Machine Learning-Driven Biomaterials Evolution

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

BACKGROUND

Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.

METHODS

The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.

RESULTS

The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.

CONCLUSION

The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.

HIGHLIGHTS

The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots

Introduction

The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.

Methods

Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.

Results

The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ε-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.

Discussion

Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.

The Application of artificial intelligence in restorative Dentistry: A narrative review of current research

This review explores the transformative impact of artificial intelligence (AI) on restorative dentistry. By discussing the diagnostic processes, treatment planning, image analysis, prosthodontics, and material/biomaterial research, this study highlights the role of AI in optimizing precision and efficiency. It emphasizes personalized material selection, accelerated biomaterial research, and AI-enabled clinical workflows for enhanced patient outcomes. The review concludes with insights into the challenges, ethical considerations, and future trends, emphasizing the collaborative efforts needed for continued innovation in AI-driven restorative dentistry.

Surface Topography Steer Soft Tissue Response and Antibacterial Function at the Transmucosal Region of Titanium Implant

Metallic dental implants have been extensively used in clinical practice due to their superior mechanical properties, biocompatibility, and aesthetic outcomes. However, their integration with the surrounding soft tissue at the mucosal region remains challenging and can cause implant failure due to the peri-implant immune microenvironment. The soft tissue integration of dental implants can be ameliorated through different surface modifications. This review discussed and summarized the current knowledge of topography-mediated immune response and topography-mediated antibacterial activity in Ti dental implants which enhance soft tissue integration and their clinical performance. For example, nanopillar-like topographies such as spinules, and spikes showed effective antibacterial activity in human salivary biofilm which was due to the lethal stretching of bacterial membrane between the nanopillars. The key findings of this review were (I) cross-talk between surface nanotopography and soft tissue integration in which the surface nanotopography can guide the perpendicular orientation of collagen fibers into connective tissue which leads to the stability of soft tissue, (II) nanotubular array could shift the macrophage phenotype from pro-inflammatory (M1) to anti-inflammatory (M2) and manipulate the balance of osteogenesis/osteoclasia, and (III) surface nanotopography can provide specific sites for the loading of antibacterial agents and metallic nanoparticles of clinical interest functionalizing the implant surface. Silver-containing nanotubular topography significantly decreased the formation of fibrous encapsulation in per-implant soft tissue and showed synergistic antifungal and antibacterial properties. Although the Ti implants with surface nanotopography have shown promising in targeting soft tissue healing in vitro and in vivo through their immunomodulatory and antibacterial properties, however, long-term in vivo studies need to be conducted particularly in osteoporotic, and diabetic patients to ensure their desired performance with immunomodulatory and antibacterial properties. The optimization of product development is another challenging issue for its clinical translation, as the dental implant with surface nanotopography must endure implantation and operation inside the dental microenvironment. Finally, the sustainable release of metallic nanoparticles could be challenging to reduce cytotoxicity while augmenting the therapeutic effects.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

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.

Recent Developments in Layer-by-Layer Assembly for Drug Delivery and Tissue Engineering Applications

Surfaces with biological functionalities are of great interest for biomaterials, tissue engineering, biophysics, and for controlling biological processes. The layer-by-layer (LbL) assembly is a highly versatile methodology introduced 30 years ago, which consists of assembling complementary polyelectrolytes or biomolecules in a stepwise manner to form thin self-assembled films. In view of its simplicity, compatibility with biological molecules, and adaptability to any kind of supporting material carrier, this technology has undergone major developments over the past decades. Specific applications have emerged in different biomedical fields owing to the possibility to load or immobilize biomolecules with preserved bioactivity, to use an extremely broad range of biomolecules and supporting carriers, and to modify the film's mechanical properties via crosslinking. In this review, the focus is on the recent developments regarding LbL films formed as 2D or 3D objects for applications in drug delivery and tissue engineering. Possible applications in the fields of vaccinology, 3D biomimetic tissue models, as well as bone and cardiovascular tissue engineering are highlighted. In addition, the most recent technological developments in the field of film construction, such as high-content liquid handling or machine learning, which are expected to open new perspectives in the future developments of LbL, are presented.

