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Updated: Nov 12, 2025
The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice
Published on: November 1, 2015
Development of Prediction Models for New Integrated Models and a Bioscore System to Identify Bacterial Infections in
Xvwen Zhai1, Min Feng2, Hui Guo3,4
1Clinical Skills Teaching Simulation Hospital, Shanxi Medical University, Jinzhong, China.
Objectives:
Distinguishing flares from bacterial infections in systemic lupus erythematosus (SLE) patients remains a challenge. This study aimed to build a model, using multiple blood cells and plasma indicators, to improve the identification of bacterial infections in SLE.
Design:
Building PLS-DA/OPLS-DA models and a bioscore system to distinguish bacterial infections from lupus flares in SLE.
Setting:
Department of Rheumatology of the Second Hospital of Shanxi Medical University.
Participants:
SLE patients with flares (n = 142) or bacterial infections (n = 106) were recruited in this retrospective study.
Outcome:
The peripheral blood of these patients was collected by the experimenter to measure the levels of routine examination indicators, immune cells, and cytokines. PLS-DA/OPLS-DA models and a bioscore system were established.
Results:
Both PLS-DA (R2Y = 0.953, Q2 = 0.931) and OPLS-DA (R2Y = 0.953, Q2 = 0.942) models could clearly identify bacterial infections in SLE. The white blood cell (WBC), neutrophile granulocyte (NEUT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), IL-10, interferon-γ (IFN-γ), and tumor necrosis factor α (TNF-α) levels were significantly higher in bacteria-infected patients, while regulatory T (Treg) cells obviously decreased. A multivariate analysis using the above 10 dichotomized indicators, based on the cut-off value of their respective ROC curve, was established to screen out the independent predictors and calculate their weights to build a bioscore system, which exhibited a strong diagnosis ability (AUC = 0.842, 95% CI 0.794-0.891). The bioscore system showed that 0 and 100% of SLE patients with scores of 0 and 8-10, respectively, were infected with bacteria. The higher the score, the greater the likelihood of bacterial infections in SLE.
Conclusions:
The PLS-DA/OPLS-DA models, including the above biomarkers, showed a strong predictive ability for bacterial infections in SLE. Combining WBC, NEUT, CRP, PCT, IL-6, and IFN-γ in a bioscore system may result in faster prediction of bacterial infections in SLE and may guide toward a more appropriate, timely treatment for SLE.
