Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability

Enhanced hyaline cartilage formation and continuous osteochondral regeneration via 3D-Printed heterogeneous hydrogel with multi-crosslinking inks

The unique gradient structure and complex composition of osteochondral tissue pose significant challenges in defect regeneration. Restoration of tissue heterogeneity while maintaining hyaline cartilage components has been a difficulty of an osteochondral tissue graft. A novel class of multi-crosslinked polysaccharide-based three-dimensional (3D) printing inks, including decellularized natural cartilage (dNC) and nano-hydroxyapatite, was designed to create a gradient scaffold with a robust interface-binding force. Herein, we report combining a dual-nozzle cross-printing technology and a gradient crosslinking method to create the scaffolds, demonstrating stable mechanical properties and heterogeneous bilayer structures. Biofunctional assessments revealed the remarkable regenerative effects of the scaffold, manifesting three orders of magnitude of mRNA upregulation during chondrogenesis and the formation of pure hyaline cartilage. Transcriptomics of the regeneration site in vivo and scaffold cell interaction tests in vitro showed that printed porous multilayer scaffolds could form the correct tissue structure for cell migration. More importantly, polysaccharides with dNC provided a hydrophilic microenvironment. The microenvironment is crucial in osteochondral regeneration because it could guide the regenerated cartilage to ensure the hyaline phenotype.

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.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

A micro-fragmented collagen gel as a cell-assembling platform for critical limb ischemia repair

Critical limb ischemia (CLI) is a devastating disease characterized by the progressive blockage of blood vessels. Although the paracrine effect of growth factors in stem cell therapy made it a promising angiogenic therapy for CLI, poor cell survival in the harsh ischemic microenvironment limited its efficacy. Thus, an imperative need exists for a stem-cell delivery method that enhances cell survival. Here, a collagen microgel (CMG) cell-delivery scaffold (40 × 20 μm) was fabricated via micro-fragmentation from collagen-hyaluronic acid polyionic complex to improve transplantation efficiency. Culturing human adipose-derived stem cells (hASCs) with CMG enabled integrin receptors to interact with CMG to form injectable 3-dimensional constructs (CMG-hASCs) with a microporous microarchitecture and enhanced mass transfer. CMG-hASCs exhibited higher cell survival (p < 0.0001) and angiogenic potential in tube formation and aortic ring angiogenesis assays than cell aggregates. Injection of CMG-hASCs intramuscularly into CLI mice increased blood perfusion and limb salvage ratios by 40 % and 60 %, respectively, compared to cell aggregate-treated mice. Further immunofluorescent analysis revealed that transplanted CMG-hASCs have greater muscle regenerative and angiogenic potential, with enhanced cell survival than cell aggregates (p < 0.05). Collectively, we propose CMG as a cell-assembling platform and CMG-hASCs as promising therapeutics to treat CLI.

In situ polymer gelation in confined flow controls intermittent dynamics

Polymer flows through pores, nozzles and other small channels govern engineered and naturally occurring dynamics in many processes, from 3D printing to oil recovery in the earth's subsurface to a wide variety of biological flows. The crosslinking of polymers can change their material properties dramatically, and it is advantageous to know a priori whether or not crosslinking polymers will lead to clogged channels or cessation of flow. In this study, we investigate the flow of a common biopolymer, alginate, while it undergoes crosslinking by the addition of a crosslinker, calcium, driven through a microfluidic channel at constant flow rate. We map the boundaries defining complete clogging and flow as a function of flow rate, polymer concentration, and crosslinker concentration. Interestingly, the boundaries of the dynamic behavior qualitatively match the thermodynamic jamming phase diagram of attractive colloidal particles. That is, polymer clogging occurs in a region analogous to colloids in a jammed state, while the polymer flows in regions corresponding to colloids in a liquid phase. However, between the dynamic regimes of complete clogging and unrestricted flow, we observe a remarkable phenomenon in which the crosslinked polymer intermittently clogs the channel. This pattern of deposition and removal of a crosslinked gel is simultaneously highly reproducible, long-lasting, and controllable by system parameters. Higher concentrations of polymer and cross-linker result in more frequent ablation, while gels formed at lower component concentrations ablate less frequently. Upon ablation, the eluted gel maintains its shape, resulting in micro-rods several hundred microns long. Our results suggest both rich dynamics of intermittent flows in crosslinking polymers and the ability to control them.

Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models

The fields of regenerative medicine and cancer modeling have witnessed tremendous growth in the application of 3D bioprinting. Maintaining high cell viability throughout the bioprinting process is crucial for the success of this technology, as it directly affects the accuracy of the 3D bioprinted models, the validity of experimental results, and the discovery of new therapeutic approaches. Therefore, optimizing bioprinting conditions, which include numerous variables influencing cell viability during and after the procedure, is of utmost importance to achieve desirable results. So far, these optimizations have been accomplished primarily through trial and error and repeating multiple time-consuming and costly experiments. To address this challenge, we initiated the process by creating a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed machine learning models to predict cell viability based on different bioprinting variables. The trained neural network yielded regressionR2value of 0.71 and classification accuracy of 0.86. Compared to models that have been developed so far, the performance of our models is superior and shows great prediction results. The study further introduces a novel optimization strategy that employs the Bayesian optimization model in combination with the developed regression neural network to determine the optimal combination of the selected bioprinting parameters to maximize cell viability and eliminate trial-and-error experiments. Finally, we experimentally validated the optimization model's performance.

