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

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.

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

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

The main tasks of a semiotics of artificial intelligence

The article indicates the essential tasks of a semiotics of artificial intelligence: studying the way it simulates the expression of intelligence; the way it produces content that is creatively endowed; the ideological assumptions of artificial intelligence within the culture that produces it. Artificial intelligence is, from a semiotic point of view, the predominant technology of fakery in the current era. On the strength of its studies on the false, semiotics can therefore also be applied to the analysis of the fake that, in increasingly sophisticated forms, is produced through artificial intelligence and through the deep learning of neural networks. The article focuses on the adversarial ones, trying to highlight their ideological assumptions and cultural developments, which seem to indicate the entry of human societies and cultures into the 'realm of the absolute fake'.

Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

Purpose

Bones have a complex hierarchical structure that supports their diverse chemical, biological, and mechanical functions. High rates of bone susceptibility to fractures and injury have attracted extensive research interest to find alternate biomaterials for bone scaffolds. Natural bone healing is only successful if the defect is very small and when a defect exceeds 1 cm3 then bone grafting is required. Large bone defects or injuries are very serious problems in orthopedics as they bring great harm to health and normal function of daily life routine. A scaffold should have good strength to maintain its own structure after implantation in a load bearing environment and without being stiff that shields surrounding bone from the load. Therefore, mechanical properties of bone scaffolds should match those of the host tissue and should be part of the natural environment of the body without any harm or further damage.

Methods

In this paper, we present two main contributions. First, we investigate the use of machine learning models in identifying biomaterials that are suitable for bone scaffolds. Second, we rank the best materials for biomedical scaffold applications using the multi-criteria decision analysis methods, the Preference Ranking Organization METhod for the Enrichment of Evaluations (PROMETHEE). Machine learning models investigated are AdaBoost, artificial neural network (ANN), Naïve Bayes (NB), Decision tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Mechanical properties such as comprehensive strength, tensile strength, and Young’s modulus with the cortical bone are used as the standard reference for classification.

Results

The results show that the ANN outperforms the other machine learning models in identifying the biomaterials suitable for bone tissue engineering, while the ranking results using PROMETHEE show that Brushite and Titanium alloy are the best appropriate biomaterials for the cancellous and cortical bones, respectively.

Conclusion

Brushite and Titanium alloy are the best biomaterials for the cancellous and cortical bones, respectively.

Machine Learning in Tissue Engineering

Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.

State-of-the-art techniques for imaging the vascular microenvironment in craniofacial bone tissue engineering applications

Vascularization is a crucial step during musculoskeletal tissue regeneration via bioengineered constructs or grafts. Functional vasculature provides oxygen and nutrients to the graft microenvironment, facilitates wound healing, enhances graft integration with host tissue, and ensures the long-term survival of regenerating tissue. Therefore, imaging de novo vascularization (i.e., angiogenesis), changes in microvascular morphology, and the establishment and maintenance of perfusion within the graft site (i.e., vascular microenvironment or VME) can provide essential insights into engraftment, wound healing, as well as inform the design of tissue engineering (TE) constructs. In this review, we focus on state-of-the-art imaging approaches for monitoring the VME in craniofacial TE applications, as well as future advances in this field. We describe how cutting-edge in vivo and ex vivo imaging methods can yield invaluable information regarding VME parameters that can help characterize the effectiveness of different TE constructs and iteratively inform their design for enhanced craniofacial bone regeneration. Finally, we explicate how the integration of novel TE constructs, preclinical model systems, imaging techniques, and systems biology approaches could usher in an era of "image-based tissue engineering."

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.

