Background: Knee osteoarthritis is a common joint degenerative disease that severely affects the quality of life. Modern pharmacological studies have confirmed that Xiao Huoluo Pills (XHLP) has anti-inflammatory, anti-oxidant, and analgesic effects. At present, it has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP's specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods: Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB>30%, DL≥0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P Value<0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results: XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 up-regulated genes and 759 down-regulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis.Conclusions: In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

Background: Knee osteoarthritis is a common joint degenerative disease that severely affects the quality of life. Modern pharmacological studies have confirmed that Xiao Huoluo Pills (XHLP) has anti-inflammatory, anti-oxidant, and analgesic effects. At present, it has been used to treat degenerative diseases such as osteoarthritis and hyperosteogeny. However, XHLP's specific effective ingredients and mechanism of action against osteoarthritis have not been explored. Therefore, bioinformatics technology and molecular docking technology are employed in this study to explore the molecular basis and mechanism of XHLP in the treatment of knee osteoarthritis. Methods: Public databases (TCMSP, Batman-TCM, HERB, DrugBank, and UniProt) are used to find the effective active components and corresponding target proteins of XHLP (screening conditions: OB>30%, DL≥0.18). Differentially expressed genes related to cartilage lesions of knee osteoarthritis are obtained based on the GEO database (screening conditions: adjust P Value<0.01, |log2 FC|≥1.0). The Venn package in R language and the BisoGenet plug-in in Cytoscape are adopted to predict the potential molecules of XHLP in the treatment of knee osteoarthritis. The XHLP-active component-target interaction network and the XHLP-knee osteoarthritis-target protein core network are constructed using Cytoscape software. Besides, GO/KEGG enrichment analysis on core genes is performed using the Bioconductor package and clusterProfiler package in the R language to explain the biological functions and signal pathways of the core proteins. Finally, molecular docking is performed through software such as Vina, LeDock, Discovery Studio 2016, PyMOL, AutoDockTools 1.5.6, so as to verify the binding ability between the active components of the drug and the core target protein. Results: XHLP has been screened out of 71 potentially effective active compounds for the treatment of OA, mainly including quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid. Knee osteoarthritis cartilage lesion sequencing data (GSE114007) was screened out of 1672 differentially expressed genes, including 913 up-regulated genes and 759 down-regulated genes, displayed as heat maps and volcano maps. Besides, 33 core target proteins are calculated by Venn data package in R and BisoGenet plug-in in Cytoscape. The enrichment analysis on these target genes revealed that the core target genes are mainly involved in biological processes such as response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation. These core target genes are involved in signaling pathways related to cartilage degeneration of knee osteoarthritis such as TNF signaling pathway and PI3K-Akt signaling pathway. Finally, the molecular docking verification demonstrates that some active components of the drug have good molecular docking and binding ability with the core target protein, further confirming that XHLP has the effect of inhibiting cartilage degeneration in knee osteoarthritis.Conclusions: In this study, based on the research foundation of bioinformatics and molecular docking technology, the active components and core target molecules of XHLP for the treatment of cartilage degeneration of knee osteoarthritis are screened out, and the potential mechanism of XHLP inhibiting cartilage degeneration of knee osteoarthritis is deeply explored. The results provide theoretical basis and new treatment plan for XHLP in the treatment of knee osteoarthritis.

Background The positional distribution and size of the weight-bearing area of femoral head in the standing position as well as the direct active surface of joint force can directly affect the result of finite element (FE) stress analysis.However,the division of this area was vague, imprecise and un-individualized in most studies related separate FE models of femur. The purpose of this study was to quantify the positional distribution and size of the weight-bearing area of femoral head in standing position by a set of simple methods, to realize individualized reconstruction of proximal femur FE model. Methods Five adult volunteers were recruited for X-ray and CT examination in the same simulated bipedal standing position with a specialized patented device. We extracted these image data, calculated the 2D weight-bearing area on X-ray image, reconstructed the 3D model of proximal femur based on CT data, and registered them to realize the 2D weight-bearing area to 3D transformation as the quantified weight-bearing surface. One of the 3D models of proximal femur was randomly selected for finite element analysis (FEA), and we defined three different loading surfaces, and compared their FEA results. Results A total of 10 weight-bearing surfaces in 5 volunteers were constructed, they were mainly distributed on the dome and anterolateral of femoral head with crescent shape, in the range of 1,218.63mm2 − 1,871.06mm2. The results of FEA showed stress magnitude and distribution in proximal femur FE models among three different loading conditions were significant differences, the loading case with quantized weight-bearing area was more in accordance with the physical phenomenon of the hip. Conclusion This study confirmed an effective FE modeling method of proximal femur, which can quantify weight-bearing area to define more reasonable load surface setting without increasing the actual modeling difficulty.

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Background: There has been no indicators that can effectively predict femoral head collapse in ONFH so far. The aim of this study is to retrospectively analyze the first-visit medial space ratio of the hip joint to evaluate its efficacy in predicting ONFH-induced collapse and impacts on the mechanical environment of necrotic femoral head.Methods: In this retrospective analysis for traditional Chinese medicine (TCM), non-traumatic osteonecrosis of femoral head (NONFH) patients from January 2010 to December 2016 were selected. The medial space ratio at their first visit and and collapse of the femoral head during the follow-up were recorded. Patients were divided into group A, B, C, D, and E according to the grading of the medial space ratio at the first visit. A total of 14 hip joint models with distinct medial space ratios were established. The maximum stress intensities of the cartilage, cortical bone and the necrotic area (N-unit area) of the femoral head in the models were quantitated using a finite element analysis.Results: One hundred twenty-eight patients (142 hips) were included in this study. The average follow-up time was 5.4±1.5 years.The Kaplan-Meier survival analysis showed that survival rates of the first-visit medial space ratios in group C and D were significantly higher (exceeding 45.76%) than that in group E (22.73%). There was no significant difference in the survival rates between group C and D (P > 0.05). The finite element analysis showed that in either the necrosis or non-necrosis group, the maximum stress in cartilage, cortical bone and the N-unit area of the femoral head increased with the medial space ratio decreasing. Conclusion: The medial space ratio shows clinical implications of predicting the collapse of NONFH except for assessing the relationship between the femoral head and the acetabulum. The stress concentrations of cartilage, cortical bone, and the necrotic area of the femoral head are enhanced with the medial space radio decreasing. Once the medial space ratio falls below 4 and continues to decline, the risk of necrotic collapse will become higher.