Multispectral and autofluorescence, human and mouse
收藏doi.org2025-01-22 收录
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http://doi.org/10.17632/bcbdcn3t6c.1
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资源简介:
Gastric inflammation is a major risk factor for gastric cancer. Current endoscopic methods are not capable to efficiently detect and characterize gastric inflammation, leading to a sub-optimal patients’ care. New non-invasive methods are needed. Reflectance mucosal light analysis is of particular interest in this indication. The aim of our study was to analyze the reflectance light and specific autofluorescence signals, both in humans and in a mouse model of gastritis.
We recruited patients undergoing gastroendoscopic procedure during which reflectance was analysed with a multispectral camera. In parallel, the gastritis mouse model of Helicobacter pylori infection was used to investigate reflectance from ex vivo gastric samples using a spectrometer. In both cases, autofluorescence signals were measured using a confocal microscope.
In gastritis patients, reflectance modifications were significant in near-infrared spectrum, with a decrease between 610 and 725 nm and an increase between 750 and 840 nm. Autofluorescence was also modified, showing variations around 550 nm of emission. In H. pylori infected mice developing gastric inflammatory lesions, we observed significant reflectance modifications 18 months after infection, with increased intensity between 617 and 672 nm. Autofluorescence was significantly modified after 1, 3 and 6 months around 550 and 630 nm. Both in human and in mouse, these reflectance data can be considered as biomarkers and accurately predicted inflammatory state.
胃黏膜炎症是引发胃癌的重要风险因素。当前的内镜检查方法尚不能有效地检测和表征胃黏膜炎症,导致患者护理效果不尽如人意。迫切需要开发新的非侵入性检测方法。在此领域,反光黏膜光分析技术尤为引人注目。本研究旨在分析人类和胃溃疡小鼠模型中的反光光和特定自荧光信号。我们招募了在接受胃镜检查期间进行反光分析的患者,并使用多光谱相机进行。同时,利用幽门螺杆菌感染的小鼠胃溃疡模型,通过光谱仪研究了离体胃样本的反光。在两种情况下,均使用共聚焦显微镜测量了自荧光信号。在胃溃疡患者中,近红外光谱范围内的反光变化显著,610至725纳米间的反光强度下降,而750至840纳米间的反光强度上升。自荧光信号亦有所改变,发射波长在550纳米附近出现变化。在感染幽门螺杆菌并出现胃炎症损伤的小鼠中,我们观察到感染18个月后反光信号发生显著变化,617至672纳米间的反光强度增加。自荧光信号在感染后的第1、3和6个月在550和630纳米处发生显著改变。在人类和小鼠中,这些反光数据均可被视为生物标志物,并准确预测炎症状态。
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Mendeley Data



