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Histomorphometry is an important technique in the evaluation of non‐traumatic osteonecrosis of the femoral head (ONFH). Quantification of empty lacunae and pyknotic cells on histological images is the most reliable measure of ONFH pathology, yet it is time and manpower consuming. This study focused on the application of artificial intelligence (AI) technology to tissue image evaluation. The aim of this study is to establish an automated cell counting platform using YOLOv8 as an object detection model on ONFH tissue images and to evaluate and validate its accuracy. From 30 ONFH model rabbits, 270 tissue images were prepared; based on evaluations by three researchers, ground truth labels were created to classify each cell in the image into two classes (osteocytes and empty lacunae) or three classes (osteocytes, pyknotic cells, and empty lacunae). Two and three classes were then annotated on each image. Transfer learning based on annotated data (80% for training and 20% for validation) was performed using YOLOv8n and YOLOv8x with different parameters. To evaluate the detection accuracy of the training model, the mean average precision (mAP (50)) and precision‐recall curve were identified. In addition, the reliability of cell counting by YOLOv8 relative to manual cell counting was evaluated by linear regression analysis using five histological images unused in previous experiments. The mAP (50) for the detection of empty lacunae was 0.868 for the YOLOv8n and 0.883 for the YOLOv8x. The mAP (50) for the three classes was 0.735 for the YOLOv8n model and 0.750 for the YOLOv8x model. The quantification of empty lacunae by automated cell counting obtained in the learning was highly correlated with the manual counting data. The development of an AI‐applied automated cell counting platform will significantly reduce the time and effort of manual cell counting in histological analysis. [ABSTRACT FROM AUTHOR]

Approximately 5%–10% of fractures go on to delayed healing and nonunion, posing significant clinical, economic, and social challenges. Current treatment methods involving open bone harvesting and grafting are associated with considerable pain and potential morbidity at the donor site. Hence, there is growing interest in minimally invasive approaches such as bone marrow aspirate concentrate (BMAC), which contains mesenchymal stromal cells (MSCs), macrophages (Mφ), and T cells. However, the use of cultured or activated cells for treatment is not yet FDA‐approved in the United States, necessitating further exploration of optimal cell types and proportions for effective bone formation. As our understanding of osteoimmunology advances, it has become apparent that factors from anti‐inflammatory Mφ (M2) promote bone formation by MSCs. Additionally, M2 Mφ promote T helper 2 (Th2) cells and Treg cells, both of which enhance bone formation. In this study, we investigated the interactions among MSCs, Mφ, and T cells in bone formation and explored the potential of subsets of BMAC. Coculture experiments were conducted using primary MSCs, Mφ, and CD4+ T cells at specific ratios. Our results indicate that nonactivated T cells had no direct influence on osteogenesis by MSCs, while coculturing MSCs with Mφ and T cells at a ratio of 1:5:10 positively impacted bone formation. Furthermore, higher numbers of T cells led to increased M2 polarization and a higher proportion of Th2 cells in the early stages of coculture. These findings suggest the potential for enhancing bone formation by adjusting immune and mesenchymal cell ratios in BMAC. By understanding the interactions and effects of immune cells on bone formation, we can develop more effective strategies and protocols for treating bone defects and nonunions. Further studies are needed to investigate these interactions in vivo and explore additional factors influencing MSC‐based therapies. [ABSTRACT FROM AUTHOR]

This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long‐term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X‐ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time‐consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X‐ray and MRI from 102 control hips and 104 ONFH‐affected hips at Association Research Circulation Osseous (ARCO) Stage II and IIIa. We employed transfer learning with the YOLOv8 model for object detection, using 80% of the data for training and 20% for validation, then assessed detection accuracy through mean average precision (mAP) and a precision‐recall curve. Additionally, AI generated synthetic MRI (sMRI) from X‐ray images using a Generative Adversarial Network (GAN) and evaluated their similarity to original MRI. Results showed that the mAP for ONFH detection was 0.923 for the YOLOv8n model and 0.951 for YOLOv8x. The GAN‐generated sMRI exhibited lower image quality compared with originals but maintained potential for lesion assessment. Intrarater reliability among evaluators was high. The findings indicate that AI techniques, particularly YOLOv8 for object detection and GAN for image generation, can effectively assist in ONFH screening, despite some limitations in the generated MRI quality. [ABSTRACT FROM AUTHOR]