Extracellular Vesicles and Hydrogels: An Innovative Approach to Tissue Regeneration

Extracellular vesicles have emerged as promising tools in regenerative medicine due to their inherent ability to facilitate intercellular communication and modulate cellular functions. These nanosized vesicles transport bioactive molecules, such as proteins, lipids, and nucleic acids, which can affect the behavior of recipient cells and promote tissue regeneration. However, the therapeutic application of these vesicles is frequently constrained by their rapid clearance from the body and inability to maintain a sustained presence at the injury site. In order to overcome these obstacles, hydrogels have been used as extracellular vesicle delivery vehicles, providing a localized and controlled release system that improves their therapeutic efficacy. This Review will examine the role of extracellular vesicle-loaded hydrogels in tissue regeneration, discussing potential applications, current challenges, and future directions. We will investigate the origins, composition, and characterization techniques of extracellular vesicles, focusing on recent advances in exosome profiling and the role of machine learning in this field. In addition, we will investigate the properties of hydrogels that make them ideal extracellular vesicle carriers. Recent studies utilizing this combination for tissue regeneration will be highlighted, providing a comprehensive overview of the current research landscape and potential future directions.

Hydrogel-mediated extracellular vesicles for enhanced wound healing: the latest progress, and their prospects for 3D bioprinting

Extracellular vesicles have shown promising tissue recovery-promoting effects, making them increasingly sought-after for their therapeutic potential in wound treatment. However, traditional extracellular vesicle applications suffer from limitations such as rapid degradation and short maintenance during wound administration. To address these challenges, a growing body of research highlights the role of hydrogels as effective carriers for sustained extracellular vesicle release, thereby facilitating wound healing. The combination of extracellular vesicles with hydrogels and the development of 3D bioprinting create composite hydrogel systems boasting excellent mechanical properties and biological activity, presenting a novel approach to wound healing and skin dressing. This comprehensive review explores the remarkable mechanical properties of hydrogels, specifically suited for loading extracellular vesicles. We delve into the diverse sources of extracellular vesicles and hydrogels, analyzing their integration within composite hydrogel formulations for wound treatment. Different composite methods as well as 3D bioprinting, adapted to varying conditions and construction strategies, are examined for their roles in promoting wound healing. The results highlight the potential of extracellular vesicle-laden hydrogels as advanced therapeutic tools in the field of wound treatment, offering both mechanical support and bioactive functions. By providing an in-depth examination of the various roles that these composite hydrogels can play in wound healing, this review sheds light on the promising directions for further research and development. Finally, we address the challenges associated with the application of composite hydrogels, along with emerging trends of 3D bioprinting in this domain. The discussion covers issues such as scalability, regulatory considerations, and the translation of this technology into practical clinical settings. In conclusion, this review underlines the significant contributions of hydrogel-mediated extracellular vesicle therapy to the field of 3D bioprinting and wound healing and tissue regeneration. It serves as a valuable resource for researchers and practitioners alike, fostering a deeper understanding of the potential benefits, applications, and challenges involved in utilizing composite hydrogels for wound treatment.

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.

Unraveling the influence of solvent composition on Drop-on-Demand binder jet 3D printed tablets containing calcium sulfate hemihydrate

Recently, binder jet printed modular tablets were loaded with three anti-viral drugs via Drop on Demand (DoD) technology where drug solutions prepared in ethanol showed faster release than those prepared in water. During printing, water is used as a binding agent, whereas ethanol is added to maintain the porous structure of the tablets. Thus, the hypothesis is that the porosity would be controlled by manipulating the percentage of water and ethanol. In this study, Rhodamine 6G (R6G) was selected as a model drug due to its high solubility in water and ethanol, visualization function as a fluorescent dye, and potential therapeutic effects for cancer treatment. Approximately, 10 mg/ml R6G solutions were prepared with five different water-ethanol ratios (0-100, 75-25, 50-50, 75-25, 100-0). The ink solutions were printed onto blank binder jet 3D-printed tablets containing calcium sulphate hemihydrate using DoD technology. The tablets were dried at room temperature and then characterized using SEM-EDX, fluorescent microscope, TGA, XRD, FTIR, and DSC as well as in vitro release studies to investigate the impact of water-ethanol ratio on the release profile of R6G. Results indicated that the solution with higher ethanol ratio penetrated the tablets faster than the lower ethanol ratio, while the solution prepared with pure water was first accumulated onto the tablets' surface and then absorbed by the tablets. Moreover, tablets with more water content gained more weight and thickness. The EDX analysis and fluorescent microscope showed the uniform surface distribution of the drug. The SEM images revealed the difference in the tablet surface among the five formulations. Furthermore, the TGA data presents a notable increase in water loss, with XRD analysis suggesting the formation of gypsum in tablets containing elevated water content. The release study exhibited that the fastest release was from WE0-100, whereas the release rate decreases as the content of water increases. The WE0-100 releases more than 40 % drug within the first hour which is almost twice as high of the WE100-0 formulation. This DoD technology could distribute drugs onto the tablet's surface uniformly. The calcium sulfate would transform from hemihydrate to dihydrate form in the presence of water and therefore, those tablets treated with higher water content led to slower release. In conclusion, this study underscores the substantial impact of the water-ethanol ratio on drug release from binder jet printed tablets and highlights the potential of DoD technology for uniform drug distribution and controlled release.

