利用大环传感器阵列诊断人体血清中 SARS-Cov-2 感染的方法学研究

IF 3.5 Q2 CHEMISTRY, ANALYTICAL
Monica Swetha Bosco, Zeki Topçu, Soumen Pradhan, Ariadne Sossah, Vassilis Tsatsaris, Christelle Vauloup-Fellous, Sarit S. Agasti, Yves Rozenholc and Nathalie Gagey-Eilstein
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引用次数: 0

摘要

本文报告了一种基于血液的 SARS-CoV-2 感染诊断策略的方法和概念验证。该方法采用非特异性/选择性阵列传感策略,模仿人类嗅觉系统,使用葫芦[7]脲大环受体与环境敏感荧光团库共轭。研究队列包括 26 个样本,即 12 个病例和 14 个对照。线性判别和随机森林等统计分析方法能够成功地对两组样本进行分类和判别,准确率接近 90%。这一诊断结果凸显了该方法学,并证实了这种非特异性/选择性传感方法在无创临床诊断方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A methodological study for the diagnosis of the SARS-Cov-2 infection in human serum with a macrocyclic sensor array†

A methodological study for the diagnosis of the SARS-Cov-2 infection in human serum with a macrocyclic sensor array†

A methodological study for the diagnosis of the SARS-Cov-2 infection in human serum with a macrocyclic sensor array†

This article reports the methodology and the proof of concept of a blood-based diagnostic strategy for the SARS-CoV-2 infection. The proposed method relies on the non-specific/selective array-based sensing strategy mimicking the human olfactory system using a cucurbit[7]uril macrocycle receptor conjugated with a library of environmentally sensitive fluorophores. The study cohort includes 26 samples, i.e. 12 cases and 14 controls. Statistical analysis methods such as linear discriminant and random forest were able to successfully classify and discriminate the two groups with almost 90% accuracy. This diagnostic result highlights the methodology and confirms the potential of this non-specific/selective sensing approach for non-invasive clinical diagnosis.

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