99%

Advances in 3D skin bioprinting for wound healing and disease modeling

Zhang

Zhao

et al. 2022

Regenerative Biomaterials

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Even with many advances in design strategies over the past three decades, an enormous gap remains between existing tissue engineering skin and natural skin. Currently available in vitro skin models still cannot replicate the three-dimensionality and heterogeneity of the dermal microenvironment sufficiently to recapitulate many of the known characteristics of skin disorder or disease in vivo. Three-dimensional (3D) bioprinting enables precise control over multiple compositions, spatial distributions, and architectural complexity, therefore offering hope for filling the gap of structure and function between natural and artificial skin. Our understanding of wound healing process and skin disease would thus be boosted by the development of in vitro models that could more completely capture the heterogeneous features of skin biology. Here we provide an overview of recent advances in 3D skin bioprinting, as well as design concepts of cells and bioinks suitable for the bioprinting process. We focus on the applications of this technology for engineering physiological or pathological skin model, focusing more specifically on the function of skin appendages and vasculature. We conclude with current challenges and the technical perspective for further development of 3D skin bioprinting.

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Section: Bioinks For Skin Bioprintingmentioning

confidence: 99%

Advances in 3D skin bioprinting for wound healing and disease modeling

Zhang

Zhao

et al. 2022

Regenerative Biomaterials

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“…The process of 3D bioprinting refers to printing and patterning bio-functional materials in a manner of layer-by-layer on substrates or culture dishes containing cell culture medium to maintain cellular adhesion and sustained growth (18,20,22). Bio-functional materials include living cells, basic structure materials and other essential components, which are defined as bioinks (23,24). To reproduce the complex and heterogeneous architecture of organs and tissue, gaining a comprehensive and sufficient understanding of composition and organization of their components is essential.…”

Section: Introductionmentioning

confidence: 99%

“…These technologies include common and noninvasive imaging modalities like magnetic resonance imaging (MRI) and computed tomography (CT) (20). Computer-aided design and computer-aided manufacturing (CAD-CAM) tools, mathematical modeling and machine learning are also used to collect and digitize the complex tomographic and architectural information for tissue (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Recent advances of three-dimensional bioprinting technology in hepato-pancreato-biliary cancer models

Zhuang

Deng

et al. 2023

Front. Oncol.

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Hepato-pancreato-biliary (HPB) cancer is a serious category of cancer including tumors originating in the liver, pancreas, gallbladder and biliary ducts. It is limited by two-dimensional (2D) cell culture models for studying its complicated tumor microenvironment including diverse contents and dynamic nature. Recently developed three-dimensional (3D) bioprinting is a state-of-the-art technology for fabrication of biological constructs through layer-by-layer deposition of bioinks in a spatially defined manner, which is computer-aided and designed to generate viable 3D constructs. 3D bioprinting has the potential to more closely recapitulate the tumor microenvironment, dynamic and complex cell-cell and cell-matrix interactions compared to the current methods, which benefits from its precise definition of positioning of various cell types and perfusing network in a high-throughput manner. In this review, we introduce and compare multiple types of 3D bioprinting methodologies for HPB cancer and other digestive tumors. We discuss the progress and application of 3D bioprinting in HPB and gastrointestinal cancers, focusing on tumor model manufacturing. We also highlight the current challenges regarding clinical translation of 3D bioprinting and bioinks in the field of digestive tumor research. Finally, we suggest valuable perspectives for this advanced technology, including combination of 3D bioprinting with microfluidics and application of 3D bioprinting in the field of tumor immunology.

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“…Machine learning, with its rich experience, is more adept at dealing with new problems [ 13 ] . In recent years, there have been a lot of investigations targeting at combining machine learning with 3D bioprinting, and favorable outcomes have been obtained [ 14 , 15 ] . In this paper, the recent work about 3D bioprinting in bioink preparation, parameter optimization, and defect detection through machine learning are summarized.…”

Section: Introductionmentioning

confidence: 99%

Machine learning boosts three-dimensional bioprinting

Ning¹,

Zhou²,

Joo³

2023

ijb

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Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layer-by-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Data-driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

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Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization

Cited by 16 publications

References 137 publications

Advances in 3D skin bioprinting for wound healing and disease modeling

Advances in 3D skin bioprinting for wound healing and disease modeling

Recent advances of three-dimensional bioprinting technology in hepato-pancreato-biliary cancer models

Machine learning boosts three-dimensional bioprinting

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