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电子鼻检测呼出气分析物受昼夜节律的影响。

Influence of circadian rhythm on exhaled breath profiling by electronic nose.

机构信息

Respiratory Diseases, University of Bari, Italy.

Pulmonology, Ospedale Di Venere, Bari, Italy.

出版信息

J Biol Regul Homeost Agents. 2018 Sep-Oct;32(5):1261-1265.

PMID:30334423
Abstract

Electronic noses (e-noses) are a cheap and easy method for exhaled Volatile Organic Compound (VOC)-analysis which has shown its potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to investigate a potential influence of circadian variation on VOC-profile analyzed by an e-nose in healthy subjects. We enrolled 22 adults free of any known diseases. A sequence of exhaled breath samplings were performed on all participants at predetermined hours (7am, 12pm, 17pm, 23pm) and analyzed by an e-nose (Cyranose 320). According to Principal Component Analysis, significant circadian variations of the exhaled VOC-profile were shown for Principal Component (PC) 1 and 3. In detail, PC1 and PC3 values were significantly higher in the morning compared to the afternoon and evening (for all parameters p less than 0.05). Successive Linear Discriminant analysis confirmed the findings above. The daily variations in VOCs-profile, with the peak in the morning, could be relevant for future clinical applications, especially in the choice of optimal time for sampling patients.

摘要

电子鼻(e-nose)是一种廉价且易于使用的呼气挥发性有机化合物(VOC)分析方法,已在多种疾病中显示出其潜力。在这些仪器在临床环境中得到充分验证之前,仍需要确定一些方法学问题。我们旨在研究健康受试者中电子鼻分析的 VOC 谱是否存在昼夜节律变化的影响。我们招募了 22 名无任何已知疾病的成年人。所有参与者都在预定时间(7 点、12 点、17 点、23 点)进行一系列呼气采样,并由电子鼻(Cyranose 320)进行分析。根据主成分分析,呼气 VOC 谱的主要成分(PC)1 和 3 显示出明显的昼夜节律变化。具体来说,与下午和晚上相比,PC1 和 PC3 的值在早上明显更高(所有参数 p 值均小于 0.05)。连续线性判别分析证实了上述发现。VOCs 谱的日间变化,早晨出现峰值,可能与未来的临床应用相关,尤其是在选择最佳采样时间方面。

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引用本文的文献

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Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms.采用简化数据收集方法对跨诊断社区样本中的电子鼻进行机器学习分析:挥发性有机化合物与精神症状之间无关联。
Front Psychiatry. 2020 Sep 16;11:503248. doi: 10.3389/fpsyt.2020.503248. eCollection 2020.