Fluorescence Analysis of Biochemical Constituents Identifies Atherosclerotic Plaque With a Thin Fibrous Cap

K. Arakawa, K. Isoda, Toshimitu Ito, K. Nakajima, T. Shibuya, F. Ohsuzu
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引用次数: 64

Abstract

Vulnerable plaque generally contains a thin fibrous cap, lipid pools, and reduced internal plaque collagen. Arterial fluorescence analysis can differentiate atherosclerotic lesions from normal arteries; however, the contribution of the lipid core to atherosclerotic arterial fluorescence remains controversial. This study aimed to identify lipid core fluorophores and to differentiate the lipid core from normal artery and atheroma. The helium-cadmium laser–induced fluorescence spectra of cadaveric arteries and known chemical constituents were recorded. Lipid core fluorescence spectra exhibited marked red shifts and broadening compared with the fluorescence spectra of normal tissue and atheroma. Similar fluorescence spectra were obtained for lipid core and oxidized low density lipoprotein, for atheroma and collagen, and for normal artery and elastin. A classification based on collagen, elastin, and oxidized low density lipoprotein spectral decomposition could discriminate the lipid core (n=29), normal artery (n=74), atheroma (n=73), and preatheroma (n=10) with 86% accuracy. Fibrous cap thickness was correlated with the spectral collagen content index (r =0.65, P <0.0001), especially at a thickness of <200 &mgr;m. We conclude that a classification algorithm based on chemical spectral decomposition can accurately classify the fluorescence spectra of normal artery, atheroma, and lipid core and may be useful in identifying vulnerable atheroma in vivo.
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荧光分析生物化学成分识别动脉粥样硬化斑块与薄纤维帽
易损斑块通常包含薄纤维帽、脂质池和减少的内部斑块胶原蛋白。动脉荧光分析可以区分动脉粥样硬化病变与正常动脉;然而,脂质核心对动脉粥样硬化动脉荧光的贡献仍然存在争议。本研究旨在鉴定脂质核心荧光团,并将脂质核心与正常动脉和动脉粥样硬化区分开。记录了氦镉激光诱导的尸体动脉和已知化学成分的荧光光谱。与正常组织和动脉粥样硬化组织相比,脂质核荧光光谱出现明显的红移和增宽。脂核和氧化低密度脂蛋白,动脉粥样硬化和胶原蛋白,正常动脉和弹性蛋白的荧光光谱相似。基于胶原蛋白、弹性蛋白和氧化低密度脂蛋白光谱分解的分类可以区分脂质核心(n=29)、正常动脉(n=74)、动脉粥样硬化(n=73)和动脉粥样硬化前期(n=10),准确率为86%。纤维帽厚度与光谱胶原含量指数相关(r =0.65, P <0.0001),特别是在纤维帽厚度<200 &mgr;m时。我们认为,基于化学光谱分解的分类算法可以准确地对正常动脉、动脉粥样硬化和脂质核心的荧光光谱进行分类,可能有助于识别体内易损动脉粥样硬化。
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