Background:Researchers initially proposed the substitution of apoptotic chondrocytes in the superficial cartilage by injecting mesenchymal stem cells (MSCs) intraarticularly. This effect was termed as bio-resurfacing. Little evidence supporting the treatment of osteoarthritis (OA) by the delivery of a MSC suspension exists. The aim of this study was to investigate the effects of injecting allogenic MSCs intraarticularly in a rat OA model and to evaluate the influence of immobility on the effects of this treatment.Methods:We established a rat knee OA model after 4 and 6 weeks and cultured primary bone marrow MSCs. A MSC suspension was injected into the articular space once per week for 3 weeks. A subgroup of knee joints was immobilized for 3 days after each injection, while the remaining joints were nonimmobilized. We used toluidine blue staining, Mankin scores, and TdT-mediated dUTP-biotin nick end labeling staining to evaluate the therapeutic effect of the injections. Comparisons between the therapy side and the control side of the knee joint were made using paired t-test, and comparisons between the immobilized and nonimmobilized subgroups were made using the unpaired t-test. A P value < 0.05 was considered significant.Results:The three investigative approaches revealed less degeneration on the therapy sides of the knee joints than the control sides in both the 4- and 6-week groups (P < 0.05), regardless of immobilization. No significant differences were observed between the immobilized and nonimmobilized subgroups (P > 0.05).Conclusions:Therapy involving the intraarticular injection of allogenic MSCs promoted cartilage repair in a rat arthritis model, and 3-day immobility after injection had little effect on this therapy.
Rationale and objectives
The aim of this study was to investigate the efficacy of magnetic resonance imaging (MRI) with a spoiled gradient-recalled (SPGR) sequence to evaluate early knee cartilage degeneration and the relationship between cartilage and other tissues using a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS).
Materials and methods
Eighty-four patients with knee joint pain were evaluated by X-ray and MRI with an SPGR sequence from June 2015 to December 2016. Joint degeneration was graded by two experienced radiologists using the Kellgren-Lawrence (K-L) grading scale. The modified WORMS was used to evaluate cartilage lesions, bone marrow abnormalities, bone cysts, osteophytes, joint effusion and synovitis. The difference between the WORMS of the SPGR and the T2 sequences evaluated by the Wilcoxon signed-rank test was determined, and the relationships between the WORMS features were evaluated by a Spearman correlation.
Results
The modified WORMS for the cartilage lesion evaluation was significantly higher with the SPGR sequence than with the T2 sequence (
P
< 0.05). The cartilage lesions showed a moderate correlation with osteophytes, synovitis and joint effusion (Rs > 0.40,
P
< 0.05) and weak correlations with bone marrow abnormalities and bone cysts (Rs < 0.4,
P
< 0.05).
Conclusion
The modified WORMS evaluation using MRI with the SPGR sequence was much better than the normal sequence for early knee osteoarthritis (OA). The cartilage lesions are associated with bone marrow abnormalities and the other features of OA.
With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and humandriven vehicles (HVs) to travel on the same roads. Existing singlevehicle planning algorithms on board struggle to handle sophisticated social interactions in the real world. Decisions made by these methods are difficult to understand for humans, raising the risk of crashes and making them unlikely to be applied in practice. Moreover, vehicle flows produced by open-source traffic simulators suffer from being overly conservative and lacking behavioral diversity. We propose a hierarchical multi-vehicle decision-making and planning framework with several advantages. The framework jointly makes decisions for all vehicles within the flow and reacts promptly to the dynamic environment through a high-frequency planning module. The decision module produces interpretable action sequences that can explicitly communicate self-intent to the surrounding HVs. We also present the cooperation factor and trajectory weight set, bringing diversity to autonomous vehicles in traffic at both the social and individual levels. The superiority of our proposed framework is validated through experiments with multiple scenarios, and the diverse behaviors in the generated vehicle trajectories are demonstrated through closed-loop simulations.
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