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Author's reply

机译：作者的答复

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We would like to thank Dr. Dokos and Dr. Tsakalidis for their interest in our work and publication describing a study of metabolomics to identify biomarkers in the amniotic fluid of women in preterm labor which predict outcome in the presence or absence of intra-amniotic infection/inflammation [1]. Metabolomics and other high-dimensional biology approaches (genomics, transcriptomics, metabolomics, lipido-mics, glycomics, etc.) [2,3] represent discovery platforms to identify differences in the composition of biological samples (e.g. serum or plasma, urine, cerebrospinal fluid, amniotic fluid, tissues, etc.) among patients, typically between healthy individuals and those with disease [4-7]. However, high-dimensional biology techniques could also be used to classify patients with a particular clinical entity into those with good and bad prognosis [8], or even uncover the degree of heterogeneity of a particular entity [9]. In the case of our study, we examined the amniotic fluid metabolome among women with preterm labor who delivered at term and those who delivered preterm with and without intra-amniotic infection/inflammation.
机译：我们要感谢Dokos博士和Tsakalidis博士对我们的工作和出版物感兴趣，该出版物和出版物描述了一项代谢组学研究，以鉴定早产妇女羊水中的生物标志物，该标志物可预测是否存在羊膜内感染的结果/发炎[1]。代谢组学和其他高维生物学方法（基因组学，转录组学，代谢组学，脂质组学，糖组学等）[2,3]代表了发现平台，可识别生物学样品（例如血清或血浆，尿液，脑脊髓）的成分差异患者之间的输液，羊水，组织等），通常是在健康个体与患有疾病的个体之间[4-7]。然而，高维生物学技术也可用于将具有特定临床实体的患者分为预后好的和不良的患者[8]，甚至揭示特定实体的异质性程度[9]。在我们的研究中，我们检查了足月分娩的早产妇女和有或没有羊水内感染/炎症的早产妇女的羊水代谢组。

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