Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*

Humans ; Lupus Vasculitis, Central Nervous System/pathology* ; Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods* ; Patients ; Lupus Erythematosus, Systemic/pathology* ; Brain/pathology*

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.

Immunoglobulin G4-related disease (IgG4-RD) is a newly recognized chronic fibro-inflammatory autoimmune disease, and its recognition has been constantly increasing worldwide over the last few years. A correct and timely recognition, as well as appropriate intervention, is crucial for the treatment of IgG4-RD. For certain subtypes of IgG4-RD, organ-specific criteria are formulated to make the diagnosis more accurate. New biomarkers have emerged in the recent years to aid the disease diagnosis, its prognosis prediction, as well as therapy response monitoring. Although recurrence is very common in IgG4-RD, glucocorticoid is still the first-line treatment for the majority of patients. The factors that affect the likelihood of disease relapse are multifaceted. The selection strategy of various steroid-sparing agents is still being explored. Besides, when patients have special sites involvement leading to severe clinical conditions, surgical operation or interventional therapy should also be considered. An update on classification, diagnosis, and management of IgG4-RD is provided in the current study to fully elucidate the recommended clinical practice of this mysterious disease.
Autoimmune Diseases/drug therapy* ; Biomarkers ; Glucocorticoids/therapeutic use* ; Humans ; Immunoglobulin G ; Immunoglobulin G4-Related Disease/drug therapy*

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.

BACKGROUND:Poly(D,L-lactide-co-glycolide)(PLGA)may produce lactic acid and glycolic acid when PLGA degrades,thus leading to the acid accumulation and inducing the inflammatory reaction locally.OBJECTIVE:To investigate the effect of lysine,histidine and arginine on regulating the acid accumulation of PLGA copolymer during the degradation.DESIGN:Repeated measurement and experiment.TIME AND SETTING:The experiments were performed in the College of Bioengineering.Chongqing University from July 2006 to August 2007.MATERIALS:PILGA(80:20)was produced by Sigma(USA);Lysine,histidine and arginine(purlty>99%)were purchased from Sigma(USA);Chitosan(deacetylation degree:85%)was purchased from Chengdu Kelong Chemical Reagent Factory;Algin(viscosity:1.05-1.15)was purchased from Tianiin Damao Chemical Reagent Factory.METHODS:Lysine,histidine and arginine were added into PLGA with the proportion of 5%(w/v)and 10%(w/v)respectively.The resultant film sample was put into a bottle with tri-distilled-water for 2-month degradation at 37℃.The pH value of degradation solution was detected by pH meter;Each film sample was taken out and lyophilized 12 hours to get its dry weight and calculate mass loss ratio.Each variety of the solution was sampled three specimens,the average pH value,average initial weight and average finial weight of these three specimens were taken as the indices at each sampling time point,respectively.Accordingly,the regulation effect of basic amino acid was comparexl with that of algin,chitosan and NaHCO3.MAIN OUTCOME MEASURES:The changes of pH value of degradation solution;the mass loss ratio of the composite.RESULTS:Each basic additive could relieve the acid accumulation,among them,NaHCO3 was the strongest,while algin and chitosan showed a lowest capacity,basic amino acid was moderate;The suitable regulation effect could be achieved at a level of 5%lysine.CONCLUSION:Basic amino acid can effectively regulate the acid accumulation after PLGA degrades,and the optimal concentration of lysine is 5%.