[Objective]To investigate the prophylactic effects of intra-articular injection of salvia miltiorrhiza on joint adhesion. [Methods]Twenty-four Japanese male rabbits were randomly divided into 3 groups.All right hind knees were made in this way: the auterior wall of suprapatellar bursae was scraped, the posterior wall of suprapatellar bursae and the infrapatellar fat pad were cut off. Each knee was injected with physiological saline 0.3 ml for group A, sodium hyaluronate injection 0.3 ml for group B, and salvia miltiorrhiza injection 0.3 ml for group C. All right hind knees were immobilized in the extension position with a long leg plaster cast for 4 weeks, and injected weekly.[Results]After 4 weeks, the joint movement range in group B or C was larger than that in group A (P<0.01), and there was no significant difference (P>0.05)compared to group B and group C. The scoring of joint adhesions in group B or C was smaller than that in group A (P<0.01), and there was no significant difference(P>0.05)compared to group B and group C. The scoring of histological changed of the synovial membrane in group B or C was smaller than that in group A (P<0.01), and there was no significant difference(P>0.05)compared to group B and group C.Total collagens content of the synovial membrane in group B or C was smaller than that in group A (P<0.01), while that of group C was less than in group B(P<0.01). [Conclusion]Similarly to sodium hyaluronate, salvia miltiorrhiza can effectively prevent joint adhesions.

BACKGROUND:COX-2 gene actual y exists in the joint fibroblast-like synoviocytes, it affects osteoarthritis occurrence and development. Understanding the differences of COX-2 gene expression levels at different stages of osteoarthritis synoviocytes has important theoretical significance for the occurrence and development of osteoarthritis, as wel as the role of synoviocytes in this process.
OBJECTIVE:To analyze the difference of COX-2 mRNA at different stages of osteoarthritis fibroblast-like synoviocytes.
METHODS:Synovial membrane from 44 osteoarthritis patients and 12 normal cases were selected. Primary cells were cultured to passage 4 fibroblast-like synoviocytes for the use in the experiment. COX-2 mRNA expression in osteoarthritis fibroblast-like synoviocytes and normal fibroblast-like synoviocytes was detected using real-time fluorescence quantitative RT-PCR. The relative quantitative analysis was performed using 2-ΔΔCt method.
RESULTS AND CONCLUSION:Expression of COX-2 mRNA in osteoarthritis fibroblast-like synoviocytes was significantly higher than that in normal fibroblast-like synoviocytes (P<0.05). The expression levels reached a peak at early osteoarthritis group, with significant differences compared with middle and late osteoarthritis groups (P<0.05). There was no significant difference between middle and later osteoarthritis groups (P>0.05). COX-2 mRNA might be important biological marker for the inflammation in osteoarthritis, and mainly plays a role in early osteoarthritis stage.

BACKGROUND:Locking compression plate and dynamic hip screw are the two major extramedul ary fixations for the femoral intertrochanteric fractures, however, the comparison of the clinical efficacy between two methods is stil controversial. OBJECTIVE:To systematical y evaluate the clinical efficacy of locking compression plate versus dynamic hip screw in the treatment of femoral intertrochanteric fractures, and provide a theoretical basis for clinical application. METHODS:Authors searched for control ed studies on locking compression plate and dynamic hip screw in the treatment of femoral intertrochanteric fractures in PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP periodical database, Wanfang resource database, Chinese Biomedical Literature service systems published from January 1999 to April 2014. The inclusion and exclusion criteria were made, and the literature meeting the criteria was screened, and the methodological quality of the included studies was evaluated. Meta-analysis was carried out using the RevMan 5.2 software. RESULTS AND CONCLUSION:Ultimately 682 patients from 8 studies met the inclusion criteria, including 336 patients in the locking compression plate group and 346 patients in the dynamic hip screw group. Meta-analysis results showed that:there were no statistical y significant differences in operating time [MD=-12.07, 95%CI (-29.85, 5.71), P=0.18], peri-operative bleeding loss [MD=-15.01, 95%CI (-87.85, 57.83), P=0.69], post-operation drainage [MD=-13.62, 95%CI (-28.49, 1.26), P=0.07], ambulation time [MD=-0.14, 95%CI (-0.68, 0.41), P=0.63], length of hospitalization [MD=-0.74, 95%CI (-2.29, 0.82), P=0.35], bone union time [MD=-1.18, 95%CI (-2.78, 0.42), P=0.15] between locking compression plate and dynamic hip screw groups. The excellent and good rate of postoperative hip function reduction [OR=2.03, 95%CI (1.23, 3.36), P=0.006] was significantly higher in locking compression plate group than in the dynamic hip screw group. The incidence of coxa vara was lower in the locking compression plate group than in the dynamic hip screw group [OR=0.34, 95%CI (0.12, 0.96), P=0.04]. There were no significant differences in looseness, breakage, withdrawal of internal fixation [OR=1.20, 95%CI (0.59, 2.45), P=0.61] and the incidence of total complications [OR=0.55, 95%CI (0.24, 1.28), P=0.16] between locking compression plate and dynamic hip screw groups. However, the included studies have high possibility of selection bias and measurement bias, and wil affect proof strength of results. Therefore, more clinical randomized control ed studies with compact design are needed for verification.

