Department of Virology, Center for Infectious Disease Control, Research Institute for Microbial Diseases, Osaka University, Yamadaoka, Suita, Osaka 565-0871, Japan.
Clin Chim Acta. 2012 Dec 24;414:130-4. doi: 10.1016/j.cca.2012.08.022. Epub 2012 Aug 29.
Influenza patients show a severe condition of the respiratory tract with high temperature. Efficient treatment of influenza requires early use of oseltamivir, and thus rapid diagnosis is needed. Recently, rapid diagnostic methods such as immunochromatography have been developed; however, immunochromatography is not an optimal technique because it is relatively expensive and has low sensitivity.
Visible and near-infrared (Vis-NIR) spectroscopy in the region 600-1100 nm, combined with chemometrics analysis such as principal component analysis (PCA) or soft modeling of class analogy (SIMCA), was used to develop a potential diagnostic method for influenza based on nasal aspirates from infected patients.
The Vis-NIR spectra of nasal aspirates from 33 non-influenza patients and 34 influenza patients were subjected to PCA and SIMCA to develop multivariate models to discriminate between influenza and non-influenza patients. These models were further assessed by the prediction of 126 masked measurements [30 from non-influenza patients, 30 from influenza patients and 66 from patients infected with respiratory syncytial virus (RSV)]. The PCA model showed some discrimination of the masked samples. The SIMCA model correctly predicted 29 of 30 (96.7%) non-influenza patients, and 30 of 30 (100%) influenza patients from the Vis-NIR spectra of masked nasal aspirate samples. Nasal aspirates of RSV-infected patients were predicted as 50% non-influenza and 50% influenza by the SIMCA model, suggesting that discrimination between patients infected with influenza virus and those infected with RSV was difficult.
Although the study sample was small and there was difficulty in discriminating between influenza virus and RSV infection, these results suggest that Vis-NIR spectroscopy of nasal aspirates, combined with chemometrics analysis, might be a potential tool for diagnosis of influenza.
流感患者表现出呼吸道严重症状和高热。奥司他韦的早期使用是流感的有效治疗方法，因此需要快速诊断。最近，已经开发出免疫层析等快速诊断方法；然而，免疫层析不是一种最佳技术，因为它相对昂贵，并且灵敏度低。
在 600-1100nm 区域使用可见和近红外（Vis-NIR）光谱，结合主成分分析（PCA）或类模拟软建模（SIMCA）等化学计量学分析，开发了一种基于感染患者鼻抽吸物的流感潜在诊断方法。
对 33 例非流感患者和 34 例流感患者的鼻抽吸物的 Vis-NIR 光谱进行 PCA 和 SIMCA 分析，以建立区分流感和非流感患者的多元模型。通过对 126 个掩蔽测量值（30 个来自非流感患者、30 个来自流感患者和 66 个来自呼吸道合胞病毒（RSV）感染患者）的预测进一步评估了这些模型。PCA 模型对掩蔽样本具有一定的区分能力。SIMCA 模型正确预测了 30 个（96.7%）非流感患者和 30 个（100%）流感患者的掩蔽鼻抽吸物样本。SIMCA 模型预测 RSV 感染患者的鼻抽吸物样本为 50%非流感和 50%流感，表明区分流感病毒和 RSV 感染患者具有一定难度。
尽管研究样本较小，且区分流感病毒和 RSV 感染具有一定难度，但这些结果表明，鼻抽吸物的 Vis-NIR 光谱结合化学计量学分析可能是诊断流感的一种潜在工具。
