Elkadi Omar Anwar, Hassan Reem, Elanany Mervat, Byrne Hugh J, Ramadan Mohammed A
Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt; Dar Elsalam Cancer Center, Cairo, Egypt.
Department of Clinical Pathology, Faculty of Medicine, Cairo University, Cairo, Egypt.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 5;248:119259. doi: 10.1016/j.saa.2020.119259. Epub 2020 Nov 30.
Invasive Aspergillosis is a challenging infection that requires convenient, efficient, and cost-effective diagnostics. This study addresses the potential of infrared spectroscopy to satisfy this clinical need with the aid of machine learning. Two models, based on Partial Least Squares-Discriminant Analysis (PLS-DA), have been trained by a set of infrared spectral data of 9 Aspergillus-spiked and 7 Aspergillus-free plasma samples, and a set of 200 spectral data simulated by oversampling these 16 samples. Two further models have also been trained by the same sets but with auto-scaling performed prior to PLS-DA. These models were assessed using 45 mock samples, simulating the challenging samples of patients at risk of Invasive Aspergillosis, including the presence of drugs (9 tested) and other common pathogens (5 tested) as potential confounders. The simple model shows good prediction performance, yielding a total accuracy of 84.4%, while oversampling and autoscaling improved this accuracy to 93.3%. The results of this study have shown that infrared spectroscopy can identify Aspergillus species in blood plasma even in presence of potential confounders commonly present in blood of patients at risk of Invasive Aspergillosis.
侵袭性曲霉病是一种具有挑战性的感染，需要便捷、高效且经济高效的诊断方法。本研究探讨了红外光谱在机器学习辅助下满足这一临床需求的潜力。基于偏最小二乘判别分析（PLS-DA）的两个模型，已通过9个添加曲霉的血浆样本和7个不含曲霉的血浆样本的一组红外光谱数据，以及通过对这16个样本进行过采样模拟得到的一组200个光谱数据进行训练。另外两个模型也使用相同的数据集进行训练，但在PLS-DA之前进行了自动缩放。这些模型使用45个模拟样本进行评估，这些样本模拟了有侵袭性曲霉病风险患者的具有挑战性的样本，包括存在药物（测试了9种）和其他常见病原体（测试了5种）作为潜在混杂因素。简单模型显示出良好的预测性能，总准确率为84.4%，而过采样和自动缩放将该准确率提高到了93.3%。本研究结果表明，即使存在侵袭性曲霉病风险患者血液中常见的潜在混杂因素，红外光谱也能识别血浆中的曲霉菌种。