The state-of-art polyurethane nanoparticles for drug delivery applications

Nowadays, polyurethanes (PUs) stand out as a promising option for drug delivery owing to their versatile properties. PUs have garnered significant attention in the biomedical sector and are extensively employed in diverse forms, including bulk devices, coatings, particles, and micelles. PUs are crucial in delivering various therapeutic agents such as antibiotics, anti-cancer medications, dermal treatments, and intravaginal rings. Effective drug release management is essential to ensure the intended therapeutic impact of PUs. Commercially available PU-based drug delivery products exemplify the adaptability of PUs in drug delivery, enabling researchers to tailor the polymer properties for specific drug release patterns. This review primarily focuses on the preparation of PU nanoparticles and their physiochemical properties for drug delivery applications, emphasizing how the formation of PUs affects the efficiency of drug delivery systems. Additionally, cutting-edge applications in drug delivery using PU nanoparticle systems, micelles, targeted, activatable, and fluorescence imaging-guided drug delivery applications are explored. Finally, the role of artificial intelligence and machine learning in drug design and delivery is discussed. The review concludes by addressing the challenges and providing perspectives on the future of PUs in drug delivery, aiming to inspire the design of more innovative solutions in this field.

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.

Fusion and classification algorithm of octacalcium phosphate production based on XRD and FTIR data

The present manuscript tested an automated analysis sequence to provide a decision support system to track the OCP synthesis from [Formula: see text]-TCP over time. Initially, the XRD and FTIR signals from a hundredfold scaled-up hydrolysis of OCP from [Formula: see text]-TCP were fused and modeled by the curve fitting based on the significantly established maxima from the literature and nine features extracted from the fitted shapes. Afterward, the analysis sequence enclosed the machine learning techniques for feature ranking, spatial filtering, and dimensionality reduction to support the automatic recognition of the synthesis stages. The proposed analysis pipeline for OCP identification might be the foundation for a decision support system explicitly targeting OCP synthesis. Future projects will exploit the suggested methodology for pinpointing the OCP production over time (including the intermediary phases present in the OCP formation) and for evaluating whether biological variables might be merged with biomaterial properties to build a unified model of tissue response to the implant.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Application of Artificial Intelligence at All Stages of Bone Tissue Engineering

The development of artificial intelligence (AI) has revolutionized medical care in recent years and plays a vital role in a number of areas, such as diagnostics and forecasting. In this review, we discuss the most promising areas of AI application to the field of bone tissue engineering and prosthetics, which can drastically benefit from AI-assisted optimization and patient personalization of implants and scaffolds in ways ranging from visualization and real-time monitoring to the implantation cases prediction, thereby leveraging the compromise between specific architecture decisions, material choice, and synthesis procedure. With the emphasized crucial role of accuracy and robustness of developed AI algorithms, especially in bone tissue engineering, it was shown that rigorous validation and testing, demanding large datasets and extensive clinical trials, are essential, and we discuss how through developing multidisciplinary cooperation among biology, chemistry with materials science, and AI, these challenges can be addressed.

Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.

DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline

Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.

Optimization of Gelatin Methacryloyl Hydrogel Properties through an Artificial Neural Network Model

Gelatin methacryloyl (GelMA) hydrogels are promising materials for tissue engineering applications due to their biocompatibility and tunable properties. However, the time-consuming process of preparing GelMA hydrogels with desirable properties for specific biomedical applications limits their clinical use. Visible-light-induced cross-linking is a well-known method for the preparation of GelMA hydrogels; however, a comprehensive investigation on the influence of critical parameters such as Eosin Y (EY), triethanolamine (TEA), and N-vinyl-2-pyrrolidone (NVP) concentrations on the stiffness and gelation time has yet to be performed. In this study, we systematically investigated the effect of these critical parameters on the stiffness and gelation time of GelMA hydrogels. We developed an artificial neural network (ANN) model with three input variables, EY, TEA, and NVP concentrations, and two output variables, Young's modulus and gelation time, derived from our experimental design. Through the alteration of individual chemical concentrations, [EY] between 0.005 and 0.5 mM and [TEA] and [NVP] between 10 and 1000 mM, we studied the impact of these alterations on the real-time values of stiffness and gelation time. Furthermore, we demonstrated the validity of the ANN model in predicting the properties of GelMA hydrogels. We also studied cell survival to establish nontoxic concentration ranges for each component, enabling safer use of GelMA hydrogels in relevant biomedical applications. Our results showed that the ANN model can accurately predict the properties of GelMA hydrogels, allowing for the synthesis of hydrogels with desirable stiffness for various biomedical applications. In conclusion, our study provides a comprehensive library that characterizes the stiffness and gelation time and demonstrates the potential of the ANN model to predict these properties of GelMA hydrogels depending on the critical parameters. The ANN models developed here can facilitate the optimization of GelMA hydrogels with the most efficient mechanical properties that resemble a native extracellular matrix and better address the need in the in vivo microenvironment. The approach of this study is to bring research about the synthesis of GelMA hydrogels to a new level where the synthesis of these hydrogels can be standardized with minimum cost and effort.

