Computer vision-aided bioprinting for bone research

Distribution and propagation of stress and strain in cube honeycombs as trabecular bone substitutes: Finite element model analysis

For designing trabecular (Tb) bone substitutes suffering from osteoporosis, finite element model (FEM) simulations were conducted on honeycombs (HCs) of 8 × 8 × 1 (2D) and 8 × 8 × 8 (3D) assemblies of cube cellular units consisting of 0.9 mm long Nylon® 66 (PA, Young's modulus E: 2.83 GPa) and polyethylene (PE, E: 1.1 GPa) right square prisms. Osteoporotic damage to the Tb bone was simulated by removing the inner vertical struts (pillars; the number of removed pillars: Δn ≤ 300) and by thinning the strut (thickness, d: 0.4-0.1 mm), while the six facade lattices were kept flawless. Uniform and uniaxial compressive loads on the HCs induced elastic deformation of the struts. The pillars held almost all the load, while the horizontal struts (beams) shared little. E for PA 3D HCs of all d smoothly decreased with Δn. PA 3D HCs of 0.2 mm struts deserved to be the substitutes for Tb bone, while PE 3D HCs of 0.05 mm struts were only for the Tb bone of the poorest bone quality. For the PA 3D HCs, the maximum von Mises stress (σM) first rapidly increased with Δn and showed a break at Δñ50, then gradually approached the yield stress of PA (50 MPa). Moreover, small portions of the stress were transferred from the façade pillars to the adjacent inner beams, especially those near the lost-pillar sites, denoted as X defects. The floor beams of thinner struts associated with the X-defects were lifted, and similar lifting effects in smaller amounts were propagated to the other floors. The 3DHCs of the thicker struts showed no such flexural deformations. The concept of force percolation through the remaining struts was proposed to interpret those mechanical behaviors of the HCs.

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Precision improvement of robotic bioprinting via vision-based tool path compensation

Robotic 3D bioprinting is a rapidly advancing technology with applications in organ fabrication, tissue restoration, and pharmaceutical testing. While the stepwise generation of organs characterizes bioprinting, challenges such as non-linear material behavior, layer shifting, and trajectory tracking are common in freeform reversible embedding of suspended hydrogels (FRESH) bioprinting, leading to imperfections in complex organ construction. To overcome these limitations, we propose a computer vision-based strategy to identify discrepancies between printed filaments and the reference robot path. Employing error compensation techniques, we generate an adjusted reference path, enhancing robotic 3D bioprinting by adapting the robot path based on vision system data. Experimental assessments confirm the reliability and agility of our vision-based robotic 3D bioprinting approach, showcasing precision in fabricating human blood vessel segments through case studies. Significantly, it minimizes the printing layer width disparity to just 0.15 mm compared to the 0.6 mm in traditional methods, and it decreases the average error for curved filaments to 7.0 mm2 from the previous 12.7 mm2 in conventional printing. While these results underscore the significant potential of our innovation in creating precise biomimetic constructs, further investigation is necessary to tackle challenges such as accurately distinguishing closely stacked layers using a vision system, especially under varying lighting conditions. These limitations, coupled with issues of computational complexity and scalability in larger-scale bioprinting, emphasize the importance of enhancing the reliability of the vision-based approach across various conditions. Nonetheless, our innovation demonstrates substantial promise in creating precise biomimetic constructs and paves the way for future advancements in vision-guided robotic bioprinting, including the integration of multi-material printing techniques to enhance versatility.

Biomaterials / bioinks and extrusion bioprinting

Bioinks are formulations of biomaterials and living cells, sometimes with growth factors or other biomolecules, while extrusion bioprinting is an emerging technique to apply or deposit these bioinks or biomaterial solutions to create three-dimensional (3D) constructs with architectures and mechanical/biological properties that mimic those of native human tissue or organs. Printed constructs have found wide applications in tissue engineering for repairing or treating tissue/organ injuries, as well as in vitro tissue modelling for testing or validating newly developed therapeutics and vaccines prior to their use in humans. Successful printing of constructs and their subsequent applications rely on the properties of the formulated bioinks, including the rheological, mechanical, and biological properties, as well as the printing process. This article critically reviews the latest developments in bioinks and biomaterial solutions for extrusion bioprinting, focusing on bioink synthesis and characterization, as well as the influence of bioink properties on the printing process. Key issues and challenges are also discussed along with recommendations for future research.

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis

Background

The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis.

Methods

We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations.

Results

We discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status.

Conclusion

This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis.

Bone-on-a-Chip: Biomimetic Models Based on Microfluidic Technologies for Biomedical Applications

With the increasing importance of preclinical evaluation of newly developed drugs or treatments, in vitro organ or disease models are necessary. Although various organ-specific on-chip (organ-on-a-chip, or OOC) systems have been developed as emerging in vitro models, bone-on-a-chip (BOC) systems that recapitulate the bone microenvironment have been less developed or reviewed compared with other OOCs. The bone is one of the most dynamic organs and undergoes continuous remodeling throughout its lifetime. The aging population is growing worldwide, and healthcare costs are rising rapidly. Since in vitro BOC models that recapitulate native bone niches and pathological features can be important for studying the underlying mechanism of orthopedic diseases and predicting drug responses in preclinical trials instead of in animals, the development of biomimetic BOCs with high efficiency and fidelity will be accelerated further. Here, we review recently engineered BOCs developed using various microfluidic technologies and investigate their use to model the bone microenvironment. We have also explored various biomimetic strategies based on biological, geometrical, and biomechanical cues for biomedical applications of BOCs. Finally, we addressed the limitations and challenging issues of current BOCs that should be overcome to obtain more acceptable BOCs in the biomedical and pharmaceutical industries.

Bio-high entropy alloys: Progress, challenges, and opportunities

With the continuous progress and development in biomedicine, metallic biomedical materials have attracted significant attention from researchers. Due to the low compatibility of traditional metal implant materials with the human body, it is urgent to develop new biomaterials with excellent mechanical properties and appropriate biocompatibility to solve the adverse reactions caused by long-term implantation. High entropy alloys (HEAs) are nearly equimolar alloys of five or more elements, with huge compositional design space and excellent mechanical properties. In contrast, biological high-entropy alloys (Bio-HEAs) are expected to be a new bio-alloy for biomedicine due to their excellent biocompatibility and tunable mechanical properties. This review summarizes the composition system of Bio-HEAs in recent years, introduces their biocompatibility and mechanical properties of human bone adaptation, and finally puts forward the following suggestions for the development direction of Bio-HEAs: to improve the theory and simulation studies of Bio-HEAs composition design, to quantify the influence of composition, process, post-treatment on the performance of Bio-HEAs, to focus on the loss of Bio-HEAs under actual service conditions, and it is hoped that the clinical application of the new medical alloy Bio-HEAs can be realized as soon as possible.

Medical high-entropy alloy: Outstanding mechanical properties and superb biological compatibility

Medical metal implants are required to have excellent mechanical properties and high biocompatibility to handle the complex human environment, which is a challenge that has always existed for traditional medical metal materials. Compared to traditional medical alloys, high entropy alloys (HEAs) have a higher design freedom to allow them to carry more medical abilities to suit the human service environment, such as low elastic modulus, high biocompatible elements, potential shape memory capability. In recent years, many studies have pointed out that bio-HEAs, as an emerging medical alloy, has reached or even surpassed traditional medical alloys in various medical properties. In this review, we summarized the recent reports on novel bio-HEAs for medical implants and divide them into two groups according the properties, namely mechanical properties and biocompatibility. These new bio-HEAs are considered hallmarks of a historic shift representative of a new medical revolution.