Vascularization in Bioartificial Parenchymal Tissue: Bioink and Bioprinting Strategies

Among advanced therapy medicinal products, tissue-engineered products have the potential to address the current critical shortage of donor organs and provide future alternative options in organ replacement therapy. The clinically available tissue-engineered products comprise bradytrophic tissue such as skin, cornea, and cartilage. A sufficient macro- and microvascular network to support the viability and function of effector cells has been identified as one of the main challenges in developing bioartificial parenchymal tissue. Three-dimensional bioprinting is an emerging technology that might overcome this challenge by precise spatial bioink deposition for the generation of a predefined architecture. Bioinks are printing substrates that may contain cells, matrix compounds, and signaling molecules within support materials such as hydrogels. Bioinks can provide cues to promote vascularization, including proangiogenic signaling molecules and cocultured cells. Both of these strategies are reported to enhance vascularization. We review pre-, intra-, and postprinting strategies such as bioink composition, bioprinting platforms, and material deposition strategies for building vascularized tissue. In addition, bioconvergence approaches such as computer simulation and artificial intelligence can support current experimental designs. Imaging-derived vascular trees can serve as blueprints. While acknowledging that a lack of structured evidence inhibits further meta-analysis, this review discusses an end-to-end process for the fabrication of vascularized, parenchymal tissue.

Designer DNA biomolecules as a defined biomaterial for 3D bioprinting applications

DNA has excellent features such as the presence of functional and targeted molecular recognition motifs, tailorability, multifunctionality, high-precision molecular self-assembly, hydrophilicity, and outstanding biocompatibility. Due to these remarkable features, DNA has emerged as a leading next-generation biomaterial of choice to make hydrogels by self-assembly. In recent times, novel routes for the chemical synthesis of DNA, advances in tailorable designs, and affordable production ways have made DNA as a building block material for various applications. These advanced features have made researchers continuously explore the interesting properties of pure and hybrid DNA for 3D bioprinting and other biomedical applications. This review article highlights the topical advancements in the use of DNA as an ideal bioink for the bioprinting of cell-laden three-dimensional tissue constructs for regenerative medicine applications. Various bioprinting techniques and emerging design approaches such as self-assembly, nucleotide sequence, enzymes, and production cost to use DNA as a bioink for bioprinting applications are described. In addition, various types and properties of DNA hydrogels such as stimuli responsiveness and mechanical properties are discussed. Further, recent progress in the applications of DNA in 3D bioprinting are emphasized. Finally, the current challenges and future perspectives of DNA hydrogels in 3D bioprinting and other biomedical applications are discussed.

Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels

Research in bioprinting is booming due to its potential in addressing several manufacturing challenges in regenerative medicine. However, there are still many hurdles to overcome to guarantee cell survival and good printability. For the 3D extrusion-based bioprinting, cell viability is amongst one of the lowest of all the bioprinting techniques and is strongly influenced by various factors including the shear stress in the print nozzle. The goal of this study is to quantify, by means of in silico modeling, the mechanical environment experienced by the bioink during the printing process. Two ubiquitous nozzle shapes, conical and blunted, were considered, as well as three common hydrogels with material properties spanning from almost Newtonian to highly shear-thinning materials following the power-law behavior: Alginate-Gelatin, Alginate and PF127. Comprehensive in silico testing of all combinations of nozzle geometry variations and hydrogels was achieved by combining a design of experiments approach (DoE) with a computational fluid dynamics (CFD) of the printing process, analyzed through a machine learning approach named Gaussian Process. Available experimental results were used to validate the CFD model and justify the use of shear stress as a surrogate for cell survival in this study. The lower and middle nozzle radius, lower nozzle length and the material properties, alone and combined, were identified as the major influencing factors affecting shear stress, and therefore cell viability, during printing. These results were successfully compared with those of reported experiments testing viability for different nozzle geometry parameters under constant flow rate or constant pressure. The in silico 3D bioprinting platform developed in this study offers the potential to assist and accelerate further development of 3D bioprinting.

The future of bone regeneration: integrating AI into tissue engineering

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Ethanol treatment of nanoPGA/PCL composite scaffolds enhances human chondrocyte development in the cellular microenvironment of tissue-engineered auricle constructs

