Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters

Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with R2 = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (i.e., polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.

MicroRNA-200c Release from Gelatin-Coated 3D-Printed PCL Scaffolds Enhances Bone Regeneration

The fabrication of clinically relevant synthetic bone grafts relies on combining multiple biodegradable biomaterials to create a structure that supports the regeneration of defects while delivering osteogenic biomolecules that enhance regeneration. MicroRNA-200c (miR-200c) functions as a potent osteoinductive biomolecule to enhance osteogenic differentiation and bone formation; however, synthetic tissue-engineered bone grafts that sustain the delivery of miR-200c for bone regeneration have not yet been evaluated. In this study, we created novel, multimaterial, synthetic bone grafts from gelatin-coated 3D-printed polycaprolactone (PCL) scaffolds. We attempted to optimize the release of pDNA encoding miR-200c by varying gelatin types, concentrations, and polymer crosslinking materials to improve its functions for bone regeneration. We revealed that by modulating gelatin type, coating material concentration, and polymer crosslinking, we effectively altered the release rates of pDNA encoding miR-200c, which promoted osteogenic differentiation in vitro and bone regeneration in a critical-sized calvarial bone defect animal model. We also demonstrated that crosslinking the gelatin coatings on the PCL scaffolds with low-concentration glutaraldehyde was biocompatible and increased cell attachment. These results strongly indicate the potential use of gelatin-based systems for pDNA encoding microRNA delivery in gene therapy and further demonstrate the effectiveness of miR-200c for enhancing bone regeneration from synthetic bone grafts.

3D bioprinting technology to construct bone reconstruction research model and its feasibility evaluation

Objective: To explore and construct a 3D bone remodeling research model displaying stability, repeatability, and precise simulation of the physiological and biochemical environment in vivo. Methods: In this study, 3D bioprinting was used to construct a bone reconstruction model. Sodium alginate (SA), hydroxyapatite (HA) and gelatin (Gel) were mixed into hydrogel as scaffold material. The osteoblast precursor cells MC3T3-E1 and osteoclast precursor cells RAW264.7 were used as seed cells, which may or may not be separated by polycarbonate membrane. The cytokines osteoprotegerin (OPG) and receptor activator of NF-κB ligand (RANKL) were used to induce cell differentiation. The function of scaffolds in the process of bone remodeling was analyzed by detecting the related markers of osteoblasts (alkaline phosphatase, ALP) and osteoclasts (tartrate resistant acid phosphatase, TRAP). Results: The scaffold showed good biocompatibility and low toxicity. The surface morphology aided cell adhesion and growth. The scaffold had optimum degradability, water absorption capacity and porosity, which are in line with the conditions of biological experiments. The effect of induced differentiation of cells was the best when cultured alone. After direct contact between the two types of cells at 2D or 3D level, the induced differentiation of cells was inhibited to varying degrees, although they still showed osteogenesis and osteoclast. After the cells were induced by indirect contact culture, the effect of induced differentiation improved when compared with direct contact culture, although it was still not as good as that of single culture. On the whole, the effect of inducing differentiation at 3D level was the same as that at 2D level, and its relative gene expression and enzyme activity were higher than that in the control group. Hence the scaffold used in this study could induce osteogenesis as well as osteoclast, thereby rendering it more effective in inducing new bone formation. Conclusion: This method can be used to construct the model of 3D bone remodeling mechanism.

Aligned Ice Templated Biomaterial Strategies for the Musculoskeletal System

Aligned pore structures present many advantages when conceiving biomaterial strategies for treatment of musculoskeletal disorders. Aligned ice templating (AIT) is one of the many different techniques capable of producing anisotropic porous scaffolds; its high versatility allows for the formation of structures with tunable pore sizes, as well as the use of many different materials. AIT has been found to yield improved compressive properties for bone tissue engineering (BTE), as well as higher tensile strength and optimized cellular alignment and proliferation in tendon and muscle repair applications. This review evaluates the work that has been done in the last decade toward the production of aligned pore structures by AIT with an outlook on the musculoskeletal system. This work describes the fundamentals of the AIT technique and focuses on the research carried out to optimize the biomechanical properties of scaffolds by modifying the pore structure, categorizing by material type and application. Related topics including growth factor incorporation into AIT scaffolds, drug delivery applications, and studies about immune system response will be discussed.

MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds

Cardiovascular disease is one of the leading causes of mortality worldwide and is responsible for millions of deaths annually. One of the most promising approaches to deal with this problem, which has spread recently, is cardiac tissue engineering (CTE). Many researchers have tried developing scaffolds with different materials, cell lines, and fabrication methods to help regenerate heart tissue. Machine learning (ML) is one of the hottest topics in science and technology, revolutionizing many fields and changing our perspective on solving problems. As a result of using ML, some scientific issues have been resolved, including protein-folding, a challenging problem in biology that remained unsolved for 50 years. However, it is not well addressed in tissue engineering. An AI-based software was developed by our group called MLATE (Machine Learning Applications in Tissue Engineering) to tackle tissue engineering challenges, which highly depend on conducting costly and time-consuming experiments. For the first time, to the best of our knowledge, a CTE scaffold dataset was created by collecting specifications from the literature, including different materials, cell lines, and fabrication methods commonly used in CTE scaffold development. These specifications were used as variables in the study. Then, the CTE scaffolds were rated based on cell behaviors such as cell viability, growth, proliferation, and differentiation on the scaffold on a scale of 0-3. These ratings were considered a function of the variables in the gathered dataset. It should be stated that this study was merely based on information available in the literature. Then, twenty-eight ML algorithms were applied to determine the most effective one for predicting cell behavior on CTE scaffolds fabricated by different materials, compositions, and methods. The results indicated the high performance of XGBoost with an accuracy of 87%. Also, by implementing ensemble learning algorithms and using five algorithms with the best performance, an accuracy of 93% with the AdaBoost Classifier and Voting Classifier was achieved. Finally, the open-source software developed in this study was made available for everyone by publishing the best model along with a step-by-step guide to using it online at: https://github.com/saeedrafieyan/MLATE.

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.

Design, synthesis, and characterization of a novel dual cross-linked gelatin-based bioadhesive for hard and soft tissues adhesion capability

Many surgical treatments require a suitable tissue adhesive that maintains its performance in wet conditions and can be applied simultaneously for hard and soft tissues. In the present study, a dual cross-linked tissue adhesive was synthesized by mixing the gelatin methacryloyl (Gel-MA) and gelatin-dopamine conjugate (Gel-Dopa). The setting reaction was based on a photopolymerization process in the presence of a combination of riboflavin and triethanolamine and a chemical cross-linking process attributed to the genipin as a natural cross-linker. Modified gelatin macromolecules were characterized and the best wavelength for free radical generation in the presence of riboflavin was obtained. Tissue adhesives were prepared with 30% hydrogels of Gel-MA and Gel-Dopa with different ratios in distilled water. The gelation occurred in a short time after light irradiation. The chemical, mechanical, physical, and cytotoxicity properties of the tissue adhesives were evaluated. The results showed that despite photopolymerization, chemical crosslinking with genipin played a more critical role in the setting process. Water uptake, degradation behavior, cytotoxicity, and adhesion properties of the adhesives were correlated with the ratio of the components. The SEM images showed a porous structure that could ensure the entry of cells and nutrients into the surgical area. While acceptable properties in most experiments were observed, all features were improved as the Gel-Dopa ratio increased. Also, the obtained hydrogels revealed excellent adhesive properties, particularly with bone even after wet incubation, and it was attributed to the amount of gelatin-dopamine conjugate. From the obtained results, it was concluded that a dual adhesive hydrogel based on gelatin macromolecules could be a good candidate as a tissue adhesive in wet condition.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

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

Application of artificial neural networks to predict Young's moduli of cartilage scaffolds: An in-vitro and micromechanical study