Valorization of organic wastes using bioreactors for polyhydroxyalkanoate production: Recent advancement, sustainable approaches, challenges, and future perspectives

Microbial polyhydroxyalkanoates (PHA) are encouraging biodegradable polymers, which may ease the environmental problems caused by petroleum-derived plastics. However, there is a growing waste removal problem and the high price of pure feedstocks for PHA biosynthesis. This has directed to the forthcoming requirement to upgrade waste streams from various industries as feedstocks for PHA production. This review covers the state-of-the-art progress in utilizing low-cost carbon substrates, effective upstream and downstream processes, and waste stream recycling to sustain entire process circularity. This review also enlightens the use of various batch, fed-batch, continuous, and semi-continuous bioreactor systems with flexible results to enhance the productivity and simultaneously cost reduction. The life-cycle and techno-economic analyses, advanced tools and strategies for microbial PHA biosynthesis, and numerous factors affecting PHA commercialization were also covered. The review includes the ongoing and upcoming strategies viz. metabolic engineering, synthetic biology, morphology engineering, and automation to expand PHA diversity, diminish production costs, and improve PHA production with an objective of "zero-waste" and "circular bioeconomy" for a sustainable future.

Emerging Bioprinting for Wound Healing

Bioprinting has attracted much attention due to its suitability for fabricating biomedical devices. In particular, bioprinting has become one of the growing centers in the field of wound healing, with various types of bioprinted devices being developed, including 3D scaffolds, microneedle patches, and flexible electronics. Bioprinted devices can be designed with specific biostructures and biofunctions that closely match the shape of wound sites and accelerate the regeneration of skin through various approaches. Herein, a comprehensive review of the bioprinting of smart wound dressings is presented, emphasizing the crucial effect of bioprinting in determining biostructures and biofunctions. The review begins with an overview of bioprinting techniques and bioprinted devices, followed with an in-depth discussion of polymer-based inks, modification strategies, additive ingredients, properties, and applications. The strategies for the modification of bioprinted devices are divided into seven categories, including chemical synthesis of novel inks, physical blending, coaxial bioprinting, multimaterial bioprinting, physical absorption, chemical immobilization, and hybridization with living cells, and examples are presented. Thereafter, the frontiers of bioprinting and wound healing, including 4D bioprinting, artificial intelligence-assisted bioprinting, and in situ bioprinting, are discussed from a perspective of interdisciplinary sciences. Finally, the current challenges and future prospects in this field are highlighted.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Preclinical in vitro evaluation of implantable materials: conventional approaches, new models and future directions

Nowadays, implants and prostheses are widely used to repair damaged tissues or to treat different diseases, but their use is associated with the risk of infection, inflammation and finally rejection. To address these issues, new antimicrobial and anti-inflammatory materials are being developed. Aforementioned materials require their thorough preclinical testing before clinical applications can be envisaged. Although many researchers are currently working on new in vitro tissues for drug screening and tissue replacement, in vitro models for evaluation of new biomaterials are just emerging and are extremely rare. In this context, there is an increased need for advanced in vitro models, which would best recapitulate the in vivo environment, limiting animal experimentation and adapted to the multitude of these materials. Here, we overview currently available preclinical methods and models for biological in vitro evaluation of new biomaterials. We describe several biological tests used in biocompatibility assessment, which is a primordial step in new material's development, and discuss existing challenges in this field. In the second part, the emphasis is made on the development of new 3D models and approaches for preclinical evaluation of biomaterials. The third part focuses on the main parameters to consider to achieve the optimal conditions for evaluating biocompatibility; we also overview differences in regulations across different geographical regions and regulatory systems. Finally, we discuss future directions for the development of innovative biomaterial-related assays: in silico models, dynamic testing models, complex multicellular and multiple organ systems, as well as patient-specific personalized testing approaches.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

Towards machine learning for hydrogel drug delivery systems

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.

Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications

The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Progress in Biomaterials for Cardiac Tissue Engineering and Regeneration

Cardiovascular diseases are one of the leading global causes of morbidity and mortality, posing considerable health and economic burden on patients and medical systems worldwide. This phenomenon is attributed to two main motives: poor regeneration capacity of adult cardiac tissues and insufficient therapeutic options. Thus, the context calls for upgrading treatments to deliver better outcomes. In this respect, recent research has approached the topic from an interdisciplinary perspective. Combining the advances encountered in chemistry, biology, material science, medicine, and nanotechnology, performant biomaterial-based structures have been created to carry different cells and bioactive molecules for repairing and restoring heart tissues. In this regard, this paper aims to present the advantages of biomaterial-based approaches for cardiac tissue engineering and regeneration, focusing on four main strategies: cardiac patches, injectable hydrogels, extracellular vesicles, and scaffolds and reviewing the most recent developments in these fields.

