BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

Objective:To construct a systematic, standardized, scientific and effective evaluation index system for standardized training of newly recruited nurses.Methods:By the literature review, applying the Delphi method and purpose sampling to conduct two rounds of expert consultations to 17 nursing experts from 5 medical units and 1 nursing school from October 2019 to April 2020 to construct the evaluation index system for standardized training of newly recruited nurses.Results:The recovery rates of the two rounds of expert consultation questionnaires were all 100%, and the expert authority coefficient was 0.92. The final evaluation index system for standardized training of newly recruited nurses included 4 first-level indicators (professional literacy, theoretical knowledge, nursing operation skills, and core competency of the post) , 17 second-level indicators, and 52 third-level indicators.Conclusions:The evaluation index system for standardized training of newly recruited nurses established in this study is systematic, scientific and effective, which can provide a reference for standardized training and evaluation of newly recruited nurses, and provide a theoretical basis and practical guidance for improving the training effect and quality.

Aim To elucidate the therapeutic effect of ginsenosides, berberine and jasminoidin after given a-lone or treatment with combination on the focal cerebral ischemia rats and study the compatibility mechanism. Methods We determined 12 endogenous amino acids in serum of rats after cerebral ischemia over 12 hours with RRLC-QQQ to evaluate the integrated role of YQJD at the dosage of 25 mg·kg-1 and 5 mg·kg-1 . Generally accepted methods were used, including be-havior test, One-Way AVONA, PLS-DA, as well as PCA to evaluate the injury induced by focal cerebral is-chemia. Results The score of neurological deficits and the level of five amino acids, namely Glu, Asp, Met, Hcy, Phe in the combination of ginsenosides, berberine and jasminoidin group in the dosage of 25 mg ·kg-1 and 5 mg·kg-1 significantly decreased (P<0. 05, P<0. 01) compared to those of model group. For another, the largest contribution group in the three principal components of PC1 , PC3 , PC4 at the dosage of 25 mg/kg and the six principal components PC1 ~PC5, PC7 in 5 mg·kg-1 was the combination of gin-senosides, berberine, jasminoidin group. Conclusions The results suggest that the efficacy of the combina-tion of ginsenosides, berberine and jasminoidin is su-perior to the combination of two or any single compo-nent, which can significantly improve the metabolic disorder of the endogenous amino acid after cerebral is-chemia. And it could be speculated that ginsenosides may play a more important role than berberine and jas-minoidin in regulating the level of amino acid metabo-lism.