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早产相关细胞因子:新的诊断算法建议。

Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm.

机构信息

Institute of Obstetric and Emergency Medicine, Faculty of Medicine, University of Rzeszow, Ul. Pigonia 6, 35-310 Rzeszów, Poland.

Centre for Innovative Research in Medical and Natural Sciences', Faculty of Medicine, University of Rzeszow, Ul. Warzywna 1a, 35-959 Rzeszów, Poland.

出版信息

J Immunol Res. 2018 Apr 8;2018:8073476. doi: 10.1155/2018/8073476. eCollection 2018.

Abstract

Predicting preterm delivery within 7 days is very important for the proper timing of glucocorticosteroid administration. If within 7 days after glucocorticosteroid administration, the delivery does not occur, it remains questionable if repeated glucocorticosteroid therapy results in improved infant respiratory function. Therefore, differentiation of preterm delivery from false preterm delivery is clinically significant. The aim of this study was to create a diagnostic algorithm to distinguish preterm delivery from false preterm delivery on the basis of concentrations of selected cytokines. The study group ( = 622) were patients hospitalized due to threatened preterm delivery. To assess the concentration of cytokines in the serum, we used a multiplex method, which allows simultaneous determination of 13 cytokines. The sets consist of the following cytokines: IGFBP-1, IGFBP-2, BDNF, L-Selectin, E-Selectin, ICAM-1, PECAM, VCAM-1, MIP-1d, MIP-3b, Eotaxin-1, Eotaxin-2, and BLC. In the study group, 67.8% patients had preterm delivery and 32.2% had false preterm delivery. Based on the analysis of cytokine concentrations, a classification tree to distinguish between preterm delivery and false preterm delivery was created. Our findings show the possibility of prediction of preterm delivery with the use of a classification and regression tree of selected cytokine concentration.

摘要

在 7 天内预测早产对于糖皮质激素给药的时机非常重要。如果在糖皮质激素给药后 7 天内未发生分娩,那么重复使用糖皮质激素治疗是否会改善婴儿的呼吸功能仍然存在疑问。因此,区分早产和假性早产在临床上具有重要意义。本研究旨在基于选定细胞因子的浓度创建一个诊断算法,以区分早产和假性早产。研究组(=622)为因早产威胁而住院的患者。为了评估血清中细胞因子的浓度,我们使用了一种多重方法,该方法允许同时测定 13 种细胞因子。试剂盒包括以下细胞因子:IGFBP-1、IGFBP-2、BDNF、L-选择素、E-选择素、ICAM-1、PECAM、VCAM-1、MIP-1d、MIP-3b、Eotaxin-1、Eotaxin-2 和 BLC。在研究组中,67.8%的患者发生早产,32.2%的患者发生假性早产。基于细胞因子浓度分析,创建了一个用于区分早产和假性早产的分类树。我们的研究结果表明,使用选定细胞因子浓度的分类和回归树可以预测早产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9ae/5911331/f1d968d9a176/JIR2018-8073476.001.jpg

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