In this study, four-phase Gelatin-Polypyrrole-Akermanite-Magnetite scaffolds were fabricated and analyzed using in-vitro tests and numerical simulations. Such scaffolds contained various amounts of Magnetite bioceramics as much as 0, 5, 10, and 15 wt% of Gelatin-Polypyrrole-Akermanite biocomposite. X-ray diffraction analysis and Fourier transform infrared spectroscopy were conducted. Swelling and degradation of the scaffolds were studied by immersing them in phosphate-buffered saline, PBS, solution. Magnetite bioceramics decreased the swelling percent and degradation duration. By immersing scaffolds in simulated body fluid, the highest formation rate of Apatite was observed in the 15 wt% Magnetite samples. The mean pore size was in an acceptable range to provide suitable conditions for cell proliferation. MG-63 cells were cultured on extracts of the scaffolds for 24, 48, and 72 h and their surfaces for 24 h. Cell viabilities and cell morphologies were assessed. Afterward, micromechanical models with spherical and polyhedral voids and artificial neural networks were employed to predict Young's moduli of the scaffolds. Based on the results of finite element analyses, spherical-shaped void models made the best predictions of elastic behavior in the 0, 5 wt% Magnetite scaffolds compared to the experimental data. Results of the simulations and experimental tests for the ten wt% Magnetite samples were well matched in both micromechanical models. In the 15 wt% Magnetite sample, models with polyhedral voids could precisely predict Young's modulus of such scaffolds.

Functional Hydrogels with Chondroitin Sulfate Release Properties Regulate the Angiogenesis Behaviors of Endothelial Cells

Functional hydrogels with properties that mimic the structure of extracellular matrix (ECM) and regulate cell behaviors have drawn much attention in biomedical applications. Herein, gelatin-based hydrogels were designed and loaded with chondroitin sulfate (CS) to endow biological regulation on the angiogenesis behaviors of endothelial cells (ECs). Manufactured hydrogels containing various amounts of CS were characterized via methods including mechanical tests, cytocompatibility, hemolysis, and angiogenesis assays. The results showed that the prepared hydrogels exhibited excellent mechanical stability, cytocompatibility, and hemocompatibility. Additionally, the angiogenesis behaviors of ECs were obviously promoted. However, excessive loading of CS would weaken the effect due to a higher proportion of occupation on the cell membrane. In conclusion, this investigation highlights the great potential of these hydrogels in treating ischemic diseases and accelerating tissue regeneration in terms of regulating the angiogenesis process via CS release.

Evaluation of different crosslinking methods in altering the properties of extrusion-printed chitosan-based multi-material hydrogel composites

Three-dimensional printing technologies exhibit tremendous potential in the advancing fields of tissue engineering and regenerative medicine due to the precise spatial control over depositing the biomaterial. Despite their widespread utilization and numerous advantages, the development of suitable novel biomaterials for extrusion-based 3D printing of scaffolds that support cell attachment, proliferation, and vascularization remains a challenge. Multi-material composite hydrogels present incredible potential in this field. Thus, in this work, a multi-material composite hydrogel with a promising formulation of chitosan/gelatin functionalized with egg white was developed, which provides good printability and shape fidelity. In addition, a series of comparative analyses of different crosslinking agents and processes based on tripolyphosphate (TPP), genipin (GP), and glutaraldehyde (GTA) were investigated and compared to select the ideal crosslinking strategy to enhance the physicochemical and biological properties of the fabricated scaffolds. All of the results indicate that the composite hydrogel and the resulting scaffolds utilizing TPP crosslinking have great potential in tissue engineering, especially for supporting neo-vessel growth into the scaffold and promoting angiogenesis within engineered tissues.