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.

Using artificial intelligence to predict the final color of leucite-reinforced ceramic restorations

OBJECTIVES

The aim of this study was to evaluate the accuracy of machine learning regression models in predicting the final color of leucite-reinforced glass CAD/CAM ceramic veneer restorations based on substrate shade, ceramic shade, thickness and translucency.

METHODS

Leucite-reinforced glass ceramics in four different shades were sectioned in thicknesses of 0.3, 0.5, 0.7, and 1.2 mm. The CIELab coordinates of each specimen were obtained over four different backgrounds (black, white, A1, and A3) interposed with an experimental translucent resin cement using a calibrated spectrophotometer. The color change (CIEDE2000) values, as well as all the CIELab values for each one of the experimental groups, were submitted to 28 different regression models. Each regression model was adjusted according to the weights of each dependent variable to achieve the best-fitting model.

RESULTS

Different substrates, ceramic shades, and thicknesses influenced the L, a, and b of the final restoration. Of all variables, the substrate influenced the final ceramic shade most, followed by the ceramic thickness and the L, a, and b of the ceramic. The decision tree regression model had the lowest mean absolute error and highest accuracy to predict the shade of the ceramic restoration according to the substrate shade, ceramic shade and thickness.

CLINICAL SIGNIFICANCE

The machine learning regression model developed in the study can help clinicians predict the final color of the ceramic veneers made with leucite-reinforced glass CAD/CAM ceramic HT and LT when cemented with translucent cements, based on the color of the substrate and ceramic thicknesses.

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.

Bottom-up design of hydrogels for programmable drug release

Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R²) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.

Polymeric biomaterials in the treatment of posterior segment diseases

Polymeric biomaterials are biological or synthetic substances which can be engineered to interact with biological systems for the diagnosis or treatment of diseases. These biomaterials have immense potential for treating eyes diseases, particularly the retina—a site of many inherited and acquired diseases. Polymeric biomaterials can be engineered to function both as an endotamponade agent and to prevent intraocular scarring in retinal detachment repair surgeries. They can also be designed as a drug delivery platform for treatment of retinal diseases. Finally, they can be used as scaffolds for cellular products and provide non-viral gene delivery solutions to the retina. This perspective article explains the role of polymeric biomaterials in the treatment of retinal conditions by highlighting recent advances being translated to clinical practice. The article will also identify potential hurdles to clinical translation as future research directions in the field.

A Generative Approach to Materials Discovery, Design, and Optimization

Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.

Pushing the Natural Frontier: Progress on the Integration of Biomaterial Cues toward Combinatorial Biofabrication and Tissue Engineering

The engineering of fully functional, biological-like tissues requires biomaterials to direct cellular events to a near-native, 3D niche extent. Natural biomaterials are generally seen as a safe option for cell support, but their biocompatibility and biodegradability can be just as limited as their bioactive/biomimetic performance. Furthermore, integrating different biomaterial cues and their final impact on cellular behavior is a complex equation where the outcome might be very different from the sum of individual parts. This review critically analyses recent progress on biomaterial-induced cellular responses, from simple adhesion to more complex stem cell differentiation, looking at the ever-growing possibilities of natural materials modification. Starting with a discussion on native material formulation and the inclusion of cell-instructive cues, the roles of shape and mechanical stimuli, the susceptibility to cellular remodeling, and the often-overlooked impact of cellular density and cell-cell interactions within constructs, are delved into. Along the way, synergistic and antagonistic combinations reported in vitro and in vivo are singled out, identifying needs and current lessons on the development of natural biomaterial libraries to solve the cell-material puzzle efficiently. This review brings together knowledge from different fields envisioning next-generation, combinatorial biomaterial development toward complex tissue engineering.

Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning

Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.

Viscoelastic Biomaterials for Tissue Regeneration

The extracellular matrix (ECM) mechanical properties regulate key cellular processes in tissue development and regeneration. The majority of scientific investigation has focused on ECM elasticity as the primary mechanical regulator of cell and tissue behavior. However, all living tissues are viscoelastic, exhibiting both solid- and liquid-like mechanical behavior. Despite increasing evidence regarding the role of ECM viscoelasticity in directing cellular behavior, this aspect is still largely overlooked in the design of biomaterials for tissue regeneration. Recently, with the emergence of various bottom-up material design strategies, new approaches can deliver unprecedented control over biomaterial properties at multiple length scales, thus enabling the design of viscoelastic biomaterials that mimic various aspect of the native tissue ECM microenvironment. This review describes key considerations for the design of viscoelastic biomaterials for tissue regeneration. We provide an overview of the role of matrix viscoelasticity in directing cell behavior towards regenerative outcomes, highlight recent strategies utilizing viscoelastic hydrogels for regenerative therapies, and outline remaining challenges, potential solutions, and emerging applications for viscoelastic biomaterials in tissue engineering and regenerative medicine.

