BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

Objective To investigate the changes of serum microRNA (miR)-138-5p and microRNA(miR)-574-5p expression levels and their clinical significance in newborns acute respiratory distress syndrome (nARDS). Methods A total of 112 infants with nARDS from September 2018 to August 2023 in the Neonatal Department of Xiaogan Hospital Affiliated to Wuhan University of Science and Technology were collected as the observation group. They were divided into good prognosis group (n=98) and the poor prognosis group (n=14). A total of 104 healthy full-term newborns born at the same time were selected as the control group. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of miR-138-5p and miR-574-5p in serum. ROC curve was used to analyze the diagnostic value of serum miR-574-5p and miR-138-5p levels for poor prognosis of infants with nARDS. Multivariate Logistic regression analysis of factors affecting the poor prognosis of infants with nARDS. Results Compared with the control group,the expression level of serum miR-574-5p in the observation group increased (1.72±0.23 vs 1.04±0.17),with the expression level of miR-138-5p decreased (0.55±0.08 vs 1.02±0.16),with statistically significant differences (t=24.557,25.597,all P<0.05). Compared with the good prognosis group,the serum miR-138-5p expression level in the poor prognosis group decreased (0.41±0.06 vs 0.57±0.08),with the miR-574-5p expression level increased (2.07±0.25 vs 1.67±0.19),with statistically significant differences (t=7.188,7.069,all P<0.05). The age,intrauterine distress,and proportion of abnormal amniotic fluid in the poor prognosis group were higher than those in the good prognosis group (t=5.359,4.257,6.577,all P<0.05). MiR-574-5p was an independent risk factor for poor prognosis with nARDS,and miR-138-5p was a protective factor (all P<0.05).The AUC (95％ CI) of serum miR-138-5p and miR-574-5p alone and combined diagnosis of nADRS were 0.793 (0.706～0.864),0.898 (0.826～0.947) and 0.931 (0.867～0.970),respectively. The combined prediction of miR-138-5p and miR-574-5p were better than that of miR-138-5p alone and miR-574-5p alone (Z=2.151,1.982,all P<0.05). Conclusion The expression levels of miR-138-5p in serum of infant with nARDS were lower and the expression levels of miR-574-5p were higher,both of which have certain diagnostic value for poor prognosis of infant with nARDS .

Objective:To develop a self-efficacy scale of milk donation and test its reliability and validity, in order to provide a scientific evaluation tool for the corresponding study.Methods:According to Bandura′s self-efficacy theory and consensus-based standards for the selection of health measurement instruments, the scale was developed by means of literature search, article pool establishment and expert letter consultation. A pre-survey was conducted on 30 newborn bereaved women, a formal investigation was conducted on 231 newborn bereaved women, and 115 newborn bereaved women were selected for exploratory factor analysis. Confirmatory factor analysis was performed on 116 parturients with neonatal loss to determine the reliability and validity of the scale.Results:The final scale includes 3 dimensions and 13 items. Three common factors were extracted by exploratory factor analysis, and the cumulative variance contribution rate was 77.962%. A scale with three dimensions, included breast milk donation resilience, breast milk donation cognition and breast milk donation motivation, and 13 items was determined. Confirmatory factor analysis showed that the model fit of the scale was good ( χ2/ df = 1.390, RMSEA = 0.063, RMR = 0.046, NFI = 0.924, NNFI = 0.971, GFI = 0.924, CFI = 0.977). The content validity index was 0.835. Cronbach′s α coefficient of the total volume table was 0.919, and the coefficients of each dimension were 0.892, 0.905 and 0.844, respectively. The broken half reliability of the scale was 0.893, and the broken half reliability of each dimension was 0.857, 0.881 and 0.711, respectively. The retest reliability of the scale was 0.814, and the retest reliability of each dimension was 0.803, 0.825 and 0.767, respectively. Conclusions:The scale has good reliability and validity, and can be used to evaluate the self-efficacy of milk donation in lost newborns.

