基于表面增强拉曼光谱的冠心病前瞻性诊断方法研究

前期研究表明血小板衍生生长因子-BB(platelet-derived growth factor-BB,PDGF-BB)与冠心病关系密切,本文基于表面增强拉曼光谱(surface-enhanced Raman spectroscopy,SERS)方法,结合PDGF-BB拉曼频移为1 509 cm~(-1)的拉曼特征峰,对60例冠心病患者,其中包括20例行皮冠状动脉介入治疗术(percutaneous coronary intervention,PCI)病人与40例未行PCI病人的尿液样本以及18例健康人尿液样本进行分析。结果显示:行PCI病人的尿液样本SERS光谱中可以检测到1 509 cm~(-1)的拉曼特征峰;而在健康人与大多数未行PCI病人的尿液SERS光谱中则检测不到。与临床资料对比发现,尿液SERS光谱与冠状动脉造影技术在判断心血管堵塞程度是否达到70%以上吻合度较高。基于表面增强拉曼光谱的冠心病检测方法有望发展为一种无创的冠心病前瞻性诊断工具,对于冠心病疑似病例是否需要行PCI提供可靠的临床诊断依据。     

拉曼光谱检测微生物的研究方法和进展北大核心

快速准确地识别和鉴定微生物对于环境科、食品质量以及医学诊断等领域研究至关重要。拉曼光谱(Raman spectroscopy)已经被证明是一种能够实现微生物快速诊断的新技术,在提供微生物指纹图谱信息的同时,能够快速、非标记、无创、敏感地在固体和液体环境中实现微生物单细胞水平的检测。本文简单介绍了拉曼光谱的基本概念和原理,重点综述了拉曼光谱微生物检测应用中的样品处理方法及光谱数据处理方法。除此之外,本文概括了拉曼光谱在细菌、病毒和真菌中的应用,其中单独概括了拉曼在细菌快速鉴定和抗生素药敏检测中的应用。最后,本文阐述了拉曼光谱在微生物检测中的挑战和展望。     

基于密度泛函理论方法的核酸碱基拉曼光谱研究

核酸碱基是核酸的重要组成部分,而拉曼光谱是研究分子结构的一种重要技术,利用拉曼光谱对核酸碱基分子进行研究对于研究核酸大分子的结构变化,以及核酸分子与小分子之间的作用具有重要的意义.本研究以表征核酸碱基的拉曼光谱为目的,利用密度泛函理论(density functional theory,DFT)的方法优化腺嘌呤、鸟嘌呤、胞嘧啶、胸腺嘧啶和尿嘧啶的分子结构,对这5种核酸碱基的分子内化学键振动进行了量化计算并获得了理论拉曼光谱结果.利用计算结果对实验获得的碱基的固体拉曼光谱进行了表征,并且结合前人的研究结果对每种碱基的一些重要特征拉曼谱峰进行了细致的阐释,为进一步利用拉曼光谱研究核酸分子的结构信息奠定了理论基础.     

葡萄糖异构酶的纯化和拉曼光谱

采用Streptomyces rosechromagenusNo.336细胞丙酮干粉,经抽提、DEAE-纤维素和DEAE-Sephsdex A-50层析,Sephadex G-200凝胶过滤,得到了电泳纯的葡萄糖异构酶,以这种均一态酶测定了其激光拉曼光谱。     

利用PCA-LDA和参数-CART方法对直肠癌血清拉曼光谱的分析

本文利用拉曼光谱技术检测由直肠癌的发生导致的血清成分的变化。比较了直肠癌患者和对照组之间血清拉曼光谱的差异,并对术后直肠癌患者血清拉曼光谱的变化也进行了比较,以监测术后治疗效果。结果表明在某些波数位置不同组的拉曼峰有统计学意义的变化,这些变化反应了血清中相应的生物物质的改变。之后,主成分分析(PCA)及峰强比参数这两种方法被用于原始拉曼光谱的特征变量的提取。将线性判别分析(LDA)和分类回归树(CART)两种判别分析法用于特征变量的判别分析。PCA-LDA和参数-CART方法的诊断准确率分别为87%和90%。     

毛姜花油细胞原位拉曼光谱研究

为避免复杂的样品的制备及提取过程,最大限度避免精油活性成分变化,常温下,用拉曼光谱原位分析毛姜花油细胞中精油。样品切片后置于共聚焦显微拉曼光谱仪下,用10倍物镜可观察到油细胞。油细胞精油的拉曼光谱与1,8-桉油精拉曼光谱非常相似。以毛姜花油细胞/1,8-桉油精的拉曼峰为序,较强峰出现在2928/2 921、647/652 cm~(-1),次强峰出现在540/545、808/813、915/920、926/930、1 012/1 016、1 075/1 080、1 270/1273、1 427/1 432 cm~(-1)。在油细胞中出现的强峰、次强峰与1,8-桉油精的拉曼峰一致,说明毛姜花油细胞中油的主要成分为1,8-桉油精。毛姜花油细胞的25条拉曼峰都与1,8-桉油精的拉曼峰有很好的对应关系。     

食道癌组织表面增强拉曼光谱研究

本研究采用以金纳米粒子为增强基底的表面增强拉曼光谱技术对31位食道癌患者的62例食道组织样本进行分析,其中包含31例肿瘤组织与31例正常组织.食道癌组织和正常组织表面增强拉曼光谱之间存在明显差异.利用主成分分析和线性判别分析算法对采集的组织表面增强拉曼光谱进行诊断研究.利用主成分分析-线性判别分析统计分析方法得到诊断灵敏度与特异性分别为90.3%与90.3%.为验证所构建的主成分分析-线性判别分析算法的有效性,利用受试样品的工作特征曲线方法,对所构建的算法的有效性进行评价.通过对组织表面增强拉曼光谱谱峰归属分析可知,癌组织中蛋白质结构可能发生变化,磷脂质的相对含量下降;而氨基化合物、组蛋白、苯基丙氨酸和酪氨酸的相对含量出现上升.研究表明,以金纳米粒子为增强基底的表面增强拉曼光谱技术结合主成分分析-线性判别分析统计分析方法能够准确区分食道癌患者肿瘤与正常组织.食道癌组织表面增强拉曼光谱技术有望成为一种食道癌的临床诊断与筛查工具.     