3D printing of biologics-what has been accomplished to date?

Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.

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.

Development of a highly concentrated collagen ink for the creation of a 3D printed meniscus

The most prevalent extracellular matrix (ECM) protein in the meniscus is collagen, which controls cell activity and aids in preserving the biological and structural integrity of the ECM. To create stable and high-precision 3D printed collagen scaffolds, ink formulations must possess good printability and cytocompatibility. This study aims to overlap the limitation in the 3D printing of pure collagen, and to develop a highly concentrated collagen ink for meniscus fabrication. The extrusion test revealed that 12.5 % collagen ink had the best combination of high collagen concentration and printability. The ink was specifically designed to have load-bearing capacity upon printing and characterized with respect to rheological and extrusion properties. Following printing of structures with different infill, a series of post-processing steps, including salt stabilization, pH shifting, washing, freeze-drying, crosslinking and sterilization were performed, and optimised to maintain the stability of the engineered construct. Mechanical testing highlighted a storage modulus of 70 kPa for the lower porous structure while swelling properties showed swelling ratio between 9 and 11 after 15 min of soaking. Moreover, human avascular and vascular meniscus cells cultured on the scaffolds deposited a meniscus-like matrix containing collagen I, II and glycosaminoglycans after 28 days of culture. Finally, as proof-of-concept, human size 3D printed meniscus scaffold were created.

Organ bioprinting: progress, challenges and outlook

Bioprinting, as a groundbreaking technology, enables the fabrication of biomimetic tissues and organs with highly complex structures, multiple cell types, mechanical heterogeneity, and diverse functional gradients. With the growing demand for organ transplantation and the limited number of organ donors, bioprinting holds great promise for addressing the organ shortage by manufacturing completely functional organs. While the bioprinting of complete organs remains a distant goal, there has been considerable progress in the development of bioprinted transplantable tissues and organs for regenerative medicine. This review article recapitulates the current achievements of organ 3D bioprinting, primarily encompassing five important organs in the human body (i.e., the heart, kidneys, liver, pancreas, and lungs). Challenges from cellular techniques, biomanufacturing technologies, and organ maturation techniques are also deliberated for the broad application of organ bioprinting. In addition, the integration of bioprinting with other cutting-edge technologies including machine learning, organoids, and microfluidics is envisioned, which strives to offer the reader the prospect of bioprinting in constructing functional organs.

Self-Healing of Pluronic® F127 Hydrogels in the Presence of Various Polysaccharides

Thermoresponsive Pluronic® F127 (PL) gels in water were investigated through rheological tests in different shear conditions. The gel strength was tuned with the addition of 1% polysaccharide solution. In the presence of xanthan gum (XG), the viscoelastic behavior of PL-based hydrogels was improved in aqueous environment, but the rheological behavior was less changed with the addition of XG in PBS solutions, whereas in the presence of 0.1 M NaCl, the viscoelastic parameters decreased. PL micellar networks exhibited a self-healing ability, recovering their initial structure after applying cycles of high strain. The rheological characteristics of the PL hydrogel changed with the addition of 1% polysaccharides (xanthan gum, alginate, κ-carrageenan, gellan, or chitosan). PL/polysaccharide systems form temperature-responsive hydrogels with shear thinning behavior, yield stress, and self-healing ability, being considered a versatile platform for injectable biomaterials or bioinks. Thus, in the presence of xanthan gum in aqueous medium, the gel strength was improved after applying a high strain (the values of elastic modulus increased). The other investigated natural polymers induced specific self-healing behaviors. Good performances were observed with the addition of gellan gum, alginate, and κ-carrageenan, but for high values of strain, the ability to recover the initial structure decreased. A modest self-healing behavior was observed in the presence of chitosan and xanthan gum dissolved in NaCl solution.

The correlation between rheological properties and extrusion-based printability in bioink artifact quantification

Bioinks for cell-based bioprinting face availability limitations. Furthermore, the bioink development process needs comprehensive printability assessment methods and a thorough understanding of rheological factors' influence on printing outcomes. To bridge this gap, our study aimed to investigate the relationship between rheological properties and printing outcomes. We developed a specialized bioink artifact specifically designed to improve the quantification of printability assessment. This bioink artifact adhered to established criteria from extrusion-based bioprinting approaches. Seven hydrogel-based bioinks were selected and tested using the bioink artifact and rheological measurement. Rheological analysis revealed that the high-performing bioinks exhibited notable characteristics such as high storage modulus, low tan(δ), high shear-thinning capabilities, high yield stress, and fast, near-complete recovery abilities. Although rheological data alone cannot fully explain printing outcomes, certain metrics like storage modulus and tan(δ) correlated well (R2 > 0.9) with specific printing outcomes, such as gap-spanning capability and turn accuracy. This study provides a comprehensive examination of bioink shape fidelity across a wide range of bioinks, rheological measures, and printing outcomes. The results highlight the importance of considering the holistic view of bioink's rheological properties and directly measuring printing outcomes. These findings underscore the need to enhance bioink availability and establish standardized methods for assessing printability.

