Nojima Junzo, Sakudo Akikazu, Hakariya Yukiko, Kuratsune Hirohiko, Watanabe Yasuyoshi, Kanakura Yuzuru, Ikuta Kazuyoshi
Laboratory for Clinical Investigation, Osaka University Hospital, 2-15 Yamadaoka, Suita, Osaka 565-0871, Japan.
Biochem Biophys Res Commun. 2007 Oct 19;362(2):522-4. doi: 10.1016/j.bbrc.2007.08.020. Epub 2007 Aug 13.
The purpose of this study was to investigate whether visible and near-infrared (Vis-NIR) spectroscopy can be used for diagnoses of anti-phospholipid syndrome (APS). Vis-NIR spectra from 90 plasma samples [anti-phospholipid antibodies (aPLs)-positive group, n=48; aPLs-negative group, n=42] were subjected to principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) to develop multivariate models to discriminate between aPLs-positive and aPLs-negative. Both PCA and SIMCA models were further assessed by the prediction of 84 masked other determinations. The PCA model predicted successful discrimination of the masked samples with respect to aPLs-positive and aPLs-negative. The SIMCA model predicted 42 of 48 (87.5%) aPLs-positive patients and 33 of 36 (91.7%) aPLs-negative patients of Vis-NIR spectra from masked samples correctly. These results suggest that Vis-NIR spectroscopy combined with multivariate analysis could provide a promising tool to objectively diagnose APS.
本研究的目的是调查可见近红外（Vis-NIR）光谱是否可用于抗磷脂综合征（APS）的诊断。对90份血浆样本[抗磷脂抗体（aPLs）阳性组，n = 48；aPLs阴性组，n = 42]的Vis-NIR光谱进行主成分分析（PCA）和类类比软独立建模（SIMCA），以建立多变量模型来区分aPLs阳性和aPLs阴性。通过对84个隐藏的其他测定进行预测，进一步评估PCA和SIMCA模型。PCA模型成功预测了隐藏样本在aPLs阳性和aPLs阴性方面的区分。SIMCA模型正确预测了来自隐藏样本的Vis-NIR光谱中48例aPLs阳性患者中的42例（87.5%）和36例aPLs阴性患者中的33例（91.7%）。这些结果表明，Vis-NIR光谱结合多变量分析可为客观诊断APS提供一种有前景的工具。
