Machine Learning in Tissue Engineering

Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.

Optimization by mixture design of chitosan/multi-phase calcium phosphate/BMP-2 biomimetic scaffolds for bone tissue engineering

The modulation of cell behavior during culture is one of the most important aspects of bone tissue engineering because of the necessity for a complex mechanical and biochemical environment. This study aimed to improve the physicochemical properties of chitosan/multi-phase calcium phosphate (MCaP) scaffolds using an optimized mixture design experiment and evaluate the effect of biofunctionalization of the obtained scaffolds with the bone morphogenetic protein BMP-2 on stem cell behavior. The present study evaluated the compressive strength, elastic modulus, porosity, pore diameter, and degradation in simulated body fluids and integrated these responses using desirability. The properties of the scaffolds with the best desirability (18.4% of MCaP) were: compressive strength of 23 kPa, elastic modulus of 430 kPa, pore diameter of 163 μm, porosity of 92%, and degradation of 20% after 21 days. Proliferation and differentiation experiments were conducted using dental pulp stem cells after grafting BMP-2 onto scaffolds via the carbodiimide route. These experiments showed that MCaP promoted cell proliferation and increased alkaline phosphatase activity, whereas BMP-2 enhanced cell differentiation. This study demonstrates that optimizing the composition of a mixture of chitosan and MCaP improves the physicochemical and biological properties of scaffolds, indicating that this solution is viable for application in bone tissue engineering.

Trends in bioactivity: inducing and detecting mineralization of regenerative polymeric scaffolds

Due to limitations of biological and alloplastic grafts, regenerative engineering has emerged as a promising alternative to treat bone defects. Bioactive polymeric scaffolds are an integral part of such an approach. Bioactivity importantly induces hydroxyapatite mineralization that promotes osteoinductivity and osseointegration with surrounding bone tissue. Strategies to confer bioactivity to polymeric scaffolds utilize bioceramic fillers, coatings and surface treatments, and additives. These approaches can also favorably impact mechanical and degradation properties. A variety of fabrication methods are utilized to prepare scaffolds with requisite morphological features. The bioactivity of scaffolds may be evaluated with a broad set of techniques, including in vitro (acellular and cellular) and in vivo methods. Herein, we highlight contemporary and emerging approaches to prepare and assess scaffold bioactivity, as well as existing challenges.

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.

Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review

We overview recent findings achieved in the field of model-driven development of additively manufactured porous materials for the development of a new generation of bioactive implants for orthopedic applications. Porous structures produced from biocompatible titanium alloys using selective laser melting can present a promising material to design scaffolds with regulated mechanical properties and with the capacity to be loaded with pharmaceutical products. Adjusting pore geometry, one could control elastic modulus and strength/fatigue properties of the engineered structures to be compatible with bone tissues, thus preventing the stress shield effect when replacing a diseased bone fragment. Adsorption of medicals by internal spaces would make it possible to emit the antibiotic and anti-tumor agents into surrounding tissues. The developed internal porosity and surface roughness can provide the desired vascularization and osteointegration. We critically analyze the recent advances in the field featuring model design approaches, virtual testing of the designed structures, capabilities of additive printing of porous structures, biomedical issues of the engineered scaffolds, and so on. Special attention is paid to highlighting the actual problems in the field and the ways of their solutions.

Rapid prediction of lab-grown tissue properties using deep learning

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set withN = 6400 cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of thepix2pixdeep learning model, with an additional 100 cases that were unseen in the training of the neural network for review and testing of the trained model. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.

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.

Bioprinted Hydrogels for Fibrosis and Wound Healing: Treatment and Modeling

Three-dimensional (3D) printing has been used to fabricate biomaterial scaffolds with finely controlled physical architecture and user-defined patterning of biological ligands. Excitingly, recent advances in bioprinting have enabled the development of highly biomimetic hydrogels for the treatment of fibrosis and the promotion of wound healing. Bioprinted hydrogels offer more accurate spatial recapitulation of the biochemical and biophysical cues that inhibit fibrosis and promote tissue regeneration, augmenting the therapeutic potential of hydrogel-based therapies. Accordingly, bioprinted hydrogels have been used for the treatment of fibrosis in a diverse array of tissues and organs, including the skin, heart, and endometrium. Furthermore, bioprinted hydrogels have been utilized for the healing of both acute and chronic wounds, which present unique biological microenvironments. In addition to these therapeutic applications, hydrogel bioprinting has been used to generate in vitro models of fibrosis in a variety of soft tissues such as the skin, heart, and liver, enabling high-throughput drug screening and tissue analysis at relatively low cost. As biological research begins to uncover the spatial biological features that underlie fibrosis and wound healing, bioprinting offers a powerful toolkit to recapitulate spatially defined pro-regenerative and anti-fibrotic cues for an array of translational applications.