A dive into the bath: embedded 3D bioprinting of freeform in vitro models

Designing functional, vascularized, human scale in vitro models with biomimetic architectures and multiple cell types is a highly promising strategy for both a better understanding of natural tissue/organ development stages to inspire regenerative medicine, and to test novel therapeutics on personalized microphysiological systems. Extrusion-based 3D bioprinting is an effective biofabrication technology to engineer living constructs with predefined geometries and cell patterns. However, bioprinting high-resolution multilayered structures with mechanically weak hydrogel bioinks is challenging. The advent of embedded 3D bioprinting systems in recent years offered new avenues to explore this technology for in vitro modeling. By providing a stable, cell-friendly and perfusable environment to hold the bioink during and after printing, it allows to recapitulate native tissues' architecture and function in a well-controlled manner. Besides enabling freeform bioprinting of constructs with complex spatial organization, support baths can further provide functional housing systems for their long-term in vitro maintenance and screening. This minireview summarizes the recent advances in this field and discuss the enormous potential of embedded 3D bioprinting technologies as alternatives for the automated fabrication of more biomimetic in vitro models.

Bioprinting: a focus on improving bioink printability and cell performance based on different process parameters

Three dimensional (3D) bioprinting is an emerging biofabrication technique that shows great potential in the field of tissue engineering, regenerative medicine and advanced drug delivery. Despite the current advancement of bioprinting technology, it faces several obstacles such as the challenge of optimizing the printing resolution of 3D constructs while retaining cell viability before, during, and after bioprinting. Therefore, it is of great significance to fully understand factors that influence the shape fidelity of printed structures and the performance of cells encapsulated in bioinks. This review presents a comprehensive analysis of bioprinting process parameters that influence bioink printability and cell performance, including bioink properties (composition, concentration, and component ratio), printing speed and pressure, nozzle charateristics (size, length, and geometry), and crosslinking parameters (crosslinker types, concentration, and crosslinking time). Key examples are provided to analyze how these parameters could be tailored to achieve the optimal printing resolution as well as cell performance. Finally, future prospects of bioprinting technology, including correlating process parameters to particular cell types with predefined applications, applying statistical analysis and artificial intelligence (AI)/machine learning (ML) technique in parameter screening, and optimizing 4D bioprinting process parameters, are highlighted.

Rheology as a Tool for Fine-Tuning the Properties of Printable Bioinspired Gels

Over the last decade, efforts have been oriented toward the development of suitable gels for 3D printing, with controlled morphology and shear-thinning behavior in well-defined conditions. As a multidisciplinary approach to the fabrication of complex biomaterials, 3D bioprinting combines cells and biocompatible materials, which are subsequently printed in specific shapes to generate 3D structures for regenerative medicine or tissue engineering. A major interest is devoted to the printing of biomimetic materials with structural fidelity after their fabrication. Among some requirements imposed for bioinks, such as biocompatibility, nontoxicity, and the possibility to be sterilized, the nondamaging processability represents a critical issue for the stability and functioning of the 3D constructs. The major challenges in the field of printable gels are to mimic at different length scales the structures existing in nature and to reproduce the functions of the biological systems. Thus, a careful investigation of the rheological characteristics allows a fine-tuning of the material properties that are manufactured for targeted applications. The fluid-like or solid-like behavior of materials in conditions similar to those encountered in additive manufacturing can be monitored through the viscoelastic parameters determined in different shear conditions. The network strength, shear-thinning, yield point, and thixotropy govern bioprintability. An assessment of these rheological features provides significant insights for the design and characterization of printable gels. This review focuses on the rheological properties of printable bioinspired gels as a survey of cutting-edge research toward developing printed materials for additive manufacturing.

3D Printability of Silk/Hydroxyapatite Composites for Microprosthetic Applications

Micro-prosthetics requires the fabrication of mechanically robust and personalized components with sub-millimetric feature accuracy. Three-dimensional (3D) printing technologies have had a major impact on manufacturing such miniaturized devices for biomedical applications; however, biocompatibility requirements greatly constrain the choice of usable materials. Hydroxyapatite (HA) and its composites have been widely employed to fabricate bone-like structures, especially at the macroscale. In this work, we investigate the rheology, printability, and prosthetic mechanical properties of HA and HA-silk protein composites, focusing on the roles of composition and water content. We correlate key linear and nonlinear shear rheological parameters to geometric outcomes of printing and explain how silk compensates for the inherent brittleness of printed HA components. By increasing ink ductility, the inclusion of silk improves the quality of printed items through two mechanisms: (1) reducing underextrusion by lowering the required elastic modulus and, (2) reducing slumping by increasing the ink yield stress proportional to the modulus. We demonstrate that the elastic modulus and compressive strength of parts fabricated from silk-HA inks are higher than those for rheologically comparable pure-HA inks. We construct a printing map to guide the manufacturing of HA-based inks with excellent final properties, especially for use in biomedical applications for which sub-millimetric features are required.

