Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.
Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.
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DOI：
10.3390/s22051744
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发表时间：
2022-02-23
期刊：
影响因子：
--
通讯作者：
Murugan GS
中科院分区：
文献类型：
--
作者：
Ahmed W;Veluthandath AV;Rowe DJ;Madsen J;Clark HW;Postle AD;Wilkinson JS;Murugan GS
本研究的作者开发了使用衰减全反射傅里叶变换红外光谱（ATR-FTIR）结合机器学习作为床旁（POC）诊断平台，考虑到新生儿呼吸窘迫综合征（nRDS），目前没有POC存在，作为一个例子。nRDS可以通过两种nRDS生物标志物（卵磷脂和鞘磷脂）的比例小于2.2（L/S比）来诊断，在本研究中，根据使用纯化试剂生成的1.0至3.4之间的L/S比记录了ATR-FTIR光谱。在预测另外104个光谱的浓度之前，使用155个原始基线和二阶导数光谱进行主成分（PCR）和偏最小二乘（PLSR）回归模型的校准。二阶导数光谱的三因素PLSR模型最好地预测了整个范围内的L/S比（R2：0.967; MSE：0.014）。当使用二阶导数光谱PLSR模型时，预测L/S比为1.0至3.4，预测区间为+0.29，-0.37，并且在L/S 2.2区域附近的平均预测区间为+0.26，-0.34。这些结果支持将ATR-FTIR与机器学习相结合以开发用于检测和定量具有可解释的中红外光谱的任何生物标志物的即时设备的有效性。
The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) combined with machine learning as a point-of-care (POC) diagnostic platform, considering neonatal respiratory distress syndrome (nRDS), for which no POC currently exists, as an example. nRDS can be diagnosed by a ratio of less than 2.2 of two nRDS biomarkers, lecithin and sphingomyelin (L/S ratio), and in this study, ATR–FTIR spectra were recorded from L/S ratios of between 1.0 and 3.4, which were generated using purified reagents. The calibration of principal component (PCR) and partial least squares (PLSR) regression models was performed using 155 raw baselined and second derivative spectra prior to predicting the concentration of a further 104 spectra. A three-factor PLSR model of second derivative spectra best predicted L/S ratios across the full range (R2: 0.967; MSE: 0.014). The L/S ratios from 1.0 to 3.4 were predicted with a prediction interval of +0.29, −0.37 when using a second derivative spectra PLSR model and had a mean prediction interval of +0.26, −0.34 around the L/S 2.2 region. These results support the validity of combining ATR–FTIR with machine learning to develop a point-of-care device for detecting and quantifying any biomarker with an interpretable mid-infrared spectrum.
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影响因子：
3.3
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1016/0002-9378(71)90342-5
发表时间：
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影响因子：
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作者：
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影响因子：
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作者：
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