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Generation and Evaluation of Novel Biomaterials Based on Decellularized Sturgeon Cartilage for Use in Tissue Engineering

Because cartilage has limited regenerative capability, a fully efficient advanced therapy medicinal product is needed to treat severe cartilage damage. We evaluated a novel biomaterial obtained by decellularizing sturgeon chondral endoskeleton tissue for use in cartilage tissue engineering. In silico analysis suggested high homology between human and sturgeon collagen proteins, and ultra-performance liquid chromatography confirmed that both types of cartilage consisted mainly of the same amino acids. Decellularized sturgeon cartilage was recellularized with human chondrocytes and four types of human mesenchymal stem cells (MSC) and their suitability for generating a cartilage substitute was assessed ex vivo and in vivo. The results supported the biocompatibility of the novel scaffold, as well as its ability to sustain cell adhesion, proliferation and differentiation. In vivo assays showed that the MSC cells in grafted cartilage disks were biosynthetically active and able to remodel the extracellular matrix of cartilage substitutes, with the production of type II collagen and other relevant components, especially when adipose tissue MSC were used. In addition, these cartilage substitutes triggered a pro-regenerative reaction mediated by CD206-positive M2 macrophages. These preliminary results warrant further research to characterize in greater detail the potential clinical translation of these novel cartilage substitutes.

Preparation of gelatin-based hydrogels with tunable mechanical properties and modulation on cell-matrix interactions

Natural polymer material-based hydrogels normally show inferior mechanical stability and strength to bear large deformation and cyclic loading, therefore their applications in food, biomedical and tissue engineering fields are greatly limited. In this study, gelatin-based hydrogels with remarkable stability, as well as tunable mechanical properties, were prepared via a facile method known as the Hofmeister effect. The higher concentration of potassium sulfatesolution resulted in more dehydration and molecular chain folding, thus the treated hydrogels showed significantly improved tensile and compressive modulus, and decreased equilibrium swelling ratio, as revealed by scanning electron microscopy (SEM), Fourier transform infraredspectroscopy (FTIR), and mechanical tests, etc. Additionally, the reinforced hydrogels were recoverable and biocompatible to modulate the proliferation behavior of human umbilical vein endothelial cells. In conclusion, this paper provides a facile reference for tuning mechanical properties of gelatin-based hydrogels and cell-hydrogel interactions, which show potential capacity in tissue engineering and biomedical fields.

Biopolymers Hybrid Particles Used in Dentistry

This literature review provides an overview of the fabrication and application of biopolymer hybrid particles in dentistry. A total of 95 articles have been included in this review. In the review paper, the common inorganic particles and biopolymers used in dentistry are discussed in general, and detailed examples of inorganic particles (i.e., hydroxyapatite, calcium phosphate, and bioactive glass) and biopolymers such as collagen, gelatin, and chitosan have been drawn from the scientific literature and practical work. Among the included studies, calcium phosphate including hydroxyapatite is the most widely applied for inorganic particles used in dentistry, but bioactive glass is more applicable and multifunctional than hydroxyapatite and is currently used in clinical practice. Today, biopolymer hybrid particles are receiving more attention as novel materials for several applications in dentistry, such as drug delivery systems, bone repair, and periodontal regeneration surgery. The literature published on the biopolymer gel-assisted synthesis of inorganic particles for dentistry is somewhat limited, and therefore, this article focuses on reviewing and discussing the biopolymer hybrid particles used in dentistry.

Regeneration of skeletal system with genipin crosslinked biomaterials

Natural biomaterials, such as collagen, gelatin, and chitosan, are considered as promising candidates for use in tissue regeneration treatment, given their similarity to natural tissues regarding components and structure. Nevertheless, only receiving a crosslinking process can these biomaterials exhibit sufficient strength to bear high tensile loads for use in skeletal system regeneration. Recently, genipin, a natural chemical compound extracted from gardenia fruits, has shown great potential as a reliable crosslinking reagent, which can reconcile the crosslinking effect and biosafety profile simultaneously. In this review, we briefly summarize the genipin extraction process, biosafety, and crosslinking mechanism. Subsequently, the applications of genipin regarding aiding skeletal system regeneration are discussed in detail, including the advances and technological strategies for reconstructing cartilage, bone, intervertebral disc, tendon, and skeletal muscle tissues. Finally, based on the specific pharmacological functions of genipin, its potential applications, such as its use in bioprinting and serving as an antioxidant and anti-tumor agent, and the challenges of genipin in the clinical applications in skeletal system regeneration are also presented.