This study aimed, through bioinformatics analysis and in vitro experiment validation, to identify the key extracellular proteins of intervertebral disc degeneration (IDD). The gene expression profile of GSE23130 was downloaded from the Gene Expression Omnibus (GEO) database. Extracellular protein-differentially expressed genes (EP-DEGs) were screened by protein annotation databases, and we used Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the functions and pathways of EP-DEGs. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and identify hub EP-DEGs. NetworkAnalyst was used to analyze transcription factors (TFs) and microRNAs (miRNAs) that regulate hub EP-DEGs. A search of the Drug Signatures Database (DSigDB) for hub EP-DEGs revealed multiple drug molecules and drug-target interactions.Aims
Methods
This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed.Aims
Methods
This study aimed to explore the role of small colony variants (SCVs) of A PJI diagnosis was made according to the MusculoSkeletal Infection Society (MSIS) for PJI. Bone and tissue samples were collected intraoperatively and the intracellular invasion and intraosseous colonization were detected. Transcriptomics of PJI samples were analyzed and verified by polymerase chain reaction (PCR).Aims
Methods
Osteoarthritis (OA) is a prevalent joint disorder with inflammatory response and cartilage deterioration as its main features. Dihydrocaffeic acid (DHCA), a bioactive component extracted from natural plant ( In vitro, interleukin-1 beta (IL-1β) was used to establish the mice OA chondrocytes. Cell counting kit-8 evaluated chondrocyte viability. Western blotting analyzed the expression levels of collagen II, aggrecan, SOX9, inducible nitric oxide synthase (iNOS), IL-6, matrix metalloproteinases (MMPs: MMP1, MMP3, and MMP13), and signalling molecules associated with nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways. Immunofluorescence analysis assessed the expression of aggrecan, collagen II, MMP13, and p-P65. In vivo, a destabilized medial meniscus (DMM) surgery was used to induce mice OA knee joints. After injection of DHCA or a vehicle into the injured joints, histological staining gauged the severity of cartilage damage.Aims
Methods
Rotator cuff tear (RCT) is the leading cause of shoulder pain, primarily associated with age-related tendon degeneration. This study aimed to elucidate the potential differential gene expressions in tendons across different age groups, and to investigate their roles in tendon degeneration. Linear regression and differential expression (DE) analyses were performed on two transcriptome profiling datasets of torn supraspinatus tendons to identify age-related genes. Subsequent functional analyses were conducted on these candidate genes to explore their potential roles in tendon ageing. Additionally, a secondary DE analysis was performed on candidate genes by comparing their expressions between lesioned and normal tendons to explore their correlations with RCTs.Aims
Methods
Post-traumatic osteoarthritis (PTOA) is a subset of osteoarthritis (OA). The gut microbiome is shown to be involved in OA. However, the effect of exercise on gut microbiome in PTOA remains elusive. A total of 18 eight-week Sprague-Dawley rats were assigned into three groups: Sham/sedentary (Sham/Sed), PTOA/sedentary (PTOA/Sed), and PTOA/treadmill-walking (PTOA/TW). PTOA model was induced by transection of the anterior cruciate ligament (ACLT) and the destabilization of the medial meniscus (DMM). Treadmill-walking (15 m/min, 30 min/d, five days/week for eight weeks) was employed in the PTOA/TW group. The response of cartilage, subchondral bone, serology, and gut microbiome and their correlations were assessed.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
The aim of this study was to identify key pathological genes in osteoarthritis (OA). We searched and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) of joint synovial tissues from OA and normal individuals. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses were used to assess the function of identified DEGs. The protein-protein interaction (PPI) network and transcriptional factors (TFs) regulatory network were used to further explore the function of identified DEGs. The quantitative real-time polymerase chain reaction (qRT-PCR) was applied to validate the result of bioinformatics analysis. Electronic validation was performed to verify the expression of selected DEGs. The diagnosis value of identified DEGs was accessed by receiver operating characteristic (ROC) analysis.Objectives
Methods
Osteoporosis is a chronic disease. The aim of this study was to identify key genes in osteoporosis. Microarray data sets GSE56815 and GSE56814, comprising 67 osteoporosis blood samples and 62 control blood samples, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in osteoporosis using Limma package (3.2.1) and Meta-MA packages. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify biological functions. Furthermore, the transcriptional regulatory network was established between the top 20 DEGs and transcriptional factors using the UCSC ENCODE Genome Browser. Receiver operating characteristic (ROC) analysis was applied to investigate the diagnostic value of several DEGs.Objectives
Methods