Background/Aims: Increased production of multiple pro-inflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, plays an essential pathogenic role in the progression of systemic lupus erythematosus (SLE). Recent studies have characterized itaconate as a novel and potent nuclear-factor-E2-related factor 2 (Nrf2) activator that activates Nrf2 signaling by alkylating cysteine residues on Keap1 (Kelch-like ECH-associated protein 1). Methods: THP-1 human macrophages and peripheral blood mononuclear cells (PBMCs) of SLE patients were treated with 4-octyl itaconate (OI). Nrf2 signaling activation was tested by qPCR assay and western blotting. mRNA expression and the production of multiple pro-inflammatory cytokines were tested by qPCR and enzyme-linked immunosorbent assays, respectively. Nuclear factor (NF)-κB activation was tested by the p65 DNA-binding assay. Results: We demonstrated that OI, the cell-permeable derivative of itaconate, induced Keap1-Nrf2 dissociation, Nrf2 protein accumulation, and nuclear translocation, which enabled the transcription and expression of multiple Nrf2-dependentantioxidant enzymes (heme oxygenase-1, NAD(P)H:quinone oxidoreductase 1, and glutamate-cysteine ligase modifier subunit) in THP-1 human macrophages. OI also induced significant Nrf2 activation in SLE patient-derived PBMCs. OI pretreatment inhibited mRNA expression and the production of multiple pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) in SLE patient-derived PBMCs and lipopolysaccharide (LPS)-activated THP-1 cells. OI potently inhibited NF-κB activation in SLE patient-derived PBMCs and LPS-activated THP-1 cells. Importantly, Nrf2 silencing (by targeted short hairpin RNA) or knockout (by CRISPR/Cas9 gene-editing method) almost abolished OI-induced anti-oxidant and anti-inflammatory actions in SLE patient-derived PBMCs and LPS-activated THP-1 cells. Conclusion: OI activates Nrf2 signaling to inhibit the production of pro-inflammatory cytokines in human macrophages and SLE patient-derived PBMCs. OI and itaconate could have important therapeutic value for the treatment of SLE.
Background
The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine learning algorithms have started being used to an increasing extent in disease diagnosis due to the high level of efficiency, objectivity, and accuracy that they offer.
Methods
Data samples on 192 patients with TPE, 54 patients with parapneumonic pleural effusion (PPE), and 197 patients with malignant pleural effusion (MPE) were retrospectively collected. Based on 28 different features obtained via statistical analysis, TPE diagnostic models using four machine learning algorithms (MLAs), namely logistic regression, k-nearest neighbors (KNN), support vector machine (SVM) and random forest (RF) were established and their respective diagnostic performances were calculated. The respective diagnostic performances of each of the four algorithmic models were compared with that of pleural fluid adenosine deaminase (pfADA). Based on 12 features with the most significant impacts on the accuracy of the RF model, a new RF model was designed for clinical application. To demonstrate its external validity, a prospective study was conducted and the diagnostic performance of the RF model was calculated.
Results
The respective sensitivity and specificity of each of the four TPE diagnostic models were as follows: logistic regression – 80.5 and 84.8%; KNN– 78.6 and 86.6%; SVM – 83.2 and 85.9%; and RF – 89.1 and 93.6%. The sensitivity and specificity of pfADA were 85.4 and 84.1%, respectively, at the best cut-off value of 17.5 U/L. RF was the superior method among the four MLAs, and was also superior to pfADA. The newly designed RF model (based on 12 out of 28 features) exhibited an acceptable performance rate for the diagnosis of TPE with a sensitivity and specificity of 90.6 and 92.3%, respectively. In the prospective study, its sensitivity and specificity were 100.0 and 90.0%, respectively.
Conclusions
Establishing a model for the diagnosis of TPE using RF resulted in a more effective, economical, and faster diagnostic method. This method could enable clinicians to diagnose and treat TPE more effectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.