Upconversion nanoparticles regulated drug & gas dual-effective nanoplatform for the targeting cooperated therapy of thrombus and anticoagulation

Thromboembolism is the leading cause of cardiovascular mortality. Currently, for the lack of targeting, short half-life, low bioavailability and high bleeding risk of the classical thrombolytic drugs, pharmacological thrombolysis is usually a slow process based on micro-pumping. In addition, frequently monitoring and regulating coagulation functions are also required during (and after) the process of thrombolysis. To address these issues, a targeted thrombolytic and anticoagulation nanoplatform (UCATS-UK) is developed based on upconversion nanoparticles (UCNPs) that can convert 808 or 980 nm near-infrared (NIR) light into UV/blue light. This nanoplatform can target and enrich in the thrombus site. Synergistic thrombolysis and anticoagulation therapy thus could be realized through the controlled release of urokinase (UK) and nitric oxide (NO). Both in vitro and in vivo experiments have confirmed the excellent thrombolytic and anticoagulative capabilities of this multifunctional nanoplatform. Combined with the unique fluorescent imaging capability of UCNPs, this work is expected to contribute to the development of clinical thrombolysis therapy towards an integrated system of imaging, diagnosis and treatment.

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This work is not only the first application of UCNPs in the thrombolysis therapy, but also the first attempt to develop a dual effective drug & gas nanoplatform for thrombolytic & anticoagulation therapy.

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Besides conventional in vitro and animal experiments, a 3D printed vascular model is also constructed to further verify the feasibility of UCATS-UK.

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Through surface chemical modification, the nanoplatform possesses the capabilities of targeting thrombus, as well as light-controlled NO release for drug-free anticoagulation therapy.

Potential of Recycled Silicon and Silicon-Based Thermoelectrics for Power Generation

Thermoelectrics can convert waste heat to electricity and vice versa. The energy conversion efficiency depends on materials figure of merit, zT, and Carnot efficiency. Due to the higher Carnot efficiency at a higher temperature gradient, high-temperature thermoelectrics are attractive for waste heat recycling. Among high-temperature thermoelectrics, silicon-based compounds are attractive due to the confluence of light weight, high abundance, and low cost. Adding to their attractiveness is the generally defect-tolerant nature of thermoelectrics. This makes them a suitable target application for recycled silicon waste from electronic (e-waste) and solar cell waste. In this review, we summarize the usage of high-temperature thermoelectric generators (TEGs) in applications such as commercial aviation and space voyages. Special emphasis is placed on silicon-based compounds, which include some recent works on recycled silicon and their thermoelectric properties. Besides materials design, device designing considerations to further maximize the energy conversion efficiencies are also discussed. The insights derived from this review can be used to guide sustainable recycling of e-waste into thermoelectrics for power harvesting.

Prosthetic Materials Used for Implant-Supported Restorations and Their Biochemical Oral Interactions: A Narrative Review

The purpose of this study is to outline relevant elements regarding the biochemical interactions between prosthetic materials used for obtaining implant-supported restorations and the oral environment. Implant-supported prostheses have seen unprecedented development in recent years, benefiting from the emergence of both new prosthetic materials (with increased biocompatibility and very good mechanical behavior), and computerized manufacturing technologies, which offer predictability, accuracy, and reproducibility. On the other hand, the quality of conventional materials for obtaining implant-supported prostheses is acknowledged, as they have already proven their clinical performance. The properties of PMMA (poly (methyl methacrylate))-which is a representative interim material frequently used in prosthodontics-and of PEEK (polyether ether ketone)-a biomaterial which is placed on the border between interim and final prosthetic use-are highlighted in order to illustrate the complex way these materials interact with the oral environment. In regard to definitive prosthetic materials used for obtaining implant-supported prostheses, emphasis is placed on zirconia-based ceramics. Zirconia exhibits several distinctive advantages (excellent aesthetics, good mechanical behavior, biocompatibility), through which its clinical applicability has become increasingly wide. Zirconia's interaction with the oral environment (fibroblasts, osteoblasts, dental pulp cells, macrophages) is presented in a relevant synthesis, thus revealing its good biocompatibility.