Sakudo Akikazu, Kuratsune Hirohiko, Kobayashi Takanori, Tajima Seiki, Watanabe Yasuyoshi, Ikuta Kazuyoshi
Department of Virology, Center for Infectious Disease Control, Research Institute for Microbial Diseases, Osaka University, Yamadaoka, Suita, Japan.
Biochem Biophys Res Commun. 2006 Jul 14;345(4):1513-6. doi: 10.1016/j.bbrc.2006.05.074. Epub 2006 May 22.
To investigate visible and near-infrared (Vis-NIR) spectroscopy enabling chronic fatigue syndrome (CFS) diagnosis, we subjected sera from CFS patients as well as healthy donors to Vis-NIR spectroscopy. Vis-NIR spectra in the 600-1100 nm region for sera from 77 CFS patients and 71 healthy donors were subjected to principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) to develop multivariate models to discriminate between CFS patients and healthy donors. The model was further assessed by the prediction of 99 masked other determinations (54 in the healthy group and 45 in the CFS patient group). The PCA model predicted successful discrimination of the masked samples. The SIMCA model predicted 54 of 54 (100%) healthy donors and 42 of 45 (93.3%) CFS patients of Vis-NIR spectra from masked serum samples correctly. These results suggest that Vis-NIR spectroscopy for sera combined with chemometrics analysis could provide a promising tool to objectively diagnose CFS.
为了研究可见及近红外（Vis-NIR）光谱技术用于慢性疲劳综合征（CFS）诊断的可行性，我们对CFS患者以及健康供体的血清进行了Vis-NIR光谱分析。对77例CFS患者和71例健康供体血清在600 - 1100 nm区域的Vis-NIR光谱进行主成分分析（PCA）和类相关软独立建模（SIMCA），以建立多变量模型来区分CFS患者和健康供体。通过对另外99个盲法测定样本（健康组54个，CFS患者组45个）进行预测，进一步评估该模型。PCA模型成功预测了盲法样本的区分情况。SIMCA模型正确预测了来自盲法血清样本的Vis-NIR光谱中54例（100%）健康供体和45例中的42例（93.3%）CFS患者。这些结果表明，血清Vis-NIR光谱结合化学计量学分析可为客观诊断CFS提供一种有前景的工具。