BACKGROUND:Increasing evidence suggests that N6-methyladenosine(m6A)regulators are closely associated with osteoarthritis and are considered to be a new direction in the prevention and treatment of osteoarthritis,but their specific mechanism of action is unknown. OBJECTIVE:To conduct a bioinformatics analysis of the osteoarthritis gene microarray dataset in order to explore the role of m6A in osteoarthritis and analyze the pathogenesis of osteoarthritis. METHODS:The m6A regulators associated with osteoarthritis and their expression were first extracted from the GSE1919 dataset in the GEO database using R software,and then the results were analyzed by gene difference analysis and GO and KEGG enrichment analyses.Subsequently,the results of protein-protein interaction network topology analysis and machine learning results were intersected to obtain the m6A Hub regulators,which were validated by in vitro cellular experiments. RESULTS AND CONCLUSION:A total of 16 osteoarthritis-related m6A regulators were extracted and 11 m6A differential regulators,including ZC3H13,YTHDC1,YTHDF3 and HNRNPC,were obtained by differential analysis.GO enrichment analysis showed that osteoarthritis-related m6A differential regulators played a role in the biological processes such as mRNA transport,RNA catabolism,and regulation of insulin-like growth factor receptor signaling pathway.(3)KEGG enrichment analysis showed that the differential regulators were mainly involved in the p53,interleukin-17 and AMPK signaling pathways.The combined protein-protein interaction network topology analysis and machine learning results obtained the m6A Hub regulator-YTHDC1.(5)The results of in vitro cellular experiments showed that there was a significant difference in the expression of m6A key regulator between the control and experimental groups(P<0.05).To conclude,YTHDC1 is closely related to the development of osteoarthritis,which is expected to be a molecular target of m6A for the treatment of osteoarthritis.

BACKGROUND:Cellular senescence is closely related to the development and progression of osteoarthritis,but the specific targets and regulatory mechanisms are not yet clear. OBJECTIVE:To mine key genes in cellular senescence-mediated osteoarthritis by integrating bioinformatics and machine learning approaches and validate them via experiments to explore the role of cellular senescence in osteoarthritis. METHODS:The osteoarthritis gene expression profiles obtained from the GEO database were intersected with cellular senescence-related genes obtained from the CellAge database and the expression of the intersected genes was extracted for differential analysis,followed by GO and KEGG analysis of the differential genes.The key osteoarthritis cellular senescence genes were then screened by protein-protein interaction network analysis and machine learning,and in vitro cellular experiments were performed.Finally,the expression of the key genes was detected by qPCR. RESULTS AND CONCLUSION:A total of 31 osteoarthritis cell senescence differential genes were identified.GO analysis showed that these genes were mainly involved in the biological processes,such as regulation of leukocyte differentiation,monocyte differentiation,regulation of T cell differentiation and exerted roles in DNA transcription factor binding,histone deacetylase binding,chromatin DNA binding,and chemokine binding.KEGG analysis showed that osteoarthritis cell senescence differential genes were mainly activated in the JAK/STAT signaling pathway,PI3K/Akt signaling pathway and FoxO signaling pathway.MYC,a key gene for osteoarthritis cellular senescence,was identified by protein-protein interaction network topology analysis and machine learning methods.The results of the in vitro cellular assay showed that the mRNA expression of MYC was significantly lower in the experimental group(osteoarthritis group)than the control group(normal group)(P＜0.05).To conclude,MYC can be a key gene in the senescence of osteoarthritic cells and may be a new target in the prevention and treatment of osteoarthritis by mediating immune response,inflammatory response and transcriptional regulation.

BACKGROUND:Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury,but the specific targets and regulatory mechanisms are unclear. OBJECTIVE:A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu)receptor 3(KDELR3)as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis,to provide new ideas and methods for the treatment of osteoarthritis. METHODS:The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes.The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language,and osteoarthritis hypoxia genes were extracted for differential analysis.Differentially expressed genes were analyzed for GO function and KEGG signaling pathway.Weighted correlation network analysis(WGCNA)and machine learning were also used to screen osteoarthritis hypoxia signature genes,and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR. RESULTS AND CONCLUSION:(1)8492 osteoarthritis genes were obtained by batch correction and principal component analysis,mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting;200 hypoxia genes were also obtained,resulting in 41 osteoarthritis hypoxia differentially expressed genes.(2)GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids;cellular components such as lysosomal lumen and Golgi lumen;and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity.(3)KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt,FoxO,and microRNAs in cancer.(4)The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening.(5)The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium(P=0.014)but lower in the meniscus(P=0.024)after validation by gene microarray.(6)In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group(P=0.005),while KDELR3 was closely associated with Macrophages M0(P=0.014)and T cells follicular helper(P=0.014).Using a machine learning approach,we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia,expecting to provide a new direction for better treatment of osteoarthritis.

BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION:(1)12 548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.000 14).(5)In vitro chondrocyte assay showed that type Ⅱ collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes(P<0.05),while in osteoarthritis ferroptosis,mast cells activated was closely correlated with dendritic cells(r=0.99);KLF2 was closely correlated with natural killer cells(r=-1,P=0.017)and T cells follicular helper(r=-1,P=0.017).(6)The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2.