Human Adipose- and Amnion-Derived Mesenchymal Stromal Cells Similarly Mitigate Osteoarthritis Progression in the Dunkin Hartley Guinea Pig

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.

Protecting the regenerative environment: selecting the optimal delivery vehicle for cartilage repair-a narrative review

Focal cartilage defects are common in youth and older adults, cause significant morbidity and constitute a major risk factor for developing osteoarthritis (OA). OA is the most common musculoskeletal (MSK) disease worldwide, resulting in pain, stiffness, loss of function, and is currently irreversible. Research into the optimal regenerative approach and methods in the setting of either focal cartilage defects and/or OA holds to the ideal of resolving both diseases. The two fundamentals required for cartilage regenerative treatment are 1) the biological element contributing to the regeneration (e.g., direct application of stem cells, or of an exogenous secretome), and 2) the vehicle by which the biological element is suspended and delivered. The vehicle provides support to the regenerative process by providing a protective environment, a structure that allows cell adherence and migration, and a source of growth and regenerative factors that can activate and sustain regeneration. Models of cartilage diseases include osteochondral defect (OCD) (which usually involve one focal lesion), or OA (which involves a more diffuse articular cartilage loss). Given the differing nature of these models, the optimal regenerative strategy to treat different cartilage diseases may not be universal. This could potentially impact the translatability of a successful approach in one condition to that of the other. An analogy would be the repair of a pothole (OCD) versus repaving the entire road (OA). In this narrative review, we explore the existing literature evaluating cartilage regeneration approaches for OCD and OA in animal then in human studies and the vehicles used for each of these two conditions. We then highlight strengths and challenges faced by the different approaches presented and discuss what might constitute the optimal cartilage regenerative delivery vehicle for clinical cartilage regeneration.

Exploring Orthopedic Stem-Cell Approaches for Osteoarthritis Management: Current Trends and Future Horizons

PURPOSE OF REVIEW

Osteoarthritis (OA) is a prevalent and debilitating condition characterized by joint degeneration and pain. Current treatment options aim to alleviate symptoms and slow disease progression but lack curative potential. Stem cell therapies have emerged as a promising alternative. This article explores the epidemiology, pathophysiology, clinical manifestations of hip and knee OA, and the evolving role of stem cell therapies in their treatment.

RECENT FINDINGS

The global prevalence of OA, with knee OA being the most common form, has fueled the demand for stem cell therapies. Despite limited robust evidence supporting their efficacy, clinical trials investigating stem-cell treatments for OA have reported encouraging radiological and clinical improvements. Stem cell therapies offer potential disease-modifying benefits through immunomodulatory actions, growth factor secretion, and chondrogenic capabilities. Adipose-derived mesenchymal stem cells (ADMSCs) have shown promise in clinical trials for OA treatment, offering potential pain relief and functional improvement. ADMSCs possess advantages such as accessibility and a favorable safety profile, making them a viable option for OA management. Although other stem-cell types, including human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs), have been used in OA treatment, ADMSCs have demonstrated superior outcomes. By providing a comprehensive overview of the evolving landscape of stem cell therapies for hip and knee OA, this article highlights the potential of stem-cell treatments to address the limitations of current therapies. However, further research is required to establish their long-term efficacy, identify optimal stem-cell types, and develop standardized protocols.