Bone marrow stem cells (BMSCs) represent a collection of different cell types exhibiting stem cell characteristics but with notable heterogeneity. Among these, Skeletal Stem Cells (SSCs) represent a distinct matrix subgroup within BMSC and demonstrate a specialized capacity to facilitate bone formation, recruit chondrocytes, and contribute to hematopoiesis. SSCs play a pivotal role in orchestrating the functions of skeletal organs. Local ischemia has a significant impact on cell survival and function. We hypothesize that bone ischemia induces alterations in the differentiation potential of SSCs, consequently influencing changes in bone structure. We mechanically dissected tissue from the necrotic segment in the femoral head and more normal appearing areas from the femoral neck of specimens from 5 patients diagnosed with osteonecrosis of the femoral head (ONFH). These tissues were enzymatically broken down into individual cell suspensions. Utilizing fluorescence-activated cell sorting (FACS) based on specific surface markers indicative of human skeletal stem cells (hSSC), namely CD45- CD235a- CD31- TIE2- Podoplanin (PDPN)+ CD146- CD73+ CD164+, we isolated a distinct cell population. Subsequent in vitro evaluations, focusing on clonogenicity, osteogenesis, and chondrogenesis were conducted to assess the functional prowess of these SSCs. Moreover, we introduced BMP2 at a concentration of 50ng/ml to SSCs extracted from necrotic regions to potentially reinstate their osteogenic capabilities. We effectively isolated SSCs from both Necrotic and Non-necrotic Zones. We observed an augmented clonal formation capacity and chondrogenesis ability of SSCs isolated from the necrotic region, accompanied by a significant decline in osteogenic ability ( Ischemia adversely affects the proliferation and function of SSCs, resulting in a diminished osteogenic capacity and an insensitivity to BMP2, ultimately leading to structural alterations in bone tissue.
Osteoarthritis (OA), one of the most common motor system disorders, is a degenerative disease involving progressive joint destruction caused by a variety of factors. At present, OA has become the fourth most common cause of disability in the world. However, the pathogenesis of OA is complex and has not yet been clarified. Long non-coding RNA (lncRNA) refers to a group of RNAs more than 200 nucleotides in length with limited protein-coding potential, which have a wide range of biological functions including regulating transcriptional patterns and protein activity, as well as binding to form endogenous small interference RNAs (siRNAs) and natural microRNA (miRNA) molecular sponges. In recent years, a large number of lncRNAs have been found to be differentially expressed in a variety of pathological processes of OA, including extracellular matrix (ECM) degradation, synovial inflammation, chondrocyte apoptosis, and angiogenesis. Obviously, lncRNAs play important roles in regulating gene expression, maintaining the phenotype of cartilage and synovial cells, and the stability of the intra-articular environment. This article reviews the results of the latest research into the role of lncRNAs in a variety of pathological processes of OA, in order to provide a new direction for the study of OA pathogenesis and a new target for prevention and treatment. Cite this article:
The study aimed to determine whether the microRNA miR21-5p (MiR21) mediates temporomandibular joint osteoarthritis (TMJ-OA) by targeting growth differentiation factor 5 (Gdf5). TMJ-OA was induced in MiR21 knockout (KO) mice and wild-type (WT) mice by a unilateral anterior crossbite (UAC) procedure. Mouse tissues exhibited histopathological changes, as assessed by: Safranin O, toluidine blue, and immunohistochemistry staining; western blotting (WB); and quantitative real-time polymerase chain reaction (RT-qPCR). Mouse condylar chondrocytes were transfected with a series of MiR21 mimic, MiR21 inhibitor, Gdf5 siRNA (si-GDF5), and flag-GDF5 constructs. The effects of MiR-21 and Gdf5 on the expression of OA related molecules were evaluated by immunofluorescence, alcian blue staining, WB, and RT-qPCR.Aims
Methods
To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence.
This study reports the outcomes of a technique of soft-tissue coverage and Chopart amputation for severe crush injuries of the forefoot. Between January 2012 to December 2016, 12 patients (nine male; three female, mean age 38.58 years; 26 to 55) with severe foot crush injury underwent treatment in our institute. All patients were followed-up for at least one year. Their medical records, imaging, visual analogue scale score, walking ability, complications, and functional outcomes one year postoperatively based on the American Orthopedic Foot and Ankle Society (AOFAS) and 36-Item Short-Form Health Survey (SF-36) scores were reviewed.Aims
Patients and Methods