Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.
In this study, we describe a β-cyclodextrins (β-CDs)/Ni-based MOF (β-CDs/Ni-based MOF) network for orthopedic applications.
Background: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value. Methods:The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration.Results: Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells.Conclusions: This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS.
Background: Post-traumatic joint contracture (PTJC) mainly manifests as excessive inflammation leading to joint capsule fibrosis. Transforming growth factor (TGF)-β1, a key regulator of inflammation and fibrosis, can promote fibroblast activation, proliferation, migration, and differentiation into myofibroblasts. Platelet-rich plasma (PRP) is considered to have strong potential for improving tissue healing and regeneration, the ability to treat joint capsule fibrosis remains largely unknown.Methods: In this study, we aimed to determine the antifibrotic potential of PRP in vivo or in vitro and its possible molecular mechanisms. The TGF-β1-induced primary joint capsule fibroblast model and rat PTJC model were used to observe several fibrotic markers (TGF-β1, α-SMA, COL-Ⅰ, MMP-9) and signaling transduction pathway (Smad2/3) using histological staining, qRT-PCR and western blot.Results: Fibroblasts transformed to myofibroblasts after TGF-β1 stimulation with an increase of TGF-β1, α-SMA, COL-Ⅰ, MMP-9 and the activation of Smad2/3 in vitro. However, TGF-β1-induced upregulation or activation of these fibrotic markers or signaling could be effectively suppressed by the introduction of PRP. Fibrotic markers’ similar changes were observed in the rat PTJC model and PRP effectively reduced inflammatory cell infiltration and collagen fiber deposition in the posterior joint capsule. Interestingly, HE staining showed that articular cartilage was degraded after rat PTJC, and PRP injection also have the potential to protect articular cartilage.Conclusion: PRP can attenuate pathological changes of joint capsule fibrosis during PTJC, which may be implemented by inhibiting TGF-β1/Smad2/3 signaling and downstream fibrotic marker expression in joint capsule fibroblasts.
The nano-hydroxyapatite/polyamide 66 (n-HA/PA66) bionic bone column, as a high-performance tissue repair and replacement material, introduced as a high osteo-induction ability agent. Nanomaterial has significantly taken a place in orthopedic surgery, however, the efficacy of using n-HA/PA66 is yet to be established. In this regard, this study evaluated various sagittal parameters (such as imaging measurement) and clinical efficacy in postoperative patients, whom underwent cervical reconstruction surgery due to cervical spondylosis myelopathy (CSM). In this study, total 62 CSM cases were enrolled between October 2016 to March 2020, and were hospitalized for cervical reconstruction surgery. 31 cases were grafted with titanium mesh and 31 cases were grafted with n-HA/P66. The sagittal parameters such as cervical spine lateral radiographs (C0–2Coob, C2–7Coob, T1S, CSVA, and TIA) were taken before operation, after operation (within 1 week), 3, 6, and 9 months after operation. In order to evaluate the clinical efficacy, we used JOA scores before, after, 3 months, 6 months and 9 months after operation. Results showed that JOA scores after the re-examination in the two groups (titanium and n-HA/P66) were significantly higher than before the operation, suggesting a well postoperative functional recovery after surgery in both groups; however, there was no significant difference in JOA score and JOA improvement index between the two groups. In regard of angles measurement (C0–2Cobb, C2–7Cobb, T1S, CSVA, and TIA), we observed no significant difference between these two groups before and after the operation. In addition, we showed that C0–2Cobb and C2–7Cobb angle had a significant positive correlation; and C0–2Cobb angle is positively correlated with T1S, and negatively correlated with CSVA. Both titanium mesh and n-HA/PA66 can be well improved and maintained within 9 months after surgery with clinical efficacy, however, using n-HA/PA66 might have more benefits.
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