BACKGROUND:In China,the patient population with osteonecrosis is large,and there is an urgent need to find new preventive targets to develop more effective treatment strategies.Metabolomics studies have shown that there is an association between human metabolites and osteonecrosis,but the causal relationship between blood metabolites and osteonecrosis has not yet been clarified.OBJECTIVE:To investigate the causal relationship between blood metabolites and osteonecrosis through two-sample Mendelian randomization analysis.METHODS:The public data of 486 blood metabolites(exposure factors)and osteonecrosis(outcome factors)were collected.Data of 486 blood metabolites were derived from a genome-wide association estimate for blood metabolites published in Nature Genetics in 2014,which covered 7 824 European adults.The single nucleotide polymorphism data for osteonecrosis were obtained from the FinnGen public database R11 dataset,containing information on a total of 431 614 samples and 21 306 430 single nucleotide polymorphism loci,with 1 788 cases of osteonecrosis and 429 826 controls,with all participants being of European descent.Mendelian randomization analysis(inverse variance weighting method,MR-Egger method,and weighted median method)was performed by Rstudio software,and then the heterogeneity test,horizontal pleiotropy test and Steiger directionality test were performed to ensure the robustness and reliability of the results.RESULTS AND CONCLUSION:(1)Sixteen blood metabolites were identified as having a significant causal relationship with osteonecrosis(Pinverse variance weighting＜Pfalse discovery rate＜0.05).(2)Eight blood metabolites increased the risk of osteonecrosis(including four known metabolites and four unknown metabolites),specifically pantothenate,beta-hydroxyisovalerate,hippurate,salicyluric glucuronide,X-08766,X-11452,X-12776 and X-14662.(3)Eight blood metabolites could reduce the risk of osteonecrosis(six known metabolites and two unknown metabolites),including cortisol,1-palmitoylglycerol(1-monopalmitin),pyroglutamyl glycine,2-stearoylglycerophosphocholine,p-cresol sulfate,ergothioneine,X-06307,X-12092.(4)The above results suggest that there is a causal relationship between 16 blood metabolites and osteonecrosis,which is expected to be a potential target for intervention in the occurrence and treatment of osteonecrosis in the future.(5)Despite the lack of relevant data from large-scale Asian populations at present,this study provides important reference value for the field of osteonecrosis in China based on European population data.In the future,domestic medical workers may be able to achieve precise intervention for osteonecrosis by regulating metabolite levels.In addition,based on the results of this study,relevant researchers can further explore the mechanism of action of metabolites in the treatment of osteonecrosis with traditional Chinese medicine,which not only helps to deepen the understanding of traditional Chinese medical therapies but also promotes the progress of integrated traditional Chinese and Western medicine research,driving the development of personalized treatment plans that are more suitable for the characteristics of the Chinese population.

BACKGROUND:In China,the patient population with osteonecrosis is large,and there is an urgent need to find new preventive targets to develop more effective treatment strategies.Metabolomics studies have shown that there is an association between human metabolites and osteonecrosis,but the causal relationship between blood metabolites and osteonecrosis has not yet been clarified.OBJECTIVE:To investigate the causal relationship between blood metabolites and osteonecrosis through two-sample Mendelian randomization analysis.METHODS:The public data of 486 blood metabolites(exposure factors)and osteonecrosis(outcome factors)were collected.Data of 486 blood metabolites were derived from a genome-wide association estimate for blood metabolites published in Nature Genetics in 2014,which covered 7 824 European adults.The single nucleotide polymorphism data for osteonecrosis were obtained from the FinnGen public database R11 dataset,containing information on a total of 431 614 samples and 21 306 430 single nucleotide polymorphism loci,with 1 788 cases of osteonecrosis and 429 826 controls,with all participants being of European descent.Mendelian randomization analysis(inverse variance weighting method,MR-Egger method,and weighted median method)was performed by Rstudio software,and then the heterogeneity test,horizontal pleiotropy test and Steiger directionality test were performed to ensure the robustness and reliability of the results.RESULTS AND CONCLUSION:(1)Sixteen blood metabolites were identified as having a significant causal relationship with osteonecrosis(Pinverse variance weighting＜Pfalse discovery rate＜0.05).(2)Eight blood metabolites increased the risk of osteonecrosis(including four known metabolites and four unknown metabolites),specifically pantothenate,beta-hydroxyisovalerate,hippurate,salicyluric glucuronide,X-08766,X-11452,X-12776 and X-14662.(3)Eight blood metabolites could reduce the risk of osteonecrosis(six known metabolites and two unknown metabolites),including cortisol,1-palmitoylglycerol(1-monopalmitin),pyroglutamyl glycine,2-stearoylglycerophosphocholine,p-cresol sulfate,ergothioneine,X-06307,X-12092.(4)The above results suggest that there is a causal relationship between 16 blood metabolites and osteonecrosis,which is expected to be a potential target for intervention in the occurrence and treatment of osteonecrosis in the future.(5)Despite the lack of relevant data from large-scale Asian populations at present,this study provides important reference value for the field of osteonecrosis in China based on European population data.In the future,domestic medical workers may be able to achieve precise intervention for osteonecrosis by regulating metabolite levels.In addition,based on the results of this study,relevant researchers can further explore the mechanism of action of metabolites in the treatment of osteonecrosis with traditional Chinese medicine,which not only helps to deepen the understanding of traditional Chinese medical therapies but also promotes the progress of integrated traditional Chinese and Western medicine research,driving the development of personalized treatment plans that are more suitable for the characteristics of the Chinese population.