This study employed the "disease-medicine-dose" pattern to mine the medication rules of traditional Chinese medicine(TCM) prescriptions containing Astragali Radix in ancient Chinese medical books, aiming to provide a scientific basis for the clinical application of Astragali Radix and the development of new medicines. The TCM prescriptions containing Astragali Radix were retrieved from databases such as Chinese Medical Dictionary and imported into Excel 2020 to construct the prescription library. Statical analysis were performed for the prescriptions regarding the indications, syndromes, medicine use frequency, herb effects, nature and taste, meridian tropism, dosage forms, and dose. SPSS statistics 26.0 and IBM SPSS Modeler 18.0 were used for association rules analysis and cluster analysis. A total of 2 297 prescriptions containing Astragali Radix were collected, involving 233 indications, among which sore and ulcer, consumptive disease, sweating disorder, and apoplexy had high frequency(>25), and their syndromes were mainly Qi and blood deficiency, Qi and blood deficiency, Yin and Yang deficiency, and Qi deficiency and collateral obstruction, respectively. In the prescriptions, 98 medicines were used with the frequency >25 and they mainly included Qi-tonifying medicines and blood-tonifying medicines. Glycyrrhizae Radix et Rhizoma, Angelicae Sinensis Radix, Ginseng Radix et Rhizoma, Atractylodis Macrocephalae Rhizoma, and Citri Reticulatae Pericarpium were frequently used. The medicines with high frequency mainly have warm or cold nature, and sweet, pungent, or bitter taste, with tropism to spleen, lung, heart, liver, and kidney meridians. In the treatment of sore and ulcer, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to promote granulation and heal up sores. In the treatment of consumptive disease, Astragali Radix was mainly used with the dose of 37.30 g and combined with Ginseng Radix et Rhizoma to tonify deficiency and replenish Qi. In the treatment of sweating disorder, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to consolidate exterior and stop sweating. In the treatment of apoplexy, Astragali Radix was mainly used with the dose of 7.46 g and combined with Glycyrrhizae Radix et Rhizoma to dispell wind and stop convulsions. Astragali Radix can be used in the treatment of multiple system diseases, with the effects of tonifying Qi and ascending Yang, consolidating exterior and stopping sweating, and expressing toxin and promoting granulation. According to the manifestations of different diseases, when combined with other medicines, Astragali Radix was endowed with the effects of promoting granulation and healing up sores, tonifying deficiency and Qi, consolidating exterior and stopping sweating, and dispelling wind and replenishing Qi. The findings provide a theoretical reference and a scientific basis for the clinical application of Astragali Radix and the development of new medicines.
Drugs, Chinese Herbal/history* ; Humans ; Medicine, Chinese Traditional/history* ; History, Ancient ; Astragalus Plant/chemistry* ; China ; Astragalus propinquus

Objective To propose a Weka-based method for classifying acute lymphoblastic leukemia(ALL)images,aiming to improve ALL cell classification accuracy and stability.Methods Firstly,totally 180 images were randomly selected from ALL-IDB2 subset of Acute Lymphoblastic Leukemia Image Database(ALL-IDB),including 90 images of patients and 90 images of healthy people;secondly,the image preprocessing was carried out using ImageJ software and image features were extracted such as texture,edge and shape;thirdly,image classification was implemented with four classifiers of Weka,including random forest(RF),Bayesian network(BN),J48 decision tree and sequential minimal optimization(SMO),and the key parameters of each classifier were optimized;finally,the performance of the classifiers was verified using 80 independent test images.Results Before parameter optimization,the accuracy of RF,J48 decision tree,BN and SMO classifiers was 94.3％,86.2％,83.6％and 83.0％,respectively.After optimization,the accuracy increased to 95.2％,86.3％,86.3％and 89.7％,respectively.After optimization,RF behaved the best on the independent test set with a classification accuracy of 90.0％,followed by SMO(81.3％),BN(81.3％)and J48 decision tree(75.0％).Conclusion The Weka-based ALL image classification method with a high accuracy is efficient and reliable for automated classification of ALL cell.[Chinese Medical Equipment Journal,2025,46(2):10-15]

