Scholarlink

Background <p>This study aimed to assess the predictive value of plasma interleukin-17&#xa0;A (IL-17&#xa0;A), interleukin-10 (IL-10), interferon-γ (IFN-γ), along with laboratory parameters and clinical manifestations, for identifying severe illness in children with Epstein-Barr virus (EBV)-induced infectious mononucleosis (IM).</p> Methods <p>Peripheral blood samples were collected from 90 children diagnosed with EBV-induced IM. The patients were divided into a non-severe group (<i>n</i> = 66) and a severe group (<i>n</i> = 24). Plasma levels of IL-17&#xa0;A, IL-10, and IFN-γ were measured using enzyme-linked immunosorbent assay (ELISA). Using severe EBV-IM as the outcome, the clinical characteristics, laboratory parameters, immune function markers, and expression levels of three cytokines were first compared between the two groups. Indicators with a <i>P</i> value of less than 0.05 in the univariable analysis were selected using stepwise regression and included in the binary logistic regression analysis. The independent risk factors identified by the regression model were used to construct a nomogram. Internal validation was performed using the bootstrap resampling method. Calibration of the model was assessed using a calibration curve, and the clinical net benefit was evaluated through decision curve analysis (DCA).</p> Results <p>Binary logistic regression analysis identified IL-10, IL-17&#xa0;A, Aspartate aminotransferase (AST), Glutamyl transpeptidase (GGT), and splenomegaly as independent risk factors for severe EBV-IM (<i>P</i> &lt; 0.05). A nomogram was constructed by incorporating the significant predictors from the logistic regression analysis: IL-10, IL-17&#xa0;A, AST, GGT, and splenomegaly. Internal validation using the Bootstrap resampling method indicated good discriminative ability of the model. The calibration curve suggested satisfactory agreement between predicted and observed probabilities. Furthermore, DCA confirmed the favorable clinical net benefit of this predictive model.</p> Conclusion <p>The nomogram incorporating IL-10, IL-17&#xa0;A, AST, GGT, and splenomegaly demonstrates substantial diagnostic value for identifying severe IM. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

IL-10, IL-17A, and IFN-γ as clinical early-warning indicators for severe Epstein-Barr virus-associated infectious mononucleosis in children

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月17日

查看全文
  • Yingying Ye,
  • Meng Cao,
  • Yuewen Su,
  • Weifang Zhou,
  • Yuqin Li

摘要

Background

This study aimed to assess the predictive value of plasma interleukin-17 A (IL-17 A), interleukin-10 (IL-10), interferon-γ (IFN-γ), along with laboratory parameters and clinical manifestations, for identifying severe illness in children with Epstein-Barr virus (EBV)-induced infectious mononucleosis (IM).

Methods

Peripheral blood samples were collected from 90 children diagnosed with EBV-induced IM. The patients were divided into a non-severe group (n = 66) and a severe group (n = 24). Plasma levels of IL-17 A, IL-10, and IFN-γ were measured using enzyme-linked immunosorbent assay (ELISA). Using severe EBV-IM as the outcome, the clinical characteristics, laboratory parameters, immune function markers, and expression levels of three cytokines were first compared between the two groups. Indicators with a P value of less than 0.05 in the univariable analysis were selected using stepwise regression and included in the binary logistic regression analysis. The independent risk factors identified by the regression model were used to construct a nomogram. Internal validation was performed using the bootstrap resampling method. Calibration of the model was assessed using a calibration curve, and the clinical net benefit was evaluated through decision curve analysis (DCA).

Results

Binary logistic regression analysis identified IL-10, IL-17 A, Aspartate aminotransferase (AST), Glutamyl transpeptidase (GGT), and splenomegaly as independent risk factors for severe EBV-IM (P < 0.05). A nomogram was constructed by incorporating the significant predictors from the logistic regression analysis: IL-10, IL-17 A, AST, GGT, and splenomegaly. Internal validation using the Bootstrap resampling method indicated good discriminative ability of the model. The calibration curve suggested satisfactory agreement between predicted and observed probabilities. Furthermore, DCA confirmed the favorable clinical net benefit of this predictive model.

Conclusion

The nomogram incorporating IL-10, IL-17 A, AST, GGT, and splenomegaly demonstrates substantial diagnostic value for identifying severe IM.