BACKGROUND:Hormonal osteonecrosis of the femoral head is strongly associated with aging,but the regulatory targets and mechanisms are still unclear.Through bioinformatics combined with machine learning analysis and experimental verification,the key genes of hormonal osteonecrosis of the femoral head mediated by cell senescence will be identified,which will provide new ideas for the prevention and treatment of hormonal osteonecrosis of the femoral head.OBJECTIVE:To screen and validate the senescence core genes of hormonal osteonecrosis of the femoral head using bioinformatics analysis to explore its mechanism of action.METHODS:The GSE123568 dataset was obtained from the GPL15207 platform of the GEO database,which contained the gene expression profiles of peripheral serum samples of 30 hormonal osteonecrosis of the femoral head patients and 10 healthy controls.Data on 279 cellular senescence-related genes were obtained from the CellAge database.Differential analysis and weighted correlation network analysis(WGCNA)were performed on hormonal osteonecrosis of the femoral head gene profiles,and both were intersected with senescence-related genes and then concatenated to obtain hormonal osteonecrosis of the femoral head senescence potential genes,and GO and KEGG analyses were performed.The machine learning method screened out the pivotal genes,constructed nomogram model,and performed consensus clustering and immune infiltration analysis.Finally,clinical femoral samples were collected for validation by qPCR and western blot assay.RESULTS AND CONCLUSION:(1)41 potential genes were obtained,which were mainly enriched in biological processes such as aging and oxidative stress response,as well as FoxO and tumor necrosis factor signaling pathways.(2)The pivotal genes catalase,connective tissue growth factor,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were obtained after machine learning identification,and the predictive ability of nomogram model was good.(3)The patients were classified into three groups,namely a,b and c,by the consensus clustering analysis.Catalase,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were differentially expressed among the three molecular subtypes(P＜0.05).Results of immune infiltration showed that the abundance of immune cells,such as activated CD4+T cells,activated CD8+T cells,and eosinophils,differed among the three molecular subclasses(P＜0.05).(4)The results of qPCR and western blot assay showed that the expression of catalase,connective tissue growth factor,forkhead box protein O3,and mitogen-activated protein kinase kinase 11 was lower in hormonal osteonecrosis of the femoral head group compared to the control group(P＜0.05),and the expression of insulin receptor substrate 2 was elevated(P＜0.05).(5)It is concluded that through in-depth analysis combined with bioinformatics and machine learning,and further experimental verification,five hormonal osteonecrosis of the femoral head age-related hub genes were finally identified.These genes are catalase,connective tissue growth factor,forkhead box o3,insulin receptor substrate 2,and serine/threonine kinase 11.These genes may provide potential molecular targets for the prevention and treatment of hormonal osteonecrosis of the femoral head in the future by regulating the cellular aging process.

BACKGROUND:Hormonal osteonecrosis of the femoral head is strongly associated with aging,but the regulatory targets and mechanisms are still unclear.Through bioinformatics combined with machine learning analysis and experimental verification,the key genes of hormonal osteonecrosis of the femoral head mediated by cell senescence will be identified,which will provide new ideas for the prevention and treatment of hormonal osteonecrosis of the femoral head.OBJECTIVE:To screen and validate the senescence core genes of hormonal osteonecrosis of the femoral head using bioinformatics analysis to explore its mechanism of action.METHODS:The GSE123568 dataset was obtained from the GPL15207 platform of the GEO database,which contained the gene expression profiles of peripheral serum samples of 30 hormonal osteonecrosis of the femoral head patients and 10 healthy controls.Data on 279 cellular senescence-related genes were obtained from the CellAge database.Differential analysis and weighted correlation network analysis(WGCNA)were performed on hormonal osteonecrosis of the femoral head gene profiles,and both were intersected with senescence-related genes and then concatenated to obtain hormonal osteonecrosis of the femoral head senescence potential genes,and GO and KEGG analyses were performed.The machine learning method screened out the pivotal genes,constructed nomogram model,and performed consensus clustering and immune infiltration analysis.Finally,clinical femoral samples were collected for validation by qPCR and western blot assay.RESULTS AND CONCLUSION:(1)41 potential genes were obtained,which were mainly enriched in biological processes such as aging and oxidative stress response,as well as FoxO and tumor necrosis factor signaling pathways.(2)The pivotal genes catalase,connective tissue growth factor,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were obtained after machine learning identification,and the predictive ability of nomogram model was good.(3)The patients were classified into three groups,namely a,b and c,by the consensus clustering analysis.Catalase,forkhead box protein O3,insulin receptor substrate 2,and mitogen-activated protein kinase kinase 11 were differentially expressed among the three molecular subtypes(P＜0.05).Results of immune infiltration showed that the abundance of immune cells,such as activated CD4+T cells,activated CD8+T cells,and eosinophils,differed among the three molecular subclasses(P＜0.05).(4)The results of qPCR and western blot assay showed that the expression of catalase,connective tissue growth factor,forkhead box protein O3,and mitogen-activated protein kinase kinase 11 was lower in hormonal osteonecrosis of the femoral head group compared to the control group(P＜0.05),and the expression of insulin receptor substrate 2 was elevated(P＜0.05).(5)It is concluded that through in-depth analysis combined with bioinformatics and machine learning,and further experimental verification,five hormonal osteonecrosis of the femoral head age-related hub genes were finally identified.These genes are catalase,connective tissue growth factor,forkhead box o3,insulin receptor substrate 2,and serine/threonine kinase 11.These genes may provide potential molecular targets for the prevention and treatment of hormonal osteonecrosis of the femoral head in the future by regulating the cellular aging process.