Objective To investigate the changes of serum microRNA (miR)-138-5p and microRNA(miR)-574-5p expression levels and their clinical significance in newborns acute respiratory distress syndrome (nARDS). Methods A total of 112 infants with nARDS from September 2018 to August 2023 in the Neonatal Department of Xiaogan Hospital Affiliated to Wuhan University of Science and Technology were collected as the observation group. They were divided into good prognosis group (n=98) and the poor prognosis group (n=14). A total of 104 healthy full-term newborns born at the same time were selected as the control group. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of miR-138-5p and miR-574-5p in serum. ROC curve was used to analyze the diagnostic value of serum miR-574-5p and miR-138-5p levels for poor prognosis of infants with nARDS. Multivariate Logistic regression analysis of factors affecting the poor prognosis of infants with nARDS. Results Compared with the control group,the expression level of serum miR-574-5p in the observation group increased (1.72±0.23 vs 1.04±0.17),with the expression level of miR-138-5p decreased (0.55±0.08 vs 1.02±0.16),with statistically significant differences (t=24.557,25.597,all P<0.05). Compared with the good prognosis group,the serum miR-138-5p expression level in the poor prognosis group decreased (0.41±0.06 vs 0.57±0.08),with the miR-574-5p expression level increased (2.07±0.25 vs 1.67±0.19),with statistically significant differences (t=7.188,7.069,all P<0.05). The age,intrauterine distress,and proportion of abnormal amniotic fluid in the poor prognosis group were higher than those in the good prognosis group (t=5.359,4.257,6.577,all P<0.05). MiR-574-5p was an independent risk factor for poor prognosis with nARDS,and miR-138-5p was a protective factor (all P<0.05).The AUC (95％ CI) of serum miR-138-5p and miR-574-5p alone and combined diagnosis of nADRS were 0.793 (0.706～0.864),0.898 (0.826～0.947) and 0.931 (0.867～0.970),respectively. The combined prediction of miR-138-5p and miR-574-5p were better than that of miR-138-5p alone and miR-574-5p alone (Z=2.151,1.982,all P<0.05). Conclusion The expression levels of miR-138-5p in serum of infant with nARDS were lower and the expression levels of miR-574-5p were higher,both of which have certain diagnostic value for poor prognosis of infant with nARDS .

Objective To propose a Weka-based method for classifying acute lymphoblastic leukemia(ALL)images,aiming to improve ALL cell classification accuracy and stability.Methods Firstly,totally 180 images were randomly selected from ALL-IDB2 subset of Acute Lymphoblastic Leukemia Image Database(ALL-IDB),including 90 images of patients and 90 images of healthy people;secondly,the image preprocessing was carried out using ImageJ software and image features were extracted such as texture,edge and shape;thirdly,image classification was implemented with four classifiers of Weka,including random forest(RF),Bayesian network(BN),J48 decision tree and sequential minimal optimization(SMO),and the key parameters of each classifier were optimized;finally,the performance of the classifiers was verified using 80 independent test images.Results Before parameter optimization,the accuracy of RF,J48 decision tree,BN and SMO classifiers was 94.3％,86.2％,83.6％and 83.0％,respectively.After optimization,the accuracy increased to 95.2％,86.3％,86.3％and 89.7％,respectively.After optimization,RF behaved the best on the independent test set with a classification accuracy of 90.0％,followed by SMO(81.3％),BN(81.3％)and J48 decision tree(75.0％).Conclusion The Weka-based ALL image classification method with a high accuracy is efficient and reliable for automated classification of ALL cell.[Chinese Medical Equipment Journal,2025,46(2):10-15]

Objective:To develop a self-efficacy scale of milk donation and test its reliability and validity, in order to provide a scientific evaluation tool for the corresponding study.Methods:According to Bandura′s self-efficacy theory and consensus-based standards for the selection of health measurement instruments, the scale was developed by means of literature search, article pool establishment and expert letter consultation. A pre-survey was conducted on 30 newborn bereaved women, a formal investigation was conducted on 231 newborn bereaved women, and 115 newborn bereaved women were selected for exploratory factor analysis. Confirmatory factor analysis was performed on 116 parturients with neonatal loss to determine the reliability and validity of the scale.Results:The final scale includes 3 dimensions and 13 items. Three common factors were extracted by exploratory factor analysis, and the cumulative variance contribution rate was 77.962%. A scale with three dimensions, included breast milk donation resilience, breast milk donation cognition and breast milk donation motivation, and 13 items was determined. Confirmatory factor analysis showed that the model fit of the scale was good ( χ2/ df = 1.390, RMSEA = 0.063, RMR = 0.046, NFI = 0.924, NNFI = 0.971, GFI = 0.924, CFI = 0.977). The content validity index was 0.835. Cronbach′s α coefficient of the total volume table was 0.919, and the coefficients of each dimension were 0.892, 0.905 and 0.844, respectively. The broken half reliability of the scale was 0.893, and the broken half reliability of each dimension was 0.857, 0.881 and 0.711, respectively. The retest reliability of the scale was 0.814, and the retest reliability of each dimension was 0.803, 0.825 and 0.767, respectively. Conclusions:The scale has good reliability and validity, and can be used to evaluate the self-efficacy of milk donation in lost newborns.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.