Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing

Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.

Recent Developments in Bio-Ink Formulations Using Marine-Derived Biomaterials for Three-Dimensional (3D) Bioprinting

3D bioprinting is a disruptive, computer-aided, and additive manufacturing technology that allows the obtention, layer-by-layer, of 3D complex structures. This technology is believed to offer tremendous opportunities in several fields including biomedical, pharmaceutical, and food industries. Several bioprinting processes and bio-ink materials have emerged recently. However, there is still a pressing need to develop low-cost sustainable bio-ink materials with superior qualities (excellent mechanical, viscoelastic and thermal properties, biocompatibility, and biodegradability). Marine-derived biomaterials, including polysaccharides and proteins, represent a viable and renewable source for bio-ink formulations. Therefore, the focus of this review centers around the use of marine-derived biomaterials in the formulations of bio-ink. It starts with a general overview of 3D bioprinting processes followed by a description of the most commonly used marine-derived biomaterials for 3D bioprinting, with a special attention paid to chitosan, glycosaminoglycans, alginate, carrageenan, collagen, and gelatin. The challenges facing the application of marine-derived biomaterials in 3D bioprinting within the biomedical and pharmaceutical fields along with future directions are also discussed.

Enhancing quality control in bioprinting through machine learning

Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

Application of artificial intelligence in 3D printing physical organ models

Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact humanity. 3D printing of patient-specific organ models is expected to replace animal carcasses, providing scenarios that simulate the surgical environment for preoperative training and educating patients to propose effective solutions. Due to the complexity of 3D printing manufacturing, it is still used on a small scale in clinical practice, and there are problems such as the low resolution of obtaining MRI/CT images, long consumption time, and insufficient realism. AI has been effectively used in 3D printing as a powerful problem-solving tool. This paper introduces 3D printed organ models, focusing on the idea of AI application in 3D printed manufacturing of organ models. Finally, the potential application of AI to 3D-printed organ models is discussed. Based on the synergy between AI and 3D printing that will benefit organ model manufacturing and facilitate clinical preoperative training in the medical field, the use of AI in 3D-printed organ model making is expected to become a reality.

Effects of surface patterning and topography on the cellular functions of tissue engineered scaffolds with special reference to 3D bioprinting

The extracellular matrix (ECM) of the tissue organ exhibits a topography from the nano to micrometer range, and the design of scaffolds has been inspired by the host environment. Modern bioprinting aims to replicate the host tissue environment to mimic the native physiological functions. A detailed discussion on the topographical features controlling cell attachment, proliferation, migration, differentiation, and the effect of geometrical design on the wettability and mechanical properties of the scaffold are presented in this review. Moreover, geometrical pattern-mediated stiffness and pore arrangement variations for guiding cell functions have also been discussed. This review also covers the application of designed patterns, gradients, or topographic modulation on 3D bioprinted structures in fabricating the anisotropic features. Finally, this review accounts for the tissue-specific requirements that can be adopted for topography-motivated enhancement of cellular functions during the fabrication process with a special thrust on bioprinting.

Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives

Hydrogel ink formulations based on rheology additives are becoming increasingly popular as they enable 3-dimensional (3D) printing of non-printable but biologically relevant materials. Despite the widespread use, a generalized understanding of how these hydrogel formulations become printable is still missing, mainly due to their variety and diversity. Employing an interpretable machine learning approach allows the authors to explain the process of rendering printability through bulk rheological indices, with no bias toward the composition of formulations and the type of rheology additives. Based on an extensive library of rheological data and printability scores for 180 different formulations, 13 critical rheological measures that describe the printability of hydrogel formulations, are identified. Using advanced statistical methods, it is demonstrated that even though unique criteria to predict printability on a global scale are highly unlikely, the accretive and collaborative nature of rheological measures provides a qualitative and physically interpretable guideline for designing new printable materials.

Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization

Bioprinting enables the fabrication of complex, heterogeneous tissues through robotically-controlled placement of cells and biomaterials. It has been rapidly developing into a powerful and versatile tool for tissue engineering. Recent advances in bioprinting modalities and biofabrication strategies as well as new materials and chemistries have led to improved mimicry and development of physiologically relevant tissue architectures constituted with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and enable real-time error detection as well as to reduce the iterative steps necessary for bioink formulation. We examined how rheometric properties, including shear storage, loss moduli, viscosity, shear-thinning property of biomaterials affect the printability of a bioink. Furthermore, we scrutinized the interplays between yield shear stress and the printability of a bioink. Moreover, we systematically surveyed the application of ML in precision in situ surgical site bioprinting, closed-loop AI printing, and post-printing optimization.

Scalable Biofabrication: A Perspective on the Current State and Future Potentials of Process Automation in 3D-Bioprinting Applications

Biofabrication, specifically 3D-Bioprinting, has the potential to disruptively impact a wide range of future technological developments to improve human well-being. Organs-on-Chips could enable animal-free and individualized drug development, printed organs may help to overcome non-treatable diseases as well as deficiencies in donor organs and cultured meat may solve a worldwide environmental threat in factory farming. A high degree of manual labor in the laboratory in combination with little trained personnel leads to high costs and is along with strict regulations currently often a hindrance to the commercialization of technologies that have already been well researched. This paper therefore illustrates current developments in process automation in 3D-Bioprinting and provides a perspective on how the use of proven and new automation solutions can help to overcome regulatory and technological hurdles to achieve an economically scalable production.

Printability and Cell Viability in Extrusion-Based Bioprinting from Experimental, Computational, and Machine Learning Views

Extrusion bioprinting is an emerging technology to apply biomaterials precisely with living cells (referred to as bioink) layer by layer to create three-dimensional (3D) functional constructs for tissue engineering. Printability and cell viability are two critical issues in the extrusion bioprinting process; printability refers to the capacity to form and maintain reproducible 3D structure and cell viability characterizes the amount or percentage of survival cells during printing. Research reveals that both printability and cell viability can be affected by various parameters associated with the construct design, bioinks, and bioprinting process. This paper briefly reviews the literature with the aim to identify the affecting parameters and highlight the methods or strategies for rigorously determining or optimizing them for improved printability and cell viability. This paper presents the review and discussion mainly from experimental, computational, and machine learning (ML) views, given their promising in this field. It is envisioned that ML will be a powerful tool to advance bioprinting for tissue engineering.

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.

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

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.