According to the problems exist in the original ultrasound system, the study elaborates the design and application of the ultrasound audit workstation system, including the workflow, trace information recording, information management, audit data interaction, application effects, et al. This study points out that the system can optimize the ultrasound process, help to improve the quality and efficiency of ultrasound report audit as well as improve the efficiency of patients' ultrasound examination and medical treatment experience.
Humans ; Medical Audit

Objective To analyze the genetic characteristics of Legionella pneumophila isolated from cooling water of central air conditioning system in public places in Zhongshan from 2012 to 2018, and to understand the spatiotemporal distribution of homologous strains, in order to provide evidence for the prevention, control and traceability of Legionella pneumophila infection. Methods Eighty-five Legionella pneumophila strains were isolated for serotype identification, and the molecular typing of the 85 isolates was performed using pulsed field gel electrophoresis (PFGE). The strain location data was converted into latitude and longitude coordinates by GIS geocoding technology. The converted location data was overlaid on the map of Zhongshan City, mapping the molecular typing distribution of clusters using Qgis2.18.11 spatial processing software. Results Eighty-five strains of Legionella pneumophila included 9 serotypes, and the highest proportion was LP1, accounting for 61.18% (52/85). According to the similarity of 100%, 85 strains of Legionella pneumophila were divided into 56 patterns of PFGE bands (T1-T56), with 3 types being dominant. Same serotype of Legionella pneumophila strains showed diverse PFGE patterns. Different serotypes of Legionella pneumophila strains were basically identified as different PFGE patterns, while some were identified as same PFGE pattern. According to over 85% similarity, 8 clusters (A-H) were designated, strains of which were distributed in 12 districts. PFGE clustering clusters did not display obvious temporal and regional distribution differences, nor did they have temporal and regional clustering distributions. Conclusion Strains of Legionella pneumophila isolated from cooling water of central air conditioning system in public places in Zhongshan from 2012 to 2018 showed genetic diversity, and the main serotype was LP1. Isolates of clusters did not exist in different years or regions.

Objective To analyze the status, trends and issues of professionals in rehabilitation institutions in China, and provide policy recommendations on rehabilitation professional development.Methods The data from database and statistical bulletin of CDPF has been analyzed using descriptive analysis and deviation analysis.Results The quantity of professionals of rehabilitation institutions increased 24,900 (12.62%) in 2016. Average professionals per institution had been decreased from 33.89 in 2012 to 28.33 in 2016. In regard to the structure of distribution, the rehabilitation professionals at provincial level had been decreased 21.95% in 2015 than that of in 2012. Both training programs and the number of trainees from rehabilitation institutions had decreased.Conclusion There were big gap between services provision and needs of professionals. The distribution of professionals at provincial, city and county level was under optimization. The on-job training for rehabilitation professionals should be improved. It is recommendated to develop national plan for professionals development to meet the needs of rehabilitation services, advance the on-job training for professionals, develop the higher education of rehabilitation and improve attractiveness of rehabilitation to retain and recruit more professionals.

In order to further research into the EEG information of rats epileptic model, we applied different nonlinear dynamic methods. After having analyzed the EEG signal of rat falling sickness by means of approximate entropy and correlation dimension, we adopted a the new method, unstable periodic orbits, which was used to analyze complex activity of neurons system to look for the change regularity of change in the EEG signal in the whole course of rat's falling sickness. We found period 1 orbits and period 2 orbits to be statistically significant in the data of ictal time of epilepsy. At the same time, we found period 1 orbits to be statistically significant in the data of preictal time of epilepsy.
Animals ; Disease Models, Animal ; Electroencephalography ; Entropy ; Epilepsy ; physiopathology ; Nonlinear Dynamics ; Rats ; Signal Processing, Computer-Assisted