Background. Immunological mechanisms play a vital role in the pathogenesis of knee osteoarthritis (KOA). Moreover, the immune phenotype is a relevant prognostic factor in various immune‐related diseases. In this study, we used CIBERSORT for deconvolution of global gene expression data to define the immune cell landscape of different structures of knee in osteoarthritis. Methods and Findings. By applying CIBERSORT, we assessed the relative proportions of immune cells in 76 samples of knee cartilage, 146 samples of knee synovial tissue, 40 samples of meniscus, and 50 samples of knee subchondral bone. Enumeration and activation status of 22 immune cell subtypes were provided by the obtained immune cell profiles. In synovial tissues, the differences in proportions of plasma cells, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and eosinophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). The area under the curve was relatively large in resting mast cells, dendritic cells, and M2 macrophages in receiver operating characteristic analyses. In subchondral bones, the differences in proportions of resting master cells and neutrophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). In subchondral bones, the proportions of immune cells, from the principle component analyses, displayed distinct group‐bias clustering. Resting mast cells and T cell CD8 were the major component of first component. Moreover, we revealed the potential interaction between immune cells. There was almost no infiltration of immune cells in the meniscus and cartilage of the knee joint. Conclusions. The immune cell composition in KOA differed substantially from that of healthy joint tissue, while it also differed in different anatomical structures of the knee. Meanwhile, activated mast cells were mainly associated with high immune cell infiltration in OA. Furthermore, we speculate M2 macrophages in synovium and mast cells in subchondral bone may play an important role in the pathogenesis of OA.

Knee osteoarthritis (KOA) is one of the most frequently encountered diseases in the orthopedic department. Existing non-surgical treatments have a limited effect on the repair of cartilage and on bone regeneration. Platelet-rich plasma (PRP) is an autologous bioactive substance that can repair cartilage injury and accelerate bone regeneration effectively. However, reporting of PRP preparation protocols in clinical studies is highly inconsistent, with the majority of studies providing insufficient information to allow the protocol to be reproduced. We describe a repeatable method of preparing PRP visually, the treatment of KOA using PRP intra-articular injection, and methods of evaluating the outcome. PRP was prepared using manual double centrifugation. The PRP layer was extracted from peripheral blood and used for knee joint cavity injection. Evaluations included assessments of blood platelet concentrations and clinical outcomes. Preparation of PRP by manual centrifugation requires less apparatus and is less costly than plasma filtration or centrifugation using equipment. The centrifugation time of our double centrifugation method was 6 and 5 minutes for the respective centrifugations at forces of 800 and 1400 x g, respectively, to allow for the consistent preparation of standardized PRP. However, a manual method is susceptible to operator error, and PRP batch preparation is not available. Intra-articular injection of PRP proved to be an effective treatment for knee osteoarthritis. The entire treatment procedure took less than 30 minutes, the blood platelet concentration of PRP could be standardized, and treatment was proven to be effective when evaluated by follow-up.

The application of thromboelastography in risk stratification for selective thromboembolism prophylaxis after total joint arthroplasty in Chinese: a randomized controlled trial Ziming Chen, Yuanchen Ma, Qingtian Li, Zhantao Deng, Qiujian Zheng

Background: A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA.Methods: We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation.Results: We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good (R² = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation.Conclusions: This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.