Predicting and elucidating the post-printing behavior of 3D printed cancer cells in hydrogel structures by integrating in-vitro and in-silico experiments

A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, analyzing post-printing cellular dynamics and optimizing their functions within the 3D fabricated environment is only possible through trial and error and replicating several experiments. This issue motivated the development of a cellular automata model for the first time to simulate post-printing cell behaviour within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using MDA-MB-231 cell-laden hydrogel and evaluated cellular functions, including viability and proliferation in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. We demonstrated that in-silico model could predict and elucidate post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatine and alginate, without replicating several costly and time-consuming in-vitro measurements. We believe such a computational framework will substantially impact 3D bioprinting's future application. We hope this study inspires researchers to further realize how an in-silico model might be utilized to advance in-vitro 3D bioprinting research.

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.

Systematic review on the application of 3D-bioprinting technology in orthoregeneration: current achievements and open challenges

BACKGROUND

Joint degeneration and large or complex bone defects are a significant source of morbidity and diminished quality of life worldwide. There is an unmet need for a functional implant with near-native biomechanical properties. The potential for their generation using 3D bioprinting (3DBP)-based tissue engineering methods was assessed. We systematically reviewed the current state of 3DBP in orthoregeneration.

METHODS

This review was performed using PubMed and Web of Science. Primary research articles reporting 3DBP of cartilage, bone, vasculature, and their osteochondral and vascular bone composites were considered. Full text English articles were analyzed.

RESULTS

Over 1300 studies were retrieved, after removing duplicates, 1046 studies remained. After inclusion and exclusion criteria were applied, 114 articles were analyzed fully. Bioink material types and combinations were tallied. Cell types and testing methods were also analyzed. Nearly all papers determined the effect of 3DBP on cell survival. Bioink material physical characterization using gelation and rheology, and construct biomechanics were performed. In vitro testing methods assessed biochemistry, markers of extracellular matrix production and/or cell differentiation into respective lineages. In vivo proof-of-concept studies included full-thickness bone and joint defects as well as subcutaneous implantation in rodents followed by histological and µCT analyses to demonstrate implant growth and integration into surrounding native tissues.

CONCLUSIONS

Despite its relative infancy, 3DBP is making an impact in joint and bone engineering. Several groups have demonstrated preclinical efficacy of mechanically robust constructs which integrate into articular joint defects in small animals. However, notable obstacles remain. Notably, researchers encountered pitfalls in scaling up constructs and establishing implant function and viability in long term animal models. Further, to translate from the laboratory to the clinic, standardized quality control metrics such as construct stiffness and graft integration metrics should be established with investigator consensus. While there is much work to be done, 3DBP implants have great potential to treat degenerative joint diseases and provide benefit to patients globally.

Machine learning-enabled optimization of extrusion-based 3D printing

Machine learning (ML) and three-dimensional (3D) printing are among the fastest-growing branches of science. While ML can enable computers to independently learn from available data to make decisions with minimal human intervention, 3D printing has opened up an avenue for modern, multi-material, manufacture of complex 3D structures with a rapid turn-around ability for users with limited manufacturing experience. However, the determination of optimum printing parameters is still a challenge, increasing pre-printing process time and material wastage. Here, we present the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization. Unlike the widely held orthogonal design used in most of the 3D printing research, we, for the first time, used nine different computer-aided design (CAD) images and in order to enable ML algorithms to distinguish the difference between designs, we devised a self-designed method to calculate the "complexity index" of CAD designs. In addition, for the first time, the similarity of the print outcomes and CAD images are measured using four different self-designed labeling methods (both manually and automatically) to figure out the best labeling method for ML purposes. Subsequently, we trained eight ML algorithms on 224 datapoints to identify the best ML model for 3D printing applications. The "gradient boosting regression" model yields the best prediction performance with an R-2 score of 0.954. The ML-embedded GUI developed in this study enables users (either skilled or unskilled in 3D printing and/or ML) to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure to obtain the approximate similarity in the case of actual 3D printing of the uploaded design. This ultimately can prevent error-and-trial steps prior to printing which in return can speed up overall design-to-end-product time with less material waste and more cost-efficiency.

Mathematical and computational models in spheroid-based biofabrication

Ubiquitous in embryonic development, tissue fusion is of interest to tissue engineers who use tissue spheroids or organoids as building blocks of three-dimensional (3D) multicellular constructs. This review presents mathematical models and computer simulations of the fusion of tissue spheroids. The motivation of this study stems from the need to predict the post-printing evolution of 3D bioprinted constructs. First, we provide a brief overview of differential adhesion, the main morphogenetic mechanism involved in post-printing structure formation. It will be shown that clusters of cohesive cells behave as an incompressible viscous fluid on the time scale of hours. The discussion turns then to mathematical models based on the continuum hydrodynamics of highly viscous liquids and on statistical mechanics. Next, we analyze the validity and practical use of computational models of multicellular self-assembly in live constructs created by tissue spheroid bioprinting. Finally, we discuss the perspectives of the field as machine learning starts to reshape experimental design, and modular robotic workstations tend to alleviate the burden of repetitive tasks in biofabrication. STATEMENT OF SIGNIFICANCE: Bioprinted constructs are living systems, which evolve via morphogenetic mechanisms known from developmental biology. This review presents mathematical and computational tools devised for modeling post-printing structure formation. They help achieving a desirable outcome without expensive optimization experiments. While previous reviews mainly focused on assumptions, technical details, strengths, and limitations of computational models of multicellular self-assembly, this article discusses their validity and practical use in biofabrication. It also presents an overview of mathematical models that proved to be useful in the evaluation of experimental data on tissue spheroid fusion, and in the calibration of computational models. Finally, the perspectives of the field are discussed in the advent of robotic biofabrication platforms and bioprinting process optimization by machine learning.

