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

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

Micro-fragmented collagen hydrogel wound dressing: Enhanced porosity facilitates elevated stem cell survival and paracrine effects for accelerated wound maturation

Human Adipose-derived stem cells (hADSCs), known for their mesenchymal stem cell properties, including multilineage differentiation and self-renewal, hold significant promise for chronic wound regeneration. Typically, hADSCs are utilized in cellular aggregates or hydrogels to enhance therapeutic efficacy. However, limitations such as reduced cell viability, inadequate mass transfer rates, and diminished paracrine effects hinder their clinical applications. This study explores an innovative approach by encapsulating hADSCs within a collagen/hyaluronic acid micro-fragmented collagen hydrogel wound dressing (MCWD). The resulting micro-fragmented collagen hydrogel-hADSC composite created through the integration of micro-sized hydrogel units and cells demonstrated markedly improved cell viability and activity, as well as superior therapeutic outcomes compared to conventional cell aggregates (CA) and collagen hydrogel wound dressings (CWD). In vitro assessments showed that the highly porous structure of MCWD promotes better mass transfer and enhances the viability and cytokine production of hADSCs associated with the paracrine effect. In vivo experiments further validated the effectiveness of the MCWD, revealing significant enhancements in cell proliferation, skin thickness restoration, collagen maturation, and blood vessel formation. These findings underscore the potential of MCWD as an advanced solution for wound healing applications.

3D Bioprinting and Artificial Intelligence-Assisted Biofabrication of Personalized Oral Soft Tissue Constructs

Regeneration of oral soft tissue defects, including mucogingival defects associated with the recession or loss of gingival and/or mucosal tissues around teeth and implants, is crucial for restoring oral tissue form, function, and health. This study presents a novel approach using three-dimensional (3D) bioprinting to fabricate individualized grafts with precise size, shape, and layer-by-layer cellular organization. A multicomponent polysaccharide/fibrinogen-based bioink is developed, and bioprinting parameters are optimized to create shape-controlled oral soft tissue (gingival) constructs. Rheological, printability, and shape-fidelity assays, demonstrated the influence of thickener concentration and print parameters on print resolution and shape fidelity. Artificial intelligence (AI)-derived tool enabled streamline the iterative bioprinting parameter optimization and analysis of the interaction between the bioprinting parameters. The cell-laden polysaccharide/fibrinogen-based bioinks exhibited excellent cellular viability and shape fidelity of shape-controlled, full-thickness gingival tissue constructs over the 18-day culture period. While variations in thickener concentrations within the bioink minimally impact the cellular organization and morphogenesis (gingival epithelial, connective tissue, and basement membrane markers), they influence the shape fidelity of the bioprinted constructs. This study represents a significant step toward the biofabrication of personalized soft tissue grafts, offering potential applications in the repair and regeneration of mucogingival defects associated with periodontal disease and dental implants.

Machine Learning-Enhanced Optimization for High-Throughput Precision in Cellular Droplet Bioprinting

Organoids produce through traditional manual pipetting methods face challenges such as labor-intensive procedures and batch-to-batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple printing parameters to achieve the desired organoid size remains a time-consuming and costly endeavor. To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. In this study, a high-throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. Finally, these top-performing machine learning models are integrated into a user-friendly interface to streamline usability. The bioprinting parameter optimization platform develops in this study is expected to create significant synergy when combined with various bioprinting technologies, advancing the scalable production of organoids for a range of applications.

Predicting rheological properties of HAMA/GelMA hybrid hydrogels via machine learning

- Rheological properties are pivotal in determining the printability of biomaterials, directly impacting the success of 3D bioprinted constructs. Understanding the intricate relationship between biomaterial formulations, rheological behavior and printability can facilitate the advancement and rapid development of biomaterials. Herein, we critically measured the rheological properties of hyaluronic acid methacrylate (HAMA)/gelatin methacrylate (GelMA) hybrid hydrogels with varied formulations and generated a dataset to train a machine learning (ML) model. By utilizing four well-known algorithms, we developed the ML model for the viscosity and shear stress of HAMA/GelMA hydrogel mixtures. To improve model interpretability, we further created a multilayer perceptron framed model, known as HydroThermoMLP, by incorporating the Redlich-Kister polynomial as the thermodynamic representation of viscosity of mixtures. To accomplish the MLP learning on limited data, the shared loss function was formulated on the basis of the R-K presentation to guide the joint training process. The established HydroThermoMLP model, while maintaining the same accuracy as Random Forest, produces outputs that adhere to thermodynamic constraints and instill confidence in generalization applications with a simple algorithm informed by the R-K polynomial. It presents a robust predictive ML tool to forecast the viscosity of hybrid hydrogels and direct the design of biomaterials while appropriately abiding by thermodynamic constraints as essential guidelines.

