Bacterial Vaginosis Monitoring with Carbon Nanotube Field-Effect Transistors
Bacterial Vaginosis Monitoring with Carbon Nanotube Field-Effect Transistors
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DOI：
10.1021/acs.analchem.1c04755
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发表时间：
2022-03-08
影响因子：
7.4
通讯作者：
Star, Alexander
中科院分区：
文献类型：
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作者：
Liu, Zhengru;Bian, Long;Star, Alexander
在妊娠期间快速可靠地筛查细菌性阴道病（BV）的能力对孕产妇健康和妊娠结局具有重要意义。在这项概念验证研究中，我们证明了碳纳米管场效应晶体管（NTFET）在BV快速诊断中的潜力，并检测了BV相关因素，如pH和生物胺。制作的传感器表现出良好的线性pH值的变化与线性相关系数为0.99。pH传感器在储存一个月以上后性能稳定。此外，该传感器能够通过机器学习对BV相关生物胺阴性/阳性样本进行分类，利用不同的测试策略和算法，包括线性判别分析（LDA），支持向量机（SVM）和主成分分析（PCA）。生物胺样品的状态可以很好地分类使用软边支持向量机模型的验证准确率为87.5%。使用金栅电极进行测量可以进一步提高精度，LDA和SVM模型的精度均高于90%。我们还探索了传感机制，发现NTFET截止电流的变化对分类至关重要。制造的传感器成功地检测BV相关因素，证明了NTFET用于BV的即时诊断的竞争优势。
The ability to rapidly and reliably screen for bacterial vaginosis (BV) during pregnancy is of great significance for maternal health and pregnancy outcomes. In this proof-of-concept study, we demonstrated the potential of carbon nanotube field-effect transistors (NTFET) in the rapid diagnostics of BV with the sensing of BV-related factors such as pH and biogenic amines. The fabricated sensors showed good linearity to pH changes with a linear correlation coefficient of 0.99. The pH sensing performance was stable after more than one month of sensor storage. In addition, the sensor was able to classify BV-related biogenic amine-negative/positive samples with machine learning, utilizing different test strategies and algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), and principal component analysis (PCA). The biogenic amine sample status could be well classified using a soft-margin SVM model with a validation accuracy of 87.5%. The accuracy could be further improved using a gold gate electrode for measurement, with accuracy higher than 90% in both LDA and SVM models. We also explored the sensing mechanisms and found that the change in NTFET off current was crucial for classification. The fabricated sensors successfully detect BV-related factors, demonstrating the competitive advantage of NTFET for point-of-care diagnostics of BV.