AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.

Bioprinting is a rapidly evolving field, as represented by the exponential growth of articles and reviews published each year on the topic. As the number of publications increases, there is a need for an automatic tool that can help researchers do more comprehensive literature analysis, standardize the nomenclature, and so accelerate the development of novel manufacturing techniques and materials for the field. In this context, we propose an automatic keyword annotation model, based on Natural Language Processing (NLP) techniques, that can be used to find insights in the bioprinting scientific literature.

Cell Instructive Behavior of Composite Scaffolds in a Co-Culture of Human Mesenchymal Stem Cells and Peripheral Blood Mononuclear Cells.

The in vitro evaluation of 3D scaffolds for bone tissue engineering in mono-cultures is a common practice; however, it does not represent the native complex nature of bone tissue. Co-cultures of osteoblasts and osteoclasts, without the addition of stimulating agents for monitoring cellular cross-talk, remains a challenge. In this study, a growth factor-free co-culture of human bone marrow-derived mesenchymal stem cells (hBM-MSCs) and human peripheral blood mononuclear cells (hPBMCs) has been established and used for the evaluation of 3D-printed scaffolds for bone tissue engineering.

Enhancing quality control in bioprinting through machine learning.

Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e.

Effect of Uniaxial Compression Frequency on Osteogenic Cell Responses in Dynamic 3D Cultures.

The application of mechanical stimulation on bone tissue engineering constructs aims to mimic the native dynamic nature of bone. Although many attempts have been made to evaluate the effect of applied mechanical stimuli on osteogenic differentiation, the conditions that govern this process have not yet been fully explored. In this study, pre-osteoblastic cells were seeded on PLLA/PCL/PHBV (90/5/5 wt.

A computational analysis of a novel therapeutic approach combining an advanced medicinal therapeutic device and a fracture fixation assembly for the treatment of osteoporotic fractures: Effects of physiological loading, interface conditions, and fracture fixation materials.

The occurrence of periprosthetic femoral fractures (PFF) has increased in people with osteoporosis due to decreased bone density, poor bone quality, and stress shielding from prosthetic implants. PFF treatment in the elderly is a genuine concern for orthopaedic surgeons as no effective solution currently exists. Therefore, the goal of this study was to determine whether the design of a novel advanced medicinal therapeutic device (AMTD) manufactured from a polymeric blend in combination with a fracture fixation plate in the femur is capable of withstanding physiological loads without failure during the bone regenerative process.

A Computational Model for the Release of Bioactive Molecules by the Hydrolytic Degradation of a Functionalized Polyester-Based Scaffold.

This work presents a computational model to study the degradation behavior of polyester-based three-dimensional (3D) functionalized scaffolds for bone regeneration. As a case study, we investigated the behavior of a 3D-printed scaffold presenting a functionalized surface with ICOS-Fc, a bioactive protein able to stimulate bone regeneration and healing, inhibiting osteoclast activity. The aim of the model was to optimize the scaffold design to control its degradation and thus the release of grafted protein over time and space.

Promotion of In Vitro Osteogenic Activity by Melt Extrusion-Based PLLA/PCL/PHBV Scaffolds Enriched with Nano-Hydroxyapatite and Strontium Substituted Nano-Hydroxyapatite.

Bone tissue engineering has emerged as a promising strategy to overcome the limitations of current treatments for bone-related disorders, but the trade-off between mechanical properties and bioactivity remains a concern for many polymeric materials. To address this need, novel polymeric blends of poly-L-lactic acid (PLLA), polycaprolactone (PCL) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) have been explored. Blend filaments comprising PLLA/PCL/PHBV at a ratio of 90/5/5 wt% have been prepared using twin-screw extrusion.

A Deep Learning Quality Control Loop of the Extrusion-based Bioprinting Process.

Extrusion-based bioprinting (EBB) represents one of the most used deposition technologies in the field of bioprinting, thanks to key advantages such as the easy-to-use hardware and the wide variety of materials that can be successfully printed. In recent years, research efforts have been focused on implementing a quality control loop for EBB, which can reduce the trial-and-error process necessary to optimize the printing parameters for a specific ink, standardize the results of a print across multiple laboratories, and so accelerate the translation of extrusion bioprinted products to more impactful clinical applications. Due to its capacity to acquire relevant features from a training dataset and generalize to unseen data, machine learning (ML) is currently being studied in literature as a relevant enabling technology for quality control in EBB.

Molecular Imprinting Strategies for Tissue Engineering Applications: A Review.

Tissue Engineering (TE) represents a promising solution to fabricate engineered constructs able to restore tissue damage after implantation. In the classic TE approach, biomaterials are used alongside growth factors to create a scaffolding structure that supports cells during the construct maturation. A current challenge in TE is the creation of engineered constructs able to mimic the complex microenvironment found in the natural tissue, so as to promote and guide cell migration, proliferation, and differentiation.

Modeling the Three-Dimensional Bioprinting Process of β-Sheet Self-Assembling Peptide Hydrogel Scaffolds.

Extrusion-based three-dimensional (3D) bioprinting is nowadays the most efficient additive manufacturing technology to fabricate well-defined and clinical-scale relevant 3D scaffolds, exploiting soft biomaterials. However, trial and error approaches are usually employed to achieve the desired structures, thus leading to a waste of time and material. In this work, we show the potential of finite element (FE) simulation in predicting the printability of a biomaterial, in terms of extrudability and scaffold mechanical stability over time.