Investigation of the Effects of 3D Printing Parameters on the Mechanical Properties of Bone Scaffolds: Experimental Study Integrated with Artificial Neural Networks

Scaffolds are critical in regenerative medicine, particularly in bone tissue engineering, where they mimic the extracellular matrix to support tissue regeneration. Scaffold efficacy depends on precise control of 3D printing parameters, which determine geometric and mechanical properties, including Young's modulus. This study examines the impact of nozzle temperature, printing speed, and feed rate on the Young's modulus of polylactic acid (PLA) scaffolds. Using a Prusa MINI+ 3D printer (Prusa Research a.s., Prague, Czech Republic), systematic experiments are conducted to explore these correlations. Results show that higher nozzle temperatures decrease Young's modulus due to reduced viscosity and weaker interlayer bonding, likely caused by thermal degradation and reduced crystallinity. Printing speed exhibits an optimal range, with Young's modulus peaking at moderate speeds (around 2100 mm/min), suggesting a balance that enhances crystallinity and bonding. Material feed rate positively correlates with Young's modulus, with increased material deposition improving scaffold density and strength. The integration of an Artificial Neural Network (ANN) model further optimized the printing parameters, successfully predicting the maximum Young's modulus while maintaining geometric constraints. Notably, the Young's modulus achieved falls within the typical range for cancellous bone, indicating the model's potential to meet specific clinical requirements. These findings offer valuable insights for designing patient-specific bone scaffolds, potentially improving clinical outcomes in bone repair.

Extrusion bioprinting: meeting the promise of human tissue biofabrication?

Extrusion is the most popular bioprinting platform. Predictions of human tissue and whole-organ printing have been made for the technology. However, after decades of development, extruded constructs lack the essential microscale resolution and heterogeneity observed in most human tissues. Extrusion bioprinting has had little clinical impact with the majority of research directed away from the tissues most needed by patients. The distance between promise and reality is a result of technology hype and inherent design flaws that limit the shape, scale and survival of extruded features. By more widely adopting resolution innovations and softening its ambitions the biofabrication field could define a future for extrusion bioprinting that more closely aligns with its capabilities.

3D-Printed Hydrogels from Natural Polymers for Biomedical Applications: Conventional Fabrication Methods, Current Developments, Advantages, and Challenges

Hydrogels are network polymers with high water-bearing capacity resembling the extracellular matrix. Recently, many studies have focused on synthesizing hydrogels from natural sources as they are biocompatible, biodegradable, and readily available. However, the structural complexities of biological tissues and organs limit the use of hydrogels fabricated with conventional methods. Since 3D printing can overcome this barrier, more interest has been drawn toward the 3D printing of hydrogels. This review discusses the structure of hydrogels and their potential biomedical applications with more emphasis on natural hydrogels. There is a discussion on various formulations of alginates, chitosan, gelatin, and hyaluronic acid. Furthermore, we discussed the 3D printing techniques available for hydrogels and their advantages and limitations.

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Droplet-based 3D bioprinting for drug delivery and screening

Recently, the conventional criterion of "one-size-fits-all" is not qualified for each individual patient, requiring precision medicine for enhanced therapeutic effects. Besides, drug screening is a high-cost and time-consuming process which requires innovative approaches to facilitate drug development rate. Benefiting from consistent technical advances in 3D bioprinting techniques, droplet-based 3D bioprinting techniques have been broadly utilized in pharmaceutics due to the noncontact printing mechanism and precise control on the deposition position of droplets. More specifically, cell-free/cell-laden bioinks which are deposited for the fabrication of drug carriers/3D tissue constructs have been broadly utilized for precise drug delivery and high throughput drug screening, respectively. This review summarizes the mechanism of various droplet-based 3D bioprinting techniques and the most up-to-date applications in drug delivery and screening and discusses the potential improvements of droplet-based 3D bioprinting techniques from both technical and material aspects. Through technical innovations, materials development, and the assistance from artificial intelligence, the formation process of drug carriers will be more stable and accurately controlled guaranteeing precise drug delivery. Meanwhile, the shape fidelity and uniformity of the printed tissue models will be significantly improved ensuring drug screening efficiency and efficacy.

Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting

The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2-5.25%), gelatin (2-5.25%), and TEMPO-Nano fibrillated cellulose (0.5-1%) at shear rates from 0.1 to 100 s-1. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering.

Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting

Spheroids serve as the building blocks for three-dimensional (3D) bioprinted tissue patches. When larger than 500 μm, the desired size for 3D bioprinting, they tend to have a hypoxic core with necrotic cells. Therefore, it is critical to assess the viability of spheroids in order to ensure the successful fabrication of high-viability patches. However, current viability assays are time-consuming, labor-intensive, require specialized training, or are subject to human bias. In this study, we build a convolutional neural network (CNN) model to efficiently and accurately predict spheroid viability, using a phase-contrast image of a spheroid as its input. A comprehensive dataset of mouse mesenchymal stem cell (mMSC) spheroids of varying sizes with corresponding viability percentages, which was obtained through CCK-8 assays, was established and used to train and validate the model. The model was trained to automatically classify spheroids into one of four distinct categories based on their predicted viability: 0-20%, 20-40%, 40-70%, and 70-100%. The model achieved an average accuracy of 92%, with a consistent loss below 0.2. This deep-learning model offers a non-invasive, efficient, and accurate method to streamline the assessment of spheroid quality, thereby accelerating the development of bioengineered cardiac tissue patches for cardiovascular disease therapies.

3D-Printed Polymeric Biomaterials for Health Applications

3D printing, also known as additive manufacturing, holds immense potential for rapid prototyping and customized production of functional health-related devices. With advancements in polymer chemistry and biomedical engineering, polymeric biomaterials have become integral to 3D-printed biomedical applications. However, there still exists a bottleneck in the compatibility of polymeric biomaterials with different 3D printing methods, as well as intrinsic challenges such as limited printing resolution and rates. Therefore, this review aims to introduce the current state-of-the-art in 3D-printed functional polymeric health-related devices. It begins with an overview of the landscape of 3D printing techniques, followed by an examination of commonly used polymeric biomaterials. Subsequently, examples of 3D-printed biomedical devices are provided and classified into categories such as biosensors, bioactuators, soft robotics, energy storage systems, self-powered devices, and data science in bioplotting. The emphasis is on exploring the current capabilities of 3D printing in manufacturing polymeric biomaterials into desired geometries that facilitate device functionality and studying the reasons for material choice. Finally, an outlook with challenges and possible improvements in the near future is presented, projecting the contribution of general 3D printing and polymeric biomaterials in the field of healthcare.

Engineering considerations in the design of tissue specific bioink for 3D bioprinting applications

Over eight million surgical procedures are conducted annually in the United Stats to address organ failure or tissue losses. In response to this pressing need, recent medical advancements have significantly improved patient outcomes, primarily through innovative reconstructive surgeries utilizing tissue grafting techniques. Despite tremendous efforts, repairing damaged tissues remains a major clinical challenge for bioengineers and clinicians. 3D bioprinting is an additive manufacturing technique that holds significant promise for creating intricately detailed constructs of tissues, thereby bridging the gap between engineered and actual tissue constructs. In contrast to non-biological printing, 3D bioprinting introduces added intricacies, including considerations for material selection, cell types, growth, and differentiation factors. However, technical challenges arise, particularly concerning the delicate nature of living cells in bioink for tissue construction and limited knowledge about the cell fate processes in such a complex biomechanical environment. A bioink must have appropriate viscoelastic and rheological properties to mimic the native tissue microenvironment and attain desired biomechanical properties. Hence, the properties of bioink play a vital role in the success of 3D bioprinted substitutes. This review comprehensively delves into the scientific aspects of tissue-centric or tissue-specific bioinks and sheds light on the current challenges of the translation of bioinks and bioprinting.

Harnessing the power of machine learning into tissue engineering: current progress and future prospects

Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.

