Early degeneration of the meniscus revealed by microbiomechanical alteration in a rabbit anterior cruciate ligament transection model

Preclinical Studies and Clinical Trials on Cell-Based Treatments for Meniscus Regeneration

This study aims at performing a thorough review of cell-based treatment strategies for meniscus regeneration in preclinical and clinical studies. The PubMed, Embase, and Web of Science databases were searched for relevant studies (both preclinical and clinical) published from the time of database construction to December 2022. Data related to cell-based therapies for in situ regeneration of the meniscus were extracted independently by two researchers. Assessment of risk of bias was performed according to the Cochrane Handbook for Systematic Reviews of Interventions. Statistical analyses based on the classification of different treatment strategies were performed. A total of 5730 articles were retrieved, of which 72 preclinical studies and 6 clinical studies were included in this review. Mesenchymal stem cells (MSCs), especially bone marrow MSCs (BMSCs), were the most commonly used cell type. Among preclinical studies, rabbit was the most commonly used animal species, partial meniscectomy was the most commonly adopted injury pattern, and 12 weeks was the most frequently chosen final time point for assessing repair outcomes. A range of natural and synthetic materials were used to aid cell delivery as scaffolds, hydrogels, or other morphologies. In clinical trials, there was large variation in the dose of cells, ranging from 16 × 106 to 150 × 106 cells with an average of 41.52 × 106 cells. The selection of treatment strategy for meniscus repair should be based on the nature of the injury. Cell-based therapies incorporating various "combination" strategies such as co-culture, composite materials, and extra stimulation may offer greater promise than single strategies for effective meniscal tissue regeneration, restoring natural meniscal anisotropy, and eventually achieving clinical translation. Impact Statement This review provides an up-to-date and comprehensive overview of preclinical and clinical studies that tested cell-based treatments for meniscus regeneration. It presents novel perspectives on studies published in the past 30 years, giving consideration to the cell sources and dose selection, delivery methods, extra stimulation, animal models and injury patterns, timing of outcome assessment, and histological and biomechanical outcomes, as well as a summary of findings for individual studies. These unique insights will help to shape future research on the repair of meniscus lesions and inform the clinical translation of new cell-based tissue engineering strategies.

Adaptation of Fibril-Reinforced Poroviscoelastic Properties in Rabbit Collateral Ligaments 8 Weeks After Anterior Cruciate Ligament Transection

Ligaments of the knee provide stability and prevent excessive motions of the joint. Rupture of the anterior cruciate ligament (ACL), a common sports injury, results in an altered loading environment for other tissues in the joint, likely leading to their mechanical adaptation. In the collateral ligaments, the patterns and mechanisms of biomechanical adaptation following ACL transection (ACLT) remain unknown. We aimed to characterize the adaptation of elastic and viscoelastic properties of the lateral and medial collateral ligaments eight weeks after ACLT. Unilateral ACLT was performed in six rabbits, and collateral ligaments were harvested from transected and contralateral knee joints after eight weeks, and from an intact control group (eight knees from four animals). The cross-sectional areas were measured with micro-computed tomography. Stepwise tensile stress-relaxation testing was conducted up to 6% final strain, and the elastic and viscoelastic properties were characterized with a fibril-reinforced poroviscoelastic material model. We found that the cross-sectional area of the collateral ligaments in the ACL transected knees increased, the nonlinear elastic collagen network modulus of the LCL decreased, and the amount of fast relaxation in the MCL decreased. Our results indicate that rupture of the ACL leads to an early adaptation of the elastic and viscoelastic properties of the collagen fibrillar network in the collateral ligaments. These adaptations may be important to consider when evaluating whole knee joint mechanics after ACL rupture, and the results aid in understanding the consequences of ACL rupture on other tissues.

Similarity and difference between aging and puncture-induced intervertebral disc degeneration

The purpose of our study was to investigate the changes in micromorphology and mechanical properties of intervertebral discs degeneration induced by aging and puncture. Normal group (NG), 2 weeks post-puncture degeneration group (PDG) and aging degeneration group (ADG) each included 10 rats. Plain film, magnetic resonance imaging, and histological testing were utilized to assess intervertebral disc degeneration. Atomic force microscope was utilized to analyze the microstructure and elastic modulus of the intervertebral disc, while immunohistochemistry was employed to assess alterations in the cell matrix using collagen I, collagen II, matrix metalloproteinase-3 (MMP-3), and tumour necrosis factor-α (TNF-α). The results showed that the disc height ratio between PDG and ADG decreased. In the PDG and ADG group, histological scores both increased, the gray value of the T2 signal decreased, the proportion of MMP-3 and TNF-positive cells in intervertebral disc tissues was higher (p < 0.05) and the IOD values of COL-2 lower in intervertebral disc tissues (p < 0.05). The elastic modulus of PDG and ADG annulus fibers (AF) increased compared to the NG (p < 0.05); when compared to PDG, the elastic modulus of ADG AF decreased (p < 0.05). The elastic modulus of PDG and ADG collagen increased in the nucleus pulposus (NP, p < 0.05); ADG had a greater AF diameter than NG and PDG (p < 0.05). The results indicated that ADG fiber diameter thickens, and chronic inflammation indicators rise; PDG suffers from severe extracellular matrix loss. The degeneration of the ADG and PDG intervertebral discs is different. The results provide foundation for clinical research.

Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model

Objective

Meniscus tear is a common problem in sports trauma, and its imaging diagnosis mainly relies on MRI. To improve the diagnostic accuracy and efficiency, a deep learning model was employed in this study and the identification efficiency was evaluated.

Methods

Standard knee MRI images from 924 individual patients were used to complete the training, validation and testing processes. Mask regional convolutional neural network (R–CNN) was used to build the deep learning network structure, and ResNet50 was adopted to develop the backbone network. The deep learning model was trained and validated with a dataset containing 504 and 220 patients, respectively. Internal testing was performed based on a dataset of 200 patients, and 180 patients from 8 hospitals were regarded as an external dataset for model validation. Additionally, 40 patients who were diagnosed by the arthroscopic surgery were enrolled as the final test dataset.

Results

After training and validation, the deep learning model effectively recognized healthy and injured menisci. Average precision for the three types of menisci (healthy, torn and degenerated menisci) ranged from 68% to 80%. Diagnostic accuracy for healthy, torn and degenerated menisci was 87.50%, 86.96%, and 84.78%, respectively. Validation results from external dataset demonstrated that the accuracy of diagnosing torn and intact meniscus tear through 3.0T MRI images was higher than 80%, while the accuracy verified by arthroscopic surgery was 87.50%.

Conclusion

Mask R–CNN effectively identified and diagnosed meniscal injuries, especially for tears that occurred in different parts of the meniscus. The recognition ability was admirable, and the diagnostic accuracy could be further improved with increased training sample size. Therefore, this deep learning model showed great potential in diagnosing meniscus injuries.

Translational potential of this article

Deep learning model exerted unique effect in terms of reducing doctors’ workload and improving diagnostic accuracy. Injured and healthy menisci could be more accurately identified and classified based on training and learning datasets. This model could also distinguish torn from degenerated menisci, making it an effective tool for MRI-assisted diagnosis of meniscus injuries in clinical practice.