Association between toll-like receptor 6 expression and auxiliary T cells in the peripheral blood of pediatric patients with allergic purpura

Chang, Hong; Cao, Yongbing; Lin, Yi; Zhu, Haiyan; Fu, Yuan; Chen, Xiuxia; Zhang, Qiuye

doi:10.3892/etm.2015.2710

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…Subsequently, 4 conference abstracts and 16 studies with no data of interest were excluded, at last, with 25 articles included in the present meta-analysis. [2,13,14,21–43] Among all the included studies, 24 were retrospective studies and 1 were case-control analysis. The main baseline characteristics of the included publications are at length provided in Table 1 and Table 2.…”

Section: Resultsmentioning

confidence: 99%

The change of Th17/Treg cells and IL-10/IL-17 in Chinese children with Henoch–Schonlein purpura

Li¹,

Ren

Ling³

et al. 2019

Medicine

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Background:To date, the relationship of Th17 and Treg cells to Henoch–Schonlein purpura (HSP) in children remains controversial. Therefore, a systematic review and meta-analysis was conducted to reveal the potential role of the Th17 and Treg cells in children in acute stage of HSP.Methods:PubMed, Embase, Web of Science and China National Knowledge Internet (CNKI) were systematically searched for eligible studies up to November 03, 2017. Quality assessment was carried out according to the modification of the Newcastle-Ottawa Scale (NOS). The data were analyzed by Stata SE12.0 (StataCorp, College Station, TX). Standard mean difference (SMD) with 95% confidence intervals (CI) was calculated continuous data.Results:A total of 25 eligible studies were identified after a thorough literature search. The pooled results of the meta-analysis showed that values of Th17 frequency (SMD = 2.60; 95% CI: 1.98 to 3.23; P < .0001; I2 = 90.3%, P < .0001) and IL-17 level (SMD = 3.53; 95% CI: 2.71 to 4.35; P < .0001; I2 = 95.6%, P < .001) were significantly higher in children with HSP as compared to healthy children. In contrast, our analysis showed significant lower values of Treg frequency (SMD = -2.86; 95% CI: -3.53 to -2.19; P < .001; I2 = 92.4%, P < .001). However, no significance of IL-10 level was observed between children with HSP and healthy children (SMD = -1.22; 95% CI: -2.78 to 0.33; P < .01; I2 = 95.9%, P < .001).Conclusion:In conclusion, our meta-analysis indicated that increased frequency of Th17 cells and level of IL-17, but lower frequency of Treg cells are associated with HSP in childhood. Considering the limitations of this meta-analysis, large-scaled studies need to be conducted to validate the current results.

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Section: Resultsmentioning

confidence: 99%

The change of Th17/Treg cells and IL-10/IL-17 in Chinese children with Henoch–Schonlein purpura

Li¹,

Ren

Ling³

et al. 2019

Medicine

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“…As studies have shown, infection is the most common cause of HSP, and about 40%-70% of children are mainly affected by respiratory tract infection [25][26] .Ma et al [17] showed that the increase of WBC was one of the independent risk factors for HSPN (P < 0.05). Chang et al [27][28] believed that the mechanism may be tissue damage caused by in ammatory mediators secreted by neutrophils, resulting in swelling and necrosis of renal vascular endothelium, while activated substances such as oxygen free radicals can chemotactic more WBC, aggravate vascular injury and form a vicious circle. EBV belongs to the γ subfamily of Herpesviridae, which is a linear double-strand DNA virus, and human is its only natural host.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

Wang

Pan

2022

Preprint

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Background This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with Henoch-Schönlein Purpura, and to construct a predictive model of Henoch-Schönlein Purpura Nephritis in children and analyze the related risk factors of Henoch-Schönlein Purpura Nephritis in children. Methods Case data of 288 hospitalized children with Henoch-Schönlein Purpura from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and Logistic regression, support vector machine, decision tree and random forest algorithm were used respectively for classification prediction. Last, the performance of four algorithms are compared using accuracy rate and recall rate. Results The accuracy rate, recall rate and AUC of the established random forest model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the Logistic regression model; higher than 0.70, 0.80 and 0.89 of support vector machine model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by random forest model are Persistent purpura≥4weeks, Cr, Clinic time, ALB, WBC, TC, TG, Relapse, TG, Recurrent purpura and EB-DNA. Conclusion The model based on random forest algorithm has better performance in the prediction of children with allergic purpura renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

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“…[22,23] Ma et al [16] showed that the increase of WBC was one of the independent risk factors for IgAVN (P < .05). Chang et al [24,25] believed that the mechanism may be tissue damage caused by inflammatory mediators secreted by neutrophils, resulting in swelling and necrosis of renal vascular endothelium, while activated substances such as oxygen free radicals can chemotactic more WBC, aggravate vascular injury and form a vicious circle. EBV belongs to the γ subfamily of Herpesviridae, which is a linear double-strand DNA virus, and human body is its only natural host.…”

Section: Discussionmentioning

confidence: 99%

Prediction of renal damage in children with IgA vasculitis based on machine learning

Wang¹,

Pan²

2022

Medicine

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This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with IgA vasculitis by constructing a predictive model and analyzing the related risk factors of IgA vasculitis Nephritis in children. Case data of 288 hospitalized children with IgA vasculitis from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and logistic regression, support vector machine (SVM), decision tree and random forest (RF) algorithms were used separately for classification prediction. Lastly, the performance of four algorithms is compared using accuracy rate, recall rate and AUC. The accuracy rate, recall rate and AUC of the established RF model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the logistic regression model; higher than 0.70, 0.80 and 0.89 of SVM model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by RF model are: Persistent purpura ≥4 weeks, Cr, Clinic time, ALB, WBC, TC, Relapse, TG, Recurrent purpura and EB-DNA. The model based on RF algorithm has better performance in the prediction of children with IgA vasculitis renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

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Association between toll-like receptor 6 expression and auxiliary T cells in the peripheral blood of pediatric patients with allergic purpura

Cited by 10 publications

References 29 publications

The change of Th17/Treg cells and IL-10/IL-17 in Chinese children with Henoch–Schonlein purpura

The change of Th17/Treg cells and IL-10/IL-17 in Chinese children with Henoch–Schonlein purpura

Prediction of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

Prediction of renal damage in children with IgA vasculitis based on machine learning

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