Objective:To elucidate the correlation between peripheral blood levels of pentraxin 3 (PTX3) and fibroblast growth factors 2 (FGF2) and clinical manifestations, immunological indexes and disease activity of systemic lupus erythematosus (SLE) patients.Methods:The correlation between peripheral blood levels of PTX3 and FGF2 and clinical manifestations, immunological indexes and disease activity of SLE pa-tients was determined. T test, Mann-Whitney U test and Spearman's rank correlation coefficient were analyzed statistically. Results:Plasma PTX3 levels were significantly higher in SLE patients than in healthy controls (3 191±2 423) pg/ml vs (755±432) pg/ml, t=5.595, P<0.01) . The titer of PTX3 in patients with hematologic in-volvement was higher than that in the patients without [(3 810±2 840) pg/ml vs (2 493±1 830) pg/ml, t=2.008, P=0.049). Plasma PTX3 concentration in SLE patients was positively correlated not only with the level of 24 h urine protein ( r=0.498 6, P=0.005 9), but also with ESR ( r= 0.376, P=0.007) and systemic lupus erythematosus disease activity index (SLEDAI) scores ( r=0.405, P=0.003). On the contrast, plasma PTX3 concentration in SLE patients was negatively correlated with complement 3 ( r=-0.405, P=0.005). Increased serum PTX3 levels accompanied by increased serum FGF2 levels was observed. Plasma FGF2 concentration in SLE patients was positively correlated with SLEDAI scores ( r=0.326, P=0.019), but negatively correlated with level of comple-ment 3 ( r=-0.414, P=0.004) and complement 4 ( r=-0.451, P=0.007). Levels of FGF2 were higher in patients with positive anti-NuA antibody [(138±91) pg/ml vs (59±68) pg/ml, t=2.996, P=0.004 2), anti-dsDNA antibody [(120±96) pg/ml vs (56±58) pg/ml, t=3.583, P=0.000 7] and anti-rRNP antibody (151±109) pg/ml vs (63±61) pg/ml, t=3.757, P=0.000 4) than in patients with negative of these antibodies. Conclusion:The levels of PTX3 and FGF2 in peripheral blood may play a role in determining the disease activity and clinical phenotype of SLE, and can help doctors to make diagnosis and treatment decisions.

Objective:To investigate the molecular mechanism of the disease based on the clinical characterization and genetic mutation analysis in a family with intellectual disability.Methods:The proband with intellectual disability was diagnosed at Luohe Central Hospital in December 2019. Peripheral blood samples were collected from four family members. Whole exome sequencing (WES) was used to screen the pathological mutations. Then the PCR and Sanger sequencing were used to verify the selected mutations and combine the relevant database to analyze variation loci.Results:We infer that the ARX c.1162 A>G was co-segregated with the phenotype of the family based on the results of WES. The results of sanger sequencing and WES are consistent. The mother of the proband is the carrier of the mutation. There is no mutation frequency reported in the healthy population. The mutation of the ARX c.1162A>G is harmful inferred by a variety of bioinformatics software. Combined with the phenotypic analysis of OMIM database, we infer the phenotype caused by the mutation is consistent with the patients in the family.Conclusion:The mutation of the ARX c.1162 A>G may be the cause of the intellectual disability in the family affected. And the variant has not been reported in China.

Objective:To investigate the DMD genetic variants of the Chinese population with Duchenne (DMD) and Becker muscular dystrophies (BMD).Methods:A cross-sectional study was conducted on 2 690 unrelated patients with DMD and BMD aged 0-18 who visited the Genetic and Prenatal Diagnosis Center of the First Affiliated Hospital of Zhengzhou University from January 2005 to February 2022. The clinical data, such as gender, age, clinical manifestations, and address, were collected. Multiplex ligation-dependent probe amplification, next generation sequencing panel, Sanger sequencing, and PCR amplification were used to detect the variants of the DMD gene in the patients, whose clinical information and gene detection results were descriptively analyzed.Results:The 2 690 patients included 2 648 males and 42 females, with an age of 6.0 (4.0, 9.0) years. The serum creatine kinase increased in all patients. Pathogenic DMD gene variants were detected in the 2 618 patients, including 1 875 cases (71.6%) large deletions, 231 cases (8.8%) duplications, and 512 cases (19.6%) small variants. Among the deletion variants, the deletion of 3 exons was the most common, accounting for 15.4% (288/1 875); and hotspot deletion involved exons 45 to 50, accounting for 6.3% (119/1 875). Exon 2 was the most common type duplication region, accounting for 13.0% (30/231). Small variants were distributed in all 79 exons of the DMD gene, with no hotspots. In addition, the 46 small variants were previously unreported.Conclusion:Exon deletion is the most common type of DMD gene variant, followed by small variants and exon duplication.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.