Mesenchymal stem cells (MSCs) have the ability to differentiate into osteoblasts, chondroblasts, adipocytes, and even myoblasts. Most studies have focused on finding MSCs in different parts of the body for medical treatment. Every joint structure, including bone, joint fat, articular cartilage, and synovium, potentially contains resident MSCs. Recently, a progenitor cell population has been found in synovial fluid and showed similarities with both bone marrow and synovial membrane MSCs. Synovial fluid MSCs have been studied in healthy persons and osteoarthritic patients in order to explore its potential for treatment of some orthopedic disorders. Here, we briefly review the current knowledge on synovial fluid MSCs, their origin, relation to some orthopedic diseases, and future applications.

Connective tissue growth factor (CTGF)/CCN family member 2 (CCN2) is a CCN family member of matricellular signaling modulators. It has been shown that CCN2/CTGF mediates cell adhesion, aggregation and migration in a large variety of cell types, including vascular endothelial cells, fibroblasts, epithelial cells, aortic smooth muscle and also pluripotent stem cells. Others matricellular proteins are capable of interacting with CCN2/CTGF to mediate its function. Cell migration is a key feature for tumor cell invasion and metastasis. CCN2/CTGF seems to be a prognostic marker for cancer. In addition, here we intend to discuss recent discoveries and a new strategy to develop therapies against CCN2/CTGF, in order to treat cancer metastasis.

Objective: This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. Methods and materials: This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature’s selection and model’s training. The radiomic features were extracted from CT’s image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model. Results: The best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters. Conclusions: Machine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. Advances in knowledge: Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.

Background: The repair of large bone defects is a major orthopedic challenge because autologous bone grafts are not available in large amounts and because harvesting is often associated with donor-site morbidity. Considering that bone marrow stromal cells (BMSC) are responsible for the maintenance of bone turnover throughout life, we investigated bone repair at a site of a critically sized segmental defect in sheep tibia treated with BMSCs loaded onto allografts. The defect was created in the mid-portion of the tibial diaphysis of eight adult sheep, and the sheep were treated with ex-vivo expanded autologous BMSCs isolated from marrow aspirates and loaded onto cortical allografts (n = 4). The treated sheep were compared with control sheep that had been treated with cell-free allografts (n = 4) obtained from donors of the same breed as the receptor sheep. Results: The healing response was monitored by radiographs monthly and by computed tomography and histology at six, ten, fourteen, and eighteen weeks after surgery. For the cell-loaded allografts, union was established more rapidly at the interface between the host bone and the allograft, and the healing process was more conspicuous. Remodeling of the allograft was complete at 18 weeks in the cell-treated animals. Histologically, the marrow cavity was reestablished, with intertrabecular spaces being filled with adipose marrow and with evidence of focal hematopoiesis. Conclusions: Allografts cellularized with AOCs (allografts of osteoprogenitor cells) can generate great clinical outcomes to noncellularized allografts to consolidate, reshape, structurally and morphologically reconstruct bone and bone marrow in a relatively short period of time. These features make this strategy very attractive for clinical use in orthopedic bioengineering.

Tendinopathy pathogenesis is associated with inflammation. Regulatory T (Treg) cells contribute to early tissue repair through an anti-inflammatory action, with the forkhead box P3 (FOXP3) transcription factor being essential for Treg function, and the FC-receptor-like 3 (FCRL3) possibly negatively regulating Treg function. FCRL3 –169T>C and FOXP3 –2383C>T polymorphisms are located near elements that regulate respective genes expression, thus it was deemed relevant to evaluate these polymorphisms as risk factors for tendinopathy development in athletes. This case-control study included 271 volleyball athletes (146 tendinopathy cases and 125 controls) recruited from the Brazilian Volleyball Federation. Genotyping analyses were performed using TaqMan assays, and the association of the polymorphisms with tendinopathy evaluated by multivariate logistic regression. Tendinopathy frequency was 63% patellar, 22% rotator cuff and 15% Achilles tendons respectively. Tendinopathy was more common in men (OR = 2.87; 95% CI = 1.67–4.93). Higher age (OR = 8.75; 95% CI = 4.33–17.69) and more years of volleyball practice (OR = 8.38; 95% CI = 3.56–19.73) were risk factors for tendinopathy. The FCRL3 –169T>C frequency was significantly different between cases and controls. After adjustment for potential confounding factors, the FCRL3 –169C polymorphism was associated with increased tendinopathy risk (OR = 1.44; 95% CI = 1.02–2.04), either considering athletes playing with tendon pain (OR = 1.98; 95% CI = 1.30–3.01) or unable to train due to pain (OR = 1.89; 95% CI = 1.01–3.53). The combined variant genotypes, FCRL3 –169TC or –169CC and FOXP3 –2383CT or –2383TT, were associated with an increased risk of tendinopathy among athletes with tendon pain (OR = 2.24; 95% CI: 1.14–4.40 and OR = 2.60; 95% CI: 1.11–6.10). The combined analysis of FCRL3 –169T>C and FOXP3 –2383C>T suggests a gene-gene interaction in the susceptibility to tendinopathy. FCRL3 –169C allele may increase the risk of developing tendinopathy, and together with knowledge of potential risk factors (age, gender and years playing) could be used to personalize elite athletes’ training or treatment in combination with other approaches, with the aim of minimizing pathology development risk.