BackgroundConnective tissue diseases (CTDs) are a group of diseases with a variety of clinical manifestations. The main drug was glucocorticoids and immunosuppressive drugs, but the results are not satisfactory and the side effects are obvious, increased the incidence of infection, especially opportunistic infections. Infections becomes important causes of morbidity and mortality in CTD patients.ObjectivesTo evaluate the incidence of infection in CTD patients who were clinically considered for co-infection by a combination of metagenomic next-generation sequencing (mNGS) and conventional diagnostic testing methods.MethodsWe analyzed 126 connective tissue diseases (CTD)patients with suspected infections admitted to The Second Hospital of Shanxi Medical University. All patients with CTD were diagnosed according to relevant diagnostic criteria, including 34 systemic lupus erythematosus (SLE), 24 dermatomyositis and polymyositis (DM/PM), 19 rheumatoid arthritis (RA), 10 undifferentiated connective tissue disease(UCTD),16 Sjogren syndrome (SS), 5 mixed connective tissue disease (MCTD), 5 ANCA associated systemtc vasculitis (AAV), 5 adult onset Stillystemtc isease disease(tic criteria, including 34nfections admitted to The Second Hospital of Shanxi (TA), 1 systemic sclerosis (SSC), 1retroperitoneal fibrosis (RPF). All enrolled patients were tested for conventional diagnostic testing methods(CDT) and mNGS.ResultsAmong the 126 patients with CTD who were clinically considered for co-infection, 31 patients were negative for mNGS, and pathogens were detected in 99 of them. In our results, the mNGS and CDT were both positive for pathogens detection in 28 individuals.Of both positive individuals, 2 cases were perfect matches,12 cases were partly matched, 14 cases were totally mismatched. A total of 23 cases were negative for both mNGS and CDT. 70 cases were positive for mNGS only.There were only 5 cases positive for pathogens detection by CDT only. In addition, the results of mNGS showed that 131 patients were virus-positive(54%), 78 patients were prokaryotes-positive (37%) inculding bacteria, mycoplasma and 14 patients were eukaryotes-positive (9%). Of course, someones have mixed infections among these patinets some of these patients, with two or more pathogens. In the mixed infection, 5 cases have no viruses infection, 38 cases with virus infection, including 20 cases of bacteria and viruses infection, 4 cases of bacteria,fungi and viruses infection, 9 cases of viruses mixed infection, 1 case of bacteria,viruses,fungi and mycoplasma infection, 1 case of bacteria,viruses and mycoplasma infection, 1 case of viruses and mycoplasma infection, 1 case of viruses and fungi infection. According to the results, viruses were the most common pathogens identified, followed by prokaryotes and eukaryotes. It is noteworthy that the incidence of Human gammaherpesvirus 4(EBV), Human betaherpesvirus 5(CMV) and Human alphaherpesvirus 1 are more common in virus-positive. The most frequently detected prokaryotes were Acinetobacter baumannii, Mycobacterium tuberculosis complex, followed by Staphylococcus aureus, Prevotella melaninogenica,Staphylococcus homini and Helicobacter pylori. The major pathogens were Pneumocystis jirovecii and Candida albicans among eukaryotes-positive individuals.ConclusionAs a complementary approach to conventional methods, mNGS could help improving the identification of infection in CTD patients.The incidence of viral infection is high in patients with connective tissue disease and close attention should be paid to it in clinical works.Figure 1.A. Comparison of test results between mNGS and conventional diagnostic testing methods(CDT) in CTD patients. B. The classification of mixed infections with or without viruses infection detected by mNGS and conventional diagnostic testing methods(CDT).Figure 2.Distribution of pathogens detected by mNGS. A. Type distribution of pathogens identified by mNGS. Species distribution of viruses of B.viruses, C.Prokayote, D. Eucayon detected by mNGS.Disclosure of InterestsNone declared

BackgroundPsoriatic arthritis (PsA) has been linked to an increased risk of metabolic syndrome (MetS). Non-alcoholic fatty liver disease (NAFLD), the hepatic manifestation of MetS, is now the commonest liver disease worldwide. About 65% of PsA patients suffer from NAFLD, and chronic systemic inflammation may be an important predisposing factor.ObjectivesThe purpose of this study was to establish and validate a diagnostic model nomogram for predicting the occurrence of NAFLD in patients with PsA.MethodsA total of 127 PsA patients (46 had NAFLD and 81 had no NAFLD) were enrolled in this study. Retrospectively collected clinical and serological parameters of these patients. The percentage and absolute number of lymphocytes and CD4+T cells were determined by Flow cytometry. The independent risk factors for NAFLD were screened in the PsA patients using univariate and multivariate binary logistic regression analyses and were used for construction of the nomogram prediction model. The AUROC (C index) was used to verify the model discrimination; the calibration curve and Hosmer-Lemeshow test were used to verify the model calibration; and the DCA curve was used to verify the clinical validity of the model.ResultsUnivariate and multivariate logistic regression analyses showed that Body Mass Index (BMI) (OR=1.25, P=0.001), serum triglyceride (TG) (OR=3.51，P=0.015) and peripheral blood Th1 cell percentage (OR=1.12, P < 0.001) is an independent risk factor for NAFLD in PsA patients, and an individualized nomogram prediction model was successfully established. The prediction model had a good discrimination power with AUROC (C-index) of 0.83 (95% CI: 0.76-0.90); the P value in the Hosmer-Lemeshow test was 0.683, suggesting a high reliability of the predicted probability by the model; the DCA curve indicating a good clinical efficiency of the model.ConclusionOur study shows that the establishment of a nomogram prediction model of PsA complicated with NAFLD patients is helpful for early clinical screening and identification of such high-risk patients.Figure 1.A. Example of prediction nomogram for risk of PsA complicated with NAFLD patients; B. The ROC curve of the prediction model; C. The calibration curve of the prediction model; D. The decision curve analysis of the prediction model.Disclosure of InterestsNone declared