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.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

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.

Computational Modeling and Experimental Characterization of Extrusion Printing into Suspension Baths

The extrusion printing of inks into suspension baths is an exciting tool, as it allows the printing of diverse and soft hydrogel inks into 3D space without the need for layer-by-layer fabrication. However, this printing process is complex and there have been limited studies to experimentally and computationally characterize the process. In this work, hydrogel inks (i.e., gelatin methacrylamide (GelMA)), suspension baths (i.e., agarose, Carbopol), and the printing process are examined via rheological, computational, and experimental analyses. Rheological data on various hydrogel inks and suspension baths is utilized to develop computational printing simulations based on Carreau constitutive viscosity models of the printing of inks within suspension baths. These results are then compared to experimental outcomes using custom print designs where features such as needle translation speed, defined in this work as print speed, are varied and printed filament resolution is quantified. Results are then used to identify print parameters for the printing of a GelMA ink into a unique guest-host hyaluronic acid suspension bath. This work emphasizes the importance of key rheological properties and print parameters for suspension bath printing and provides a computational model and experimental tools that can be used to inform the selection of print settings.

Contact-Free Remote Manipulation of Hydrogel Properties Using Light-Triggerable Nanoparticles: A Materials Science Perspective for Biomedical Applications

Considerable progress has been made in synthesizing "intelligent", biodegradable hydrogels that undergo rapid changes in physicochemical properties once exposed to external stimuli. These advantageous properties of stimulus-triggered materials make them highly appealing to diverse biomedical applications. Of late, research on the incorporation of light-triggered nanoparticles (NPs) into polymeric hydrogel networks has gained momentum due to their ability to remotely tune hydrogel properties using facile, contact-free approaches, such as adjustment of wavelength and intensity of light source. These multi-functional NPs, in combination with tissue-mimicking hydrogels, are increasingly being used for on-demand drug release, preparing diagnostic kits, and fabricating smart scaffolds. Here, the authors discuss the atomic behavior of different NPs in the presence of light, and critically review the mechanisms by which NPs convert light stimuli into heat energy. Then, they explain how these NPs impact the mechanical properties and rheological behavior of NPs-impregnated hydrogels. Understanding the rheological behavior of nanocomposite hydrogels using different sophisticated strategies, including computer-assisted machine learning, is critical for designing the next generation of drug delivery systems. Next, they highlight the salient strategies that have been used to apply light-induced nanocomposites for diverse biomedical applications and provide an outlook for the further improvement of these NPs-driven light-responsive hydrogels.

Optimized 3D Bioprinting Technology Based on Machine Learning: A Review of Recent Trends and Advances

The need for organ transplants has risen, but the number of available organ donations for transplants has stagnated worldwide. Regenerative medicine has been developed to make natural organs or tissue-like structures with biocompatible materials and solve the donor shortage problem. Using biomaterials and embedded cells, a bioprinter enables the fabrication of complex and functional three-dimensional (3D) structures of the organs or tissues for regenerative medicine. Moreover, conventional surgical 3D models are made of rigid plastic or rubbers, preventing surgeons from interacting with real organ or tissue-like models. Thus, finding suitable biomaterials and printing methods will accelerate the printing of sophisticated organ structures and the development of realistic models to refine surgical techniques and tools before the surgery. In addition, printing parameters (e.g., printing speed, dispensing pressure, and nozzle diameter) considered in the bioprinting process should be optimized. Therefore, machine learning (ML) technology can be a powerful tool to optimize the numerous bioprinting parameters. Overall, this review paper is focused on various ideas on the ML applications of 3D printing and bioprinting to optimize parameters and procedures.

Biomanufacturing in low Earth orbit for regenerative medicine

Research in low Earth orbit (LEO) has become more accessible. The 2020 Biomanufacturing in Space Symposium reviewed space-based regenerative medicine research and discussed leveraging LEO to advance biomanufacturing for regenerative medicine applications. The symposium identified areas where financial investments could stimulate advancements overcoming technical barriers. Opportunities in disease modeling, stem-cell-derived products, and biofabrication were highlighted. The symposium will initiate a roadmap to a sustainable market for regenerative medicine biomanufacturing in space. This perspective summarizes the 2020 Biomanufacturing in Space Symposium, highlights key biomanufacturing opportunities in LEO, and lays the framework for a roadmap to regenerative medicine biomanufacturing in space.

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.