A major obstacle for tissue engineering ear-shaped cartilage is poorly developed tissue comprising cell-scaffold constructs. To address this issue, bioresorbable scaffolds of poly-ε-caprolactone (PCL) and polyglycolic acid nanofibers (nanoPGA) were evaluated using an ethanol treatment step before auricular chondrocyte scaffold seeding, an approach considered to enhance scaffold hydrophilicity and cartilage regeneration. Auricular chondrocytes were isolated from canine ears and human surgical samples discarded during otoplasty, including microtia reconstruction. Canine chondrocytes were seeded onto PCL and nanoPGA sheets either with or without ethanol treatment to examine cellular adhesion in vitro. Human chondrocytes were seeded onto three-dimensional bioresorbable composite scaffolds (PCL with surface coverage of nanoPGA) either with or without ethanol treatment and then implanted into athymic mice for 10 and 20 weeks. On construct retrieval, scanning electron microscopy showed canine auricular chondrocytes seeded onto ethanol-treated scaffolds in vitro developed extended cell processes contacting scaffold surfaces, a result suggesting cell-scaffold adhesion and a favorable microenvironment compared to the same cells with limited processes over untreated scaffolds. Adhesion of canine chondrocytes was statistically significantly greater (p ≤ 0.05) for ethanol-treated compared to untreated scaffold sheets. After implantation for 10 weeks, constructs of human auricular chondrocytes seeded onto ethanol-treated scaffolds were covered with glossy cartilage while constructs consisting of the same cells seeded onto untreated scaffolds revealed sparse connective tissue and cartilage regeneration. Following 10 weeks of implantation, RT-qPCR analyses of chondrocytes grown on ethanol-treated scaffolds showed greater expression levels for several cartilage-related genes compared to cells developed on untreated scaffolds with statistically significantly increased SRY-box transcription factor 5 (SOX5) and decreased interleukin-1α (inflammation-related) expression levels (p ≤ 0.05). Ethanol treatment of scaffolds led to increased cartilage production for 20- compared to 10-week constructs. While hydrophilicity of scaffolds was not assessed directly in the present findings, a possible factor supporting the summary data is that hydrophilicity may be enhanced for ethanol-treated nanoPGA/PCL scaffolds, an effect leading to improvement of chondrocyte adhesion, the cellular microenvironment and cartilage regeneration in tissue-engineered auricle constructs.

Supervised machine learning for automated classification of human Wharton's Jelly cells and mechanosensory hair cells

Tissue engineering and gene therapy strategies offer new ways to repair permanent damage to mechanosensory hair cells (MHCs) by differentiating human Wharton's Jelly cells (HWJCs). Conventionally, these strategies require the classification of each cell as differentiated or undifferentiated. Automated classification tools, however, may serve as a novel method to rapidly classify these cells. In this paper, images from previous work, where HWJCs were differentiated into MHC-like cells, were examined. Various cell features were extracted from these images, and those which were pertinent to classification were identified. Different machine learning models were then developed, some using all extracted data and some using only certain features. To evaluate model performance, the area under the curve (AUC) of the receiver operating characteristic curve was primarily used. This paper found that limiting algorithms to certain features consistently improved performance. The top performing model, a voting classifier model consisting of two logistic regressions, a support vector machine, and a random forest classifier, obtained an AUC of 0.9638. Ultimately, this paper illustrates the viability of a novel machine learning pipeline to automate the classification of undifferentiated and differentiated cells. In the future, this research could aid in automated strategies that determine the viability of MHC-like cells after differentiation.

Digitally Fabricated and Naturally Augmented In Vitro Tissues

Human in vitro tissues are extracorporeal 3D cultures of human cells embedded in biomaterials, commonly hydrogels, which recapitulate the heterogeneous, multiscale, and architectural environment of the human body. Contemporary strategies used in 3D tissue and organ engineering integrate the use of automated digital manufacturing methods, such as 3D printing, bioprinting, and biofabrication. Human tissues and organs, and their intra- and interphysiological interplay, are particularly intricate. For this reason, attentiveness is rising to intersect materials science, medicine, and biology with arts and informatics. This report presents advances in computational modeling of bioink polymerization and its compatibility with bioprinting, the use of digital design and fabrication in the development of fluidic culture devices, and the employment of generative algorithms for modeling the natural and biological augmentation of in vitro tissues. As a future direction, the use of serially linked in vitro tissues as human body-mimicking systems and their application in drug pharmacokinetics and metabolism, disease modeling, and diagnostics are discussed.