The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA. Cartilage samples from patients with KBD (n = 10) and patients with OA (n = 10) were collected during total knee arthroplasty surgery. An untargeted metabolomics approach using liquid chromatography coupled with mass spectrometry (LC-MS) was conducted to investigate the metabolomics profiles of KBD and OA. LC-MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with R software. The online Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the metabolites by matching the exact molecular mass data of samples with those from the database.Aims
Methods
Mendelian randomization (MR) is considered to overcome the bias of observational studies, but there is no current meta-analysis of MR studies on rheumatoid arthritis (RA). The purpose of this study was to summarize the relationship between potential pathogenic factors and RA risk based on existing MR studies. PubMed, Web of Science, and Embase were searched for MR studies on influencing factors in relation to RA up to October 2022. Meta-analyses of MR studies assessing correlations between various potential pathogenic factors and RA were conducted. Random-effect and fixed-effect models were used to synthesize the odds ratios of various pathogenic factors and RA. The quality of the study was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines.Aims
Methods
MicroRNA-183 ( Clinical samples were collected from patients with OA, and a mouse model of OA pain was constructed by surgically induced destabilization of the medial meniscus (DMM). Reverse transcription quantitative polymerase chain reaction was employed to measure the expression of miR-183, transforming growth factor α (TGFα), C-C motif chemokine ligand 2 (Aims
Methods
Osteoarthritis (OA) is characterized by persistent destruction of articular cartilage. It has been found that microRNAs (miRNAs) are closely related to the occurrence and development of OA. The purpose of the present study was to investigate the mechanism of miR-486 in the development and progression of OA. The expression levels of miR-486 in cartilage were determined by quantitative real-time polymerase chain reaction (qRT-PCR). The expression of collagen, type II, alpha 1 (COL2A1), aggrecan (ACAN), matrix metalloproteinase (MMP)-13, and a disintegrin and metalloproteinase with thrombospondin motifs-4 (ADAMTS4) in SW1353 cells at both messenger RNA (mRNA) and protein levels was determined by qRT-PCR, western blot, and enzyme-linked immunosorbent assay (ELISA). Double luciferase reporter gene assay, qRT-PCR, and western blot assay were used to determine whether silencing information regulator 6 (SIRT6) was involved in miR-486 induction of chondrocyte-like cells to a more catabolic phenotype.Aims
Methods
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
Aims. The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Methods. Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA. Results. PLS-DA produced a streamlined biomarker model with excellent
