双因素判别分析胃癌组织与正常组织自体荧光光谱

通过对手术切除的37例胃癌患者的离体标本的自体荧光光谱检测,显示308nm,337nm激发光谱均呈双主峰结构,双主峰的比值均能区分胃癌与正常胃壁(P<0.001).分别用308nm,337nm激发荧光光谱单因素分析诊断胃癌的灵敏度、特异度、阳性预测值分别为88.8%,86.8%,86.4%和80.1%,72.1%,67.5%.对308nm及337nm激发荧光光谱双因素分析,癌诊断的灵敏度、特异度、阳性预测值分别为94.4%,92.1%,91.9%,其诊断正确符合率明显高于单因素分析的结果.研究表明,以308nm及337nm双波长激发检测胃壁浆膜,双因素判别算法可较好地区分胃癌,这种方法有望成为手术中快速识别胃癌在胃壁的浸润范围的有效方法.     

荧光淬灭剂TrueBlack Lipofuscin对珊瑚自体荧光的消除及其在免疫荧光标记中的应用

自体荧光是生物体内部组织成分在吸收光时自然发出的荧光,会影响人工标记荧光的有效识别,严重干扰对抗体标记的目的蛋白荧光的观察及数据分析,在珊瑚冰冻切片免疫荧光标记研究中尤为突显。为更有效地去除珊瑚组织切片中的自体荧光,本研究以共生模式种花伞软珊瑚（Xenia sp.）为研究对象,首次尝试在花伞软珊瑚冰冻切片免疫荧光染色中,使用一种在小鼠和人体组织切片中常用的TrueBlack Lipofuscin自体荧光淬灭剂,并与已有研究中的酒精梯度脱水法进行比较。结果表明,使用荧光淬灭剂TrueBlack Lipofuscin后,花伞软珊瑚组织切片中的自体荧光强度是传统的酒精梯度脱水法的52.4%,且在Cy5通道下效果最佳。同时,通过珊瑚免疫荧光标记实验表明使用TrueBlack Lipofuscin的实验组特异性荧光信号强度为对照组的205.3%,背景荧光和自体荧光基本去除。     

PLS-ANN分析血浆自体荧光光谱的二阶导数光谱识别胃癌

探讨偏最小二乘法结合神经网络法(简称PLS-ANN)分析血浆自体荧光光谱的二阶导数光谱识别胃癌的优势,对20例胃癌病人和23例健康人血浆进行以405nm为激发光的自体荧光光谱检测,采用PLS-ANN法分别对血浆自体荧光光谱和二阶导数光谱进行判别分析。PLS-ANN分析血浆的自体荧光光谱法诊断胃癌的灵敏度为75%,特异度为83%,准确率为79%,PLS-ANN分析血浆的二阶导数光谱法诊断胃癌的灵敏度为90%,特异度为96%,准确率为93%。结果表明PLS-ANN分析血浆的二阶导数光谱法识别胃癌,优于PLS-ANN分析血浆的自体荧光光谱法,有望成为快速识别胃癌的较理想方法。     

Evaluating the Effects of Autofluorescence during Raman Hyperspectral Imaging

Raman hyperspectral imaging is becoming a popular technique to analyse geological materials. Autofluorescence can affect the quality of the spectra that comprise hyperspectral data sets. Few studies have addressed potential misinterpretation of Raman images from hyperspectral data sets affected by autofluorescence. Additionally, little work has been done to develop methods for identifying the spatial distribution of spectra affected by autofluorescence. This study illustrates how autofluorescence may lead to misinterpretation of the distribution of materials based on intensity at a point images. A method is proposed utilising signal to axis analysis to create images that identify regions affected by autofluorescence. Post‐processing baseline correction is often used to address autofluorescence, and most software programs utilise a form of partial least squares regression modelling based on a subjective choice of polynomial order. This study shows that an inappropriate choice of polynomial order can introduce error, which may lead to misinterpretation of Raman images. A signal to axis analysis method is proposed to statistically compare seemingly ‘appropriate’ baseline correction trials. Although post‐processing of hyperspectral data sets and creating Raman images seem simple, data quality issues such as autofluorescence must be considered. If baseline correction is deemed necessary, it should be addressed as an experiment involving statistical comparison.