家兔晶状体拉曼光谱空间分布研究

晶状体透明性的维持与其蛋白的结构成分极为密切,因此研究晶状体内的蛋白空间分布变化有重要意义.由实验中得到的450 cm~(-1)～2 000 cm~(-1)范围内健康家兔晶状体拉曼光谱,计算了家兔晶状体蛋白中三条氨基酸侧链和两条蛋白质主链拉曼谱峰沿赤道部和视轴部的光谱强度,根据实验结果讨论了这五条拉曼光谱在家兔晶状体中的空间分布特性.     

胆绿素的激光表面增强拉曼光谱研究

本文中 ,SERS被用来测定胆绿素 ,并对溶液中的 p H和 Na Cl浓度对谱图的影响进行了研究。测定下限可达 1 .2 8× 1 0 -7M,且摄谱时间只需 1 5分钟。另外 ,讨论了胆绿素在银胶表面的吸附状态及质子化状态。研究表明胆绿素在银胶表面是呈近乎平面的环状结构吸附 ,其内酰胺环 A上氧原子和吡咯环上的氮原子可能发生了质子化。     

尿液表面增强拉曼光谱的初步研究

利用紫外与可见分光光度计测量银溶胶与尿液的吸收谱,采用拉曼光谱测量系统检测并研究分析了尿液加入银胶前后的拉曼光谱.基于表面增强技术,尿液的拉曼光谱信号得到显著增强,尿液中微弱的尿酸SERS信号被成功检测.文中对尿液的拉曼峰进行了谱峰归属,并分析了晨尿与夜尿的SERS谱.对晨尿的检测具有更高的可信度和信噪比.研究结果表明...     

Raman Spectroscopy and Chemometrics for Identification and Strain Discrimination of the Wine Spoilage Yeasts Saccharomyces cerevisiae,Zygosaccharomyces bailii,and Brettanomyces bruxellensis

The yeasts Zygosaccharomyces bailii, Dekkera bruxellensis (anamorph, Brettanomyces bruxellensis), and Saccharomyces cerevisiae are the major spoilage agents of finished wine. A novel method using Raman spectroscopy in combination with a chemometric classification tool has been developed for the identification of these yeast species and for strain discrimination of these yeasts. Raman spectra were collected for six strains of each of the yeasts Z. bailii, B. bruxellensis, and S. cerevisiae. The yeasts were classified with high sensitivity at the species level: 93.8% for Z. bailii, 92.3% for B. bruxellensis, and 98.6% for S. cerevisiae. Furthermore, we have demonstrated that it is possible to discriminate between strains of these species. These yeasts were classified at the strain level with an overall accuracy of 81.8%.     

紫外指纹图谱结合化学计量学对黄精基原植物的鉴别

运用紫外光谱技术结合化学计量学,建立快速鉴别不同基原黄精的方法。通过单因素实验确定黄精最佳提取溶剂、时间和用量,制备测试液,采用紫外光谱技术建立3种基原黄精的紫外指纹图谱,光谱数据转化后进行主成分(PCA)和系统聚类分析(HCA)。该方法重现性、精密度、稳定性较好,结果表明不同种类黄精紫外指纹图谱具有指纹特性,3种基原植物黄精紫外光谱图在210 nm、220 nm、280 nm附近差异明显;聚类分析和主成分分析三维投影图反映出不同种类黄精的化学成分积累具有差异,能较好地区分滇黄精(Polygonatumkingianum)、黄精(P.sibiricum)与多花黄精(P.cyrtonema)。紫外光谱结合化学计量学能快速鉴别不同种类黄精,可作为黄精的鉴别和质量控制新方法,为黄精临床应用、资源开发及黄精属植物分类提供辅助方法。     

Differential Diagnostics of Bacteria Using the Surface-Enhanced Raman Spectra

Microbiology - Surface-enhanced Raman scattering of bioorganic compounds close to silver nanoparticles may be used for species identification of microbial colonies. The preparations of silver...     

Automated Identification of Subcellular Organelles by Coherent Anti-Stokes Raman Scattering

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the “random forest” ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.     

Automated Identification of Subcellular Organelles by Coherent Anti-Stokes Raman Scattering

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the “random forest” ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.     

一种用于测试生物样品拉曼光谱的新方法

本文报道了一种在测试拉曼光谱时把硝基苯掺入蛋白质溶液再加以萃取的预处理方法。该法能迅速猝灭蛋白质分子的荧光,得到稳定清晰的拉曼图谱,而且不影响蛋白质分子的构象分析。     

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.     

RAPD技术应用于紫杉醇产生菌的分类鉴定

紫杉醇是一种用于癌症治疗的特效药物,微生物发酵法是生产紫杉醇的有效方法,采用RAPD技术对4株紫杉醇产生菌的遗传差异进行分析,重新从分子水平对其进行了进一步的分类鉴定;并将为下一步的基因工程育种工作中的基因定位和分子克隆提供丰富可靠的分子标记。     

Identification of Different Varieties of Sesame Oil Using Near-Infrared Hyperspectral Imaging and Chemometrics Algorithms

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874–1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.