Objective:To investigate the clinical effect and application value of free skin or auricle composite tissue transplantation in the correction of nasal alar retraction.Methods:From August 2019 to January 2023, a total of 45 patients with nasal retraction (7 males and 38 females) were treated in Shanghai United Regal Medical Cosmetology Hospital. Age ranged from 18 to 46 years, with a mean of 26.6 years. All patients had retroalar margin retraction caused by insufficient alar lining to varying degrees, with a retraction distance between 2-5 mm, with an average retraction distance of 3.3 mm. 11 cases of nasal flange retraction were corrected by free transplantation of composite tissue of auricle directly. The remaining 34 patients were corrected by alar border cartilage graft and free skin graft.Results:During 2-18 months of follow-up, 41 patients had satisfactory results and no obvious complications. Partial necrosis of free skin was observed in 2 patients, ischemic necrosis of complex flap of auricle was observed in 1 patient, and necrosis of free skin was observed in 1 patient. In addition to the above complications, 3 patients reported that the correction of alar retraction did not achieve the expected effect, but indicated that they were satisfied with the surgical effect, which was considered to be caused by skin contracture and other factors. The overall satisfaction rate of all patients was 91.1%. After follow-up and statistics, 41 patients in this group were satisfied with the results, with an overall satisfaction rate of 91.12%.Conclusions:The application of free skin graft (or auricle composite tissue) to correct nasal alar retraction has the advantages of simple operation, wide indications and accurate efficacy, and is worthy of clinical promotion.

In view of the shortage of medical equipment road transportation simulation platform, we put forward a road transportation simulation method based on 6-DOF parallel robots. A 3D road spectrum model was built by the improvement of the harmonic superposition method. The simulation model was then compared with the standard model to verify its performance. Taking the road spectrum as the excitation, we could get the robot motion data to control the parallel robot through the S-shaped linear interpolation of the absolute position. It can simulate the movement of vehicles with different speed under various road conditions efficiently and accurately.
Equipment Design ; Motor Vehicles ; Robotics ; instrumentation ; Transportation ; instrumentation ; methods

Objective To analyze survival and recurrence rates of patients with Masaoka stage Ⅲ thymoma and to explore the prognostic factors.Methods Between September 1965 and December 2010,a total of 111 patients with stage Ⅲ thymoma treated in our hospital were retrospectively analyzed.Sixty-eight patientsreceived comple te rescction ± radiotherapy,whilc 23 patients received incomplete resection ±radiotherapy and 20 patients received biopsy ± radiotherapy.Eighty-seven patients received postoperative radiotherapy (12 patients received preoperative radiotherapy) while 24 patients received surgery alone.Results The median follow-up time was 66 months (5-540) with a follow-up rate of 92.5％ (111/120).Compared with incomplete resection ± radiotherapy and biopsy ± radiotherapy,the 5-year overall survival (OS) (88％ vs.59％ and 57％,x2 =12.11,P =0.002),disease free survival (DFS) (74％ vs.40％ and 41％,x2 =11.49,P =0.003) and disease specific survival (DSS) (94％ vs.69％ and 60％,x2 =10.95,P =0.004) could be improved with complete resected ± radiotherapy.Compared with surgery alone,postoperative radiotherapy did not improve OS,DFS and DSS (55％ vs.77％ (x2 =1.01,P =0.316),61％vs.61％ (x2 =0.12,P =0.729) and 72％ vs.85％ (x2 =0.27,P =0.601),respectively).For the 68 patients received complete resection,radiotherapy after complete resection (56 patients) did not improve OS,DFS and DSS (82％ vs.89％ (x2 =0.31,P =0.576),72％ vs.81％ (x2 =0.05,P=0.819) and 89％vs.95 ％ (x2 =0.05,P =0.825),respectively) compared with surgery alone (8 patients).Conclusions Stage Ⅲ thymoma patients received complete resection had better outcome than patients received incomplete resection or biopsied only.The role of postoperative radiotherapy is still controversial for stage Ⅲ thymoma,randomized clinical trial is needed

BACKGROUND:Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis.Artificial neural networks have powerful data computing and classification capabilities,which have shown better performance in disease diagnosis. OBJECTIVE:To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS:The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group.The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes.GO and KEGG enrichment analyses were performed on the differentially expressed genes.Through Lasso regression model,support vector machine model and random forest tree model,the key genes of osteoarthritis were further identified from the differentially expressed genes.The R software"Neuralnet"package was then used to construct the osteoarthritis diagnosis model based on artificial neural network,and the model performance was evaluated by the five-fold cross-validation.Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION:A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis,of which 33 were down-regulated and 57 were up-regulated.GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes,including leukocyte-mediated immunity,leukocyte migration in bone marrow and chemokine production.KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis,interleukin-17 signaling pathway and osteoclast differentiation pathway.Five key genes for the diagnosis of osteoarthritis,HMGB2,GADD45A,SLC19A2,TPPP3 and FOLR2,were identified by three machine learning methods.The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36%and the area under the curve was 0.997.The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness.Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively.Therefore,the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value.

BACKGROUND:Rheumatoid arthritis is a chronic systemic autoimmune disease.It is important to study the immunological changes involved in it for diagnosis and treatment. OBJECTIVE:To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers. METHODS:Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases.Kyoto encyclopedia of genes and genomes(KEGG)and gene ontology(GO)enrichment analyses were used to investigate the possible function of these elevated genes.The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator(Lasso)and support vector machine recursive feature elimination(SVM-RFE).Independent datasets were used for difference validation,and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes.Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells.In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells,Mendelian randomization analysis was ultimately employed. RESULTS AND CONCLUSION:There were 39 upregulated differentially expressed genes in rheumatoid arthritis.The genes were primarily enriched in chemotaxis,cytokine activity,and immune receptor activity,according to GO enrichment analysis,while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration.Lasso and SVM-RFE identified five feature genes:CXCL13,SDC1,IGLC1,PLXNC1,and SLC29A3.Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples,with area under the curve values greater than 0.8 for all five feature genes in both datasets.Immune cell infiltration indicated that most immune cells,including natural killer cells and monocytes,exhibited increased levels of infiltration in rheumatoid arthritis samples.The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes.Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells.The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis.