3D Bioprinting for Engineered Tissue Constructs and Patient-Specific Models: Current Progress and Prospects in Clinical Applications

Advancements in bioprinting technology are driving the creation of complex, functional tissue constructs for use in tissue engineering and regenerative medicine. Various methods, including extrusion, jetting, and light-based bioprinting, have their unique advantages and drawbacks. Over the years, researchers and industry leaders have made significant progress in enhancing bioprinting techniques and materials, resulting in the production of increasingly sophisticated tissue constructs. Despite this progress, challenges still need to be addressed in achieving clinically relevant, human-scale tissue constructs, presenting a hurdle to widespread clinical translation. However, with ongoing interdisciplinary research and collaboration, the field is rapidly evolving and holds promise for personalized medical interventions. Continued development and refinement of bioprinting technologies have the potential to address complex medical needs, enabling the development of functional, transplantable tissues and organs, as well as advanced in vitro tissue models.

Leveraging printability and biocompatibility in materials for printing implantable vessel scaffolds

Vessel scaffolds are crucial for treating cardiovascular diseases (CVDs). It is currently feasible to fabricate vessel scaffolds from a variety of materials using traditional fabrication methods, but the risks of thrombus formation, chronic inflammation, and atherosclerosis associated with these scaffolds have led to significant limitations in the clinical usages. Bioprinting, as an emerging technology, has great potential in constructing implantable vessel scaffolds. During the fabrication of the constructs, the biomaterials used for bioprinting have offered significant contributions for the successful fabrications of the vessel scaffolds. Herein, we review recent advances in biomaterials for bioprinting implantable vessel scaffolds. First, we briefly introduce the requirements for implantable vessel scaffolds and its conventional manufacturing methods. Next, a brief overview of the classic methods for bioprinting vessel scaffolds is presented. Subsequently, we provide an in-depth analysis of the properties of the representative natural, synthetic, composite and hybrid biomaterials that can be used for bioprinting implantable vessel scaffolds. Ultimately, we underscore the necessity of leveraging biocompatibility and printability for biomaterials, and explore the unmet needs and potential applications of these biomaterials in the field of bioprinted implantable vessel scaffolds.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

Recent advances in the 3D skin bioprinting for regenerative medicine: Cells, biomaterials, and methods

The skin is a tissue constantly exposed to the risk of damage, such as cuts, burns, and genetic disorders. The standard treatment is autograft, but it can cause pain to the patient being extremely complex in patients suffering from burns on large body surfaces. Considering that there is a need to develop technologies for the repair of skin tissue like 3D bioprinting. Skin is a tissue that is approximately 1/16 of the total body weight and has three main layers: epidermis, dermis, and hypodermis. Therefore, there are several studies using cells, biomaterials, and bioprinting for skin regeneration. Here, we provide an overview of the structure and function of the epidermis, dermis, and hypodermis, and showed in the recent research in skin regeneration, the main cells used, biomaterials studied that provide initial support for these cells, allowing the growth and formation of the neotissue and general characteristics, advantages and disadvantages of each methodology and the landmarks in recent research in the 3D skin bioprinting.

Constraint based Bayesian optimization of bioink precursor: a machine learning framework

Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, shape fidelity and biocompatibility. Predicting bioink properties can be beneficial to the research community but is a challenging task due to the non-Newtonian behavior in complex composition. Existing models such as Cross model become inadequate for predicting the viscosity for heterogeneous composition of bioinks. In this paper, we utilize a machine learning framework to accurately predict the viscosity of heterogeneous bioink compositions, aiming to enhance extrusion-based bioprinting techniques. Utilizing Bayesian optimization (BO), our strategy leverages a limited dataset to inform our model. This is a technique especially useful of the typically sparse data in this domain. Moreover, we have also developed a mask technique that can handle complex constraints, informed by domain expertise, to define the feasible parameter space for the components of the bioink and their interactions. Our proposed method is focused on predicting the intrinsic factor (e.g. viscosity) of the bioink precursor which is tied to the extrinsic property (e.g. cell viability) through the mask function. Through the optimization of the hyperparameter, we strike a balance between exploration of new possibilities and exploitation of known data, a balance crucial for refining our acquisition function. This function then guides the selection of subsequent sampling points within the defined viable space and the process continues until convergence is achieved, indicating that the model has sufficiently explored the parameter space and identified the optimal or near-optimal solutions. Employing this AI-guided BO framework, we have developed, tested, and validated a surrogate model for determining the viscosity of heterogeneous bioink compositions. This data-driven approach significantly reduces the experimental workload required to identify bioink compositions conducive to functional tissue growth. It not only streamlines the process of finding the optimal bioink compositions from a vast array of heterogeneous options but also offers a promising avenue for accelerating advancements in tissue engineering by minimizing the need for extensive experimental trials.

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.

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.

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.

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.

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.

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.