Patient-specific induced pluripotent stem cells (iPSCs)-based modeling potentially recapitulates the pathology and mechanisms more faithfully than cell line models and general animal models. Utilizing iPSC-derived cells for personalized bone formation research offers a powerful tool to better understand the role of individual differences in bone health and disease and provide more precise information for personalized bone regeneration therapies. Here we generated iPSC-derived mesenchymal progenitor cells (iMPCs), endothelial cells (iECs), and macrophages (iMØ), from different donors. Cellular markers, pluripotency properties, and immune regulatory properties were investigated. To replicate bone regeneration, we utilize different iPSC models and co-cultured three distinct cell types (iMPCs, iECs, and iMØ) in a 3D in vitro model derived from the same donor. Cells from different donors exhibited patient-specific characteristics and different regenerative capacities. Our study suggests that cells differentiated from iPSCs can be used to anticipate the effectiveness of cell-based therapies for personalized tissue regeneration.

Background: Corticosteroids provide well-established therapeutic benefits; however, they are also accompanied by adverse effects on bone. Metformin is a widely used medication for managing type 2 diabetes mellitus. Recent studies have highlighted additional therapeutic benefits of metformin, particularly concerning bone health and oxidative stress., Objective: This research investigates the effects of prednisolone on cellular metabolic functions and bone formation using a 3D in vitro model. Then, we demonstrate the potential therapeutic effects of metformin on oxidative stress and the formation of calcified matrix due to corticosteroids., Methods: Human mesenchymal stem cells (MSCs) and macrophages were cultured in a 3D GelMA scaffold and stimulated with prednisolone, with and without metformin. The adverse effects of prednisolone and metformin's therapeutic effect(s) were assessed by analyzing cell viability, osteogenesis markers, bone mineralization, and inflammatory markers. Oxidative stress was measured by evaluating reactive oxygen species (ROS) levels and ATP production., Results: Prednisolone exhibited cytotoxic effects, reducing the viability of MSCs and macrophages. Lower osteogenesis potential was also detected in the MSC group. Metformin positively affected cell functions, including enhanced osteoblast activity and increased bone mineralization. Furthermore, metformin effectively reduced oxidative stress, as evidenced by decreased ROS levels and increased ATP production. These findings indicate that metformin protects against oxidative damage, thus supporting osteogenesis., Conclusion: Metformin exhibits promising therapeutic potential beyond its role in diabetes management. The capacity to alleviate oxidative stress highlights the potential of metformin in supporting bone formation in inflammatory environments., Competing Interests: The authors declare no conflict of interest., (© 2024 Cekuc et al.)

Bone regeneration and repair are crucial to ambulation and quality of life. Factors such as poor general health, serious medical comorbidities, chronic inflammation, and ageing can lead to delayed healing and nonunion of fractures, and persistent bone defects. Bioengineering strategies to heal bone often involve grafting of autologous bone marrow aspirate concentrate (BMAC) or mesenchymal stem cells (MSCs) with biocompatible scaffolds. While BMAC shows promise, variability in its efficacy exists due to discrepancies in MSC concentration and robustness, and immune cell composition. Understanding the mechanisms by which macrophages and lymphocytes - the main cellular components in BMAC - interact with MSCs could suggest novel strategies to enhance bone healing. Macrophages are polarized into pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes, and influence cell metabolism and tissue regeneration via the secretion of cytokines and other factors. T cells, especially helper T1 (Th1) and Th17, promote inflammation and osteoclastogenesis, whereas Th2 and regulatory T (Treg) cells have anti-inflammatory pro-reconstructive effects, thereby supporting osteogenesis. Crosstalk among macrophages, T cells, and MSCs affects the bone microenvironment and regulates the local immune response. Manipulating the proportion and interactions of these cells presents an opportunity to alter the local regenerative capacity of bone, which potentially could enhance clinical outcomes., Competing Interests: S. B. Goodman reports partial funding from his position as Robert L. and Mary Ellenburg Professor of Surgery, Stanford University, related to this study., (© 2024 Murayama et al.)