BACKGROUND:Knee osteoarthritis(KOA)is a common chronic inflammatory disease that causes damage to joint cartilage and surrounding tissues.Immune cells play an important role in the immune-inflammatory response in knee osteoarthritis,but the specific mechanisms involved are still not fully understood. OBJECTIVE:To evaluate the potential causal relationship between 731 immune cell phenotypes and the risk of knee osteoarthritis using Mendelian randomization. METHODS:Summary statistics of genome-wide association studies(GWAS)for 731 immune cell phenotypes(from GCST0001391 to GCST0002121)obtained from the GWAS catalog and GWAS data for knee osteoarthritis from the IEUGWAS database(ebi-a-GCST007090)were used.Inverse variance-weighted method,MR-Egger regression,weighted median method,weighted mode method,and simple mode method were employed to investigate the causal relationship between immune cells and knee osteoarthritis.Sensitivity analyses were conducted to assess the reliability of the Mendelian randomization results.Reverse Mendelian randomization analysis was also performed using the same methods. RESULTS AND CONCLUSION:The forward MR analysis indicated significant causal relationships(FDR<0.20)between knee osteoarthritis and four immune cell phenotypes,namely CD27 on CD24+CD27+in B cells(OR=1.026,P=0.000 26,Pfdr=0.18),CD33 on CD33dim HLA DR-in myeloid cells(OR=1.014,P=0.000 50,Pfdr=0.18),and CD45RA+CD28-CD8br%CD8br in Treg cells(OR=1.001,P=0.000 78,Pfdr=0.18),and PDL-1 on monocytes in mononuclear cells(OR=0.952,P=0.000 98,Pfdr=0.18).These immune cell phenotypes showed direct positive or negative causal associations with the risk of knee osteoarthritis.Reverse Mendelian randomization analysis revealed no significant causal relationships(FDR<0.20)between knee osteoarthritis as exposure and any of the 731 immune cell phenotypes.The results of sensitivity analysis show that the P-values of the Cochran's Q test and the MR-Egger regression method for bidirectional Mendelian randomization were both greater than 0.05,indicating that there is no significant heterogeneity and pleiotropy in the causal effect analysis between immune cell phenotypes and knee osteoarthritis.To conclude,there may be four potential causal relationships between immune cell phenotypes,such as CD27 on CD24+CD27+cells,CD33 on CD33dim HLA DR-cells,CD45RA+CD28-CD8br%CD8br cells,and PDL-1 on monocytes,and knee osteoarthritis.These findings provide valuable clues for studying the biological mechanisms of knee osteoarthritis and exploring early prevention and treatment strategies.They also offer new directions for the development of intervention drugs.

Objective: To investigate the effect of endogenous iron accumulation onbone mass, intraosseous vessels and the effect of exogenous iron on endothelial cell activity. Methods: The mice were divided into control group (C57/BL6 mice without hepcidin knockout) and hepcidin-knockout group (10 mice in each group, 8 weeks old and weighing about 22 g). The mice in both groups were killed at the age of 16 weeks. Serum ferritin levels were measured by Enzyme-linked immunosorbent assay (ELISA), and iron accumulation in liver tissue was measured by Prussian blue staining, while femoral micro-structure was measured by micro-CT, and H-type vessel immunofluorescence staining was used to detect the number of H-vessels in bone. Cell experiments were divided into normal culture group (normal cell group) and intervention group (Fe group) with 200 μmol/L ammonium ferric citrate. Scratch test was used to detect the migration ability of vascular endothelial cells, and tube formation test was used to detect the function of vascular endothelial cells. The endothelial activity of vascular endothelial cells was detected by immunofluorescence. Results: The level of serum ferritin (318.30±12.53 ng/ml) in the hepcidin-knockout group was significantly higher than that in control group (109.60±4.66 ng/ml). The percentage of blue area of Prussian liver iron staining in the hepcidin-knockout group (80.80%±3.156%) was significantly higher than that in control group (20.94%±2.813%). Bone mineral density in the hepcidin-knockout group (0.044±0.002 mg/m³) was significantly higher than that in control group (0.131±0.008 mg/m³). The number of intraosseous blood vessels in the hepcidin-depleted mice (17.06%±1.060%) was significantly lower than that in control group (38.76%±4.576%). There were significant differences between the two groups in each index (t=-49.367,-13.788, 35.293, 6.165; all P < 0.05). The scratches of vascular endothelial cells in Fe group were reduced by 24.300%±1.849% after 24 hours of culture, which was significantly lower than that in normal group (39.060%±3.211%). The area of vascular endothelial cells in Fe group (0.035±0.003 mm²) was significantly lower than that in normal group (0.330±0.018 mm²). The EMCN positive cells of vascular endothelial cells in Fe group were significantly lower than those in normal group. The percentage of cell number (12.000%±3.462%) was significantly lower than that of normal cell group (0.035%±0.003%). There were significant differences in cell indices between the two groups (t=9.790, 18.929, 13.922; all P< 0.05). Conclusion Endogenous iron accumulation aggravated bone loss in mice, and the number of intraosseous blood vessels decreased significantly. Vascular endothelial cells were inhibited by iron intervention in migration, tube-formation and endothelial ability.