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. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

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. The approach is based on two main data sources, the abstracts and related author keywords, which are used to train a composite model based on (i) an embeddings part (using the FastText algorithm), which generates word vectors for an input keyword, and (ii) a classifier part (using the Support Vector Machine algorithm), to label the keyword based on its word vector into a manufacturing technique, employed material, or application of the bioprinted product. The composite model was trained and optimized based on a two-stage optimization procedure to yield the best classification performance. The annotated author keywords were then reprojected on the abstract collection to both generate a lexicon of the bioprinting field and extract relevant information, like technology trends and the relationship between manufacturing-material-application. The proposed approach can serve as a basis for more complex NLP-related analysis toward the automated analysis of the bioprinting 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. The scaffolds were produced from PLLA/PCL/PHBV polymeric blends, with two composite materials produced through the addition of 2.5% w/v nanohydroxyapatite (nHA) or strontium-substituted nanohydroxyapatite (Sr-nHA). Cell morphology data showed that hPBMCs remained undifferentiated in co-culture, while no obvious differences were observed in the mono- and co-cultures of hBM-MSCs. A significantly increased alkaline phosphatase (ALP) activity and osteogenic gene expression was observed in co-culture on Sr-nHA-containing scaffolds. Tartrate-resistant acid phosphatase (TRAP) activity and osteoclastogenic gene expression displayed significantly suppressed levels in co-culture on Sr-nHA-containing scaffolds. Interestingly, mono-cultures of hPBMCs on Sr-nHA-containing scaffolds indicated a delay in osteoclasts formation, as evidenced from TRAP activity and gene expression, demonstrating that strontium acts as an osteoclastogenesis inhibitor. This co-culture study presents an effective 3D model to evaluate the regenerative capacity of scaffolds for bone tissue engineering, thus minimizing time-consuming and costly in vivo experiments.

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. Two different scenarios were considered: (i) a scaffold without macroporosity presenting a functionalized external surface; and (ii) a scaffold presenting an internal functionalized macroporous architecture with open channels to locally deliver the degradation products.

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. The PLLA/PCL/PHBV blends were enriched with nano-hydroxyapatite (nano-HA) and strontium-substituted nano-HA (Sr-nano-HA) to produce composite filaments. Three-dimensional scaffolds were printed by fused deposition modelling from PLLA/PCL/PHBV blend and composite filaments and evaluated mechanically and biologically for their capacity to support bone formation in vitro. The composite scaffolds had a mean porosity of 40%, mean pores of 800 µm, and an average compressive modulus of 32 MPa. Polymer blend and enriched scaffolds supported cell attachment and proliferation. The alkaline phosphatase activity and calcium production were significantly higher in composite scaffolds compared to the blends. These findings demonstrate that thermoplastic polyesters (PLLA and PCL) can be combined with polymers produced via a bacterial route (PHBV) to produce polymer blends with excellent biocompatibility, providing additional options for polymer blend optimization. The enrichment of the blend with nano-HA and Sr-nano-HA powders enhanced the osteogenic potential in vitro.

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. This was achieved by developing a finite element (FE) model of the AMTD together with a fracture fixation assembly, and a femur with an implanted femoral stem. The response of both normal and osteoporotic bone was investigated by implementing their respective material properties in the model. Physiological loading simulating the peak load during standing, walking, and stair climbing was investigated. The results showed that the fixation assembly was the prime load bearing component for this configuration of devices. Within the fixation assembly, the bone screws were found to have the highest stresses in the fixation assembly for all the loading conditions. Whereas the stresses within the AMTD were significantly below the maximum yield strength of the device's polymeric blend material. Furthermore, this study also investigated the performance of different fixation assembly materials and found Ti-6Al-4V to be the optimal material choice from those included in this study.

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.%) polymeric blend scaffolds. The constructs were subjected every day to cyclic uniaxial compression for 40 min at a displacement of 400 µm, using three frequency values, 0.5, 1, and 1.5 Hz, for up to 21 days, and their osteogenic response was compared to that of static cultures. Finite element simulation was performed to validate the scaffold design and the loading direction, and to assure that cells inside the scaffolds would be subjected to significant levels of strain during stimulation. None of the applied loading conditions negatively affected the cell viability. The alkaline phosphatase activity data indicated significantly higher values at all dynamic conditions compared to the static ones at day 7, with the highest response being observed at 0.5 Hz. Collagen and calcium production were significantly increased compared to static controls. These results indicate that all of the examined frequencies substantially promoted the osteogenic capacity.

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. In this context, we propose a robust, deep learning-based control loop to automatically optimize the printing parameters and monitor the printing process online. We collected a comprehensive dataset of EBB prints by recording the process with a high-resolution webcam. To model multiple printing scenarios, each video represents a combination of multiple parameters, including printing set-up (either mechanical or pneumatic extrusion), material color, layer height, and infill density. After pre-processing, the collected dataset was used to thoroughly train and evaluate an ad hoc defined convolutional neural network by controlling over-fitting and optimizing the prediction time of the network. Finally, the ML model was used in a control loop to: (i) monitor the printing process and detect if a print with an error could be stopped before completion to save material and time and (ii) automatically optimize the printing parameters by combining the ML model with a previously published mathematical model of the EBB process. Altogether, we demonstrated for the first time how ML techniques can be used to automatize the EBB process, paving the way for a total quality control loop of the printing process.

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. In this context, the introduction inside the scaffold of molecularly imprinted polymers (MIPs)-synthetic receptors able to reversibly bind to biomolecules-holds great promise to enhance the scaffold-cell interaction. In this review, we analyze the main strategies that have been used for MIP design and fabrication with a particular focus on biomedical research. Furthermore, to highlight the potential of MIPs for scaffold-based TE, we present recent examples on how MIPs have been used in TE to introduce biophysical cues as well as for drug delivery and sequestering.