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

Cancer still ranks as a leading cause of mortality worldwide. Although considerable efforts have been dedicated to anticancer therapeutics, progress is still slow, partially due to the absence of robust prediction models. Multicellular tumor spheroids, as a major three-dimensional (3D) culture model exhibiting features of avascular tumors, gained great popularity in pathophysiological studies and high throughput drug screening. However, limited control over cellular and structural organization is still the key challenge in achieving in vivo like tissue microenvironment. 3D bioprinting has made great strides toward tissue/organ mimicry, due to its outstanding spatial control through combining both cells and materials, scalability, and reproducibility. Prospectively, harnessing the power from both 3D bioprinting and multicellular spheroids would likely generate more faithful tumor models and advance our understanding on the mechanism of tumor progression. In this review, the emerging concept on using spheroids as a building block in 3D bioprinting for tumor modeling is illustrated. We begin by describing the context of the tumor microenvironment, followed by an introduction of various methodologies for tumor spheroid formation, with their specific merits and drawbacks. Thereafter, we present an overview of existing 3D printed tumor models using spheroids as a focus. We provide a compilation of the contemporary literature sources and summarize the overall advancements in technology and possibilities of using spheroids as building blocks in 3D printed tissue modeling, with a particular emphasis on tumor models. Future outlooks about the wonderous advancements of integrated 3D spheroidal printing conclude this review.

3D Printing of Microgel Scaffolds with Tunable Void Fraction to Promote Cell Infiltration

Granular, microgel-based materials have garnered interest as promising tissue engineering scaffolds due to their inherent porosity, which can promote cell infiltration. Adapting these materials for 3D bioprinting, while maintaining sufficient void space to enable cell migration, can be challenging, since the rheological properties that determine printability are strongly influenced by microgel packing and void fraction. In this work, a strategy is proposed to decouple printability and void fraction by blending UV-crosslinkable gelatin methacryloyl (GelMA) microgels with sacrificial gelatin microgels to form composite inks. It is observed that inks with an apparent viscosity greater than ≈100 Pa s (corresponding to microgel concentrations ≥5 wt%) have rheological properties that enable extrusion-based printing of multilayered structures in air. By altering the ratio of GelMA to sacrificial gelatin microgels, while holding total concentration constant at 6 wt%, a family of GelMA:gelatin microgel inks is created that allows for tuning of void fraction from 0.20 to 0.57. Furthermore, human umbilical vein endothelial cells (HUVEC) seeded onto printed constructs are observed to migrate into granular inks in a void fraction-dependent manner. Thus, the family of microgel inks holds promise for use in 3D printing and tissue engineering applications that rely upon cell infiltration.

Physical and Mechanical Characterization of Fibrin-Based Bioprinted Constructs Containing Drug-Releasing Microspheres for Neural Tissue Engineering Applications

Three-dimensional bioprinting can fabricate precisely controlled 3D tissue constructs. This process uses bioinks—specially tailored materials that support the survival of incorporated cells—to produce tissue constructs. The properties of bioinks, such as stiffness and porosity, should mimic those found in desired tissues to support specialized cell types. Previous studies by our group validated soft substrates for neuronal cultures using neural cells derived from human-induced pluripotent stem cells (hiPSCs). It is important to confirm that these bioprinted tissues possess mechanical properties similar to native neural tissues. Here, we assessed the physical and mechanical properties of bioprinted constructs generated from our novel microsphere containing bioink. We measured the elastic moduli of bioprinted constructs with and without microspheres using a modified Hertz model. The storage and loss modulus, viscosity, and shear rates were also measured. Physical properties such as microstructure, porosity, swelling, and biodegradability were also analyzed. Our results showed that the elastic modulus of constructs with microspheres was 1032 ± 59.7 Pascal (Pa), and without microspheres was 728 ± 47.6 Pa. Mechanical strength and printability were significantly enhanced with the addition of microspheres. Thus, incorporating microspheres provides mechanical reinforcement, which indicates their suitability for future applications in neural tissue engineering.

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.

Advances in 3D bioprinting technology for cardiac tissue engineering and regeneration

Cardiovascular disease is still one of the leading causes of death in the world, and heart transplantation is the current major treatment for end-stage cardiovascular diseases. However, because of the shortage of heart donors, new sources of cardiac regenerative medicine are greatly needed. The prominent development of tissue engineering using bioactive materials has creatively laid a direct promising foundation. Whereas, how to precisely pattern a cardiac structure with complete biological function still requires technological breakthroughs. Recently, the emerging three-dimensional (3D) bioprinting technology for tissue engineering has shown great advantages in generating micro-scale cardiac tissues, which has established its impressive potential as a novel foundation for cardiovascular regeneration. Whether 3D bioprinted hearts can replace traditional heart transplantation as a novel strategy for treating cardiovascular diseases in the future is a frontier issue. In this review article, we emphasize the current knowledge and future perspectives regarding available bioinks, bioprinting strategies and the latest outcome progress in cardiac 3D bioprinting to move this promising medical approach towards potential clinical implementation.

Bioscaffolds embedded with regulatory modules for cell growth and tissue formation: A review

The demand for artificial organs has greatly increased because of various aging-associated diseases and the wide need for organ transplants. A recent trend in tissue engineering is the precise reconstruction of tissues by the growth of cells adhering to bioscaffolds, which are three-dimensional (3D) structures that guide tissue and organ formation. Bioscaffolds used to fabricate bionic tissues should be able to not only guide cell growth but also regulate cell behaviors. Common regulation methods include biophysical and biochemical stimulations. Biophysical stimulation cues include matrix hardness, external stress and strain, surface topology, and electromagnetic field and concentration, whereas biochemical stimulation cues include growth factors, proteins, kinases, and magnetic nanoparticles. This review discusses bioink preparation, 3D bioprinting (including extrusion-based, inkjet, and ultraviolet-assisted 3D bioprinting), and regulation of cell behaviors. In particular, it provides an overview of state-of-the-art methods and devices for regulating cell growth and tissue formation and the effects of biophysical and biochemical stimulations on cell behaviors. In addition, the fabrication of bioscaffolds embedded with regulatory modules for biomimetic tissue preparation is explained. Finally, challenges in cell growth regulation and future research directions are presented.

Emergence of FRESH 3D printing as a platform for advanced tissue biofabrication

In tissue engineering, an unresolved challenge is how to build complex 3D scaffolds in order to recreate the structure and function of human tissues and organs. Additive manufacturing techniques, such as 3D bioprinting, have the potential to build biological material with unprecedented spatial control; however, printing soft biological materials in air often results in poor fidelity. Freeform Reversible Embedding of Suspended Hydrogels (FRESH) is an embedded printing approach that solves this problem by extruding bioinks within a yield-stress support bath that holds the bioinks in place until cured. In this Perspective, we discuss the challenges of 3D printing soft and liquid-like bioinks and the emergence for FRESH and related embedded printing techniques as a solution. This includes the development of FRESH and embedded 3D printing within the bioprinting field and the rapid growth in adoption, as well as the advantages of FRESH printing for biofabrication and the new research results this has enabled. Specific focus is on the customizability of the FRESH printing technique where the chemical composition of the yield-stress support bath and aqueous phase crosslinker can all be tailored for printing a wide range of bioinks in complex 3D structures. Finally, we look ahead at the future of FRESH printing, discussing both the challenges and the opportunities that we see as the biofabrication field develops.

The rheology of direct and suspended extrusion bioprinting

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.

3D printing of tissue engineering scaffolds: a focus on vascular regeneration

Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine. Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels. Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues. The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering. Recent advances in 3D printing have facilitated fabrication of vascular scaffolds, contributing to broad prospects for tissue vascularization. This review presents state of the art on modeling methods, print materials and preparation processes for fabrication of vascular scaffolds, and discusses the advantages and application fields of each method. Specially, significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized. Print materials and preparation processes are discussed in detail. And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting, electrospinning, and Lego-like construction. And related studies are exemplified. Transformation of vascular scaffolds to clinical application is discussed. Also, four trends of 3D printing of tissue engineering vascular scaffolds are presented, including machine learning, near-infrared photopolymerization, 4D printing, and combination of self-assembly and 3D printing-based methods.

Freeform 3D printing of vascularized tissues: Challenges and strategies

In recent years, freeform three-dimensional (3D) printing has led to significant advances in the fabrication of artificial tissues with vascularized structures. This technique utilizes a supporting matrix that holds the extruded printing ink and ensures shape maintenance of the printed 3D constructs within the prescribed spatial precision. Since the printing nozzle can be translated omnidirectionally within the supporting matrix, freeform 3D printing is potentially applicable for the fabrication of complex 3D objects, incorporating curved, and irregular shaped vascular networks. To optimize freeform 3D printing quality and performance, the rheological properties of the printing ink and supporting matrix, and the material matching between them are of paramount importance. In this review, we shall compare conventional 3D printing and freeform 3D printing technologies for the fabrication of vascular constructs, and critically discuss their working principles and their advantages and disadvantages. We also provide the detailed material information of emerging printing inks and supporting matrices in recent freeform 3D printing studies. The accompanying challenges are further discussed, aiming to guide freeform 3D printing by the effective design and selection of the most appropriate materials/processes for the development of full-scale functional vascularized artificial tissues.

Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

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.

Guidelines for establishing a 3-D printing biofabrication laboratory

Advanced manufacturing and 3D printing are transformative technologies currently undergoing rapid adoption in healthcare, a traditionally non-manufacturing sector. Recent development in this field, largely enabled by merging different disciplines, has led to important clinical applications from anatomical models to regenerative bioscaffolding and devices. Although much research to-date has focussed on materials, designs, processes, and products, little attention has been given to the design and requirements of facilities for enabling clinically relevant biofabrication solutions. These facilities are critical to overcoming the major hurdles to clinical translation, including solving important issues such as reproducibility, quality control, regulations, and commercialization. To improve process uniformity and ensure consistent development and production, large-scale manufacturing of engineered tissues and organs will require standardized facilities, equipment, qualification processes, automation, and information systems. This review presents current and forward-thinking guidelines to help design biofabrication laboratories engaged in engineering model and tissue constructs for therapeutic and non-therapeutic applications.