NONINVASIVE MEASUREMENT OF BLOOD ANALYTES

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 该项目的目标是使用近红外拉曼光谱法测量人类受试者血液组织基质中的临床相关分析物。这种技术可能对整个人群的医疗保健实践产生巨大影响，短期研究针对糖尿病患者的葡萄糖测量。最近，我们已经证明了检测的可行性葡萄糖浓度在体内从经皮测量的前臂进行个人校准。从这些测量中，我们已经确定了当校准算法应用于较大人群时的以下预测误差来源：（A）样品浊度的变化，（B）组织自发荧光和光漂白的存在，以及（C）血液和间质液（ISF）葡萄糖之间的生理滞后。我们以前已经完成了一种方法（称为浊度校正拉曼光谱，TCRS）的发展，用于校正浊度引起的失真，在拉曼光谱利用光子迁移的方法。我们已经证明，在应用TCRS时，从具有相同浓度的拉曼散射体但不同浊度的一组组织幻影中观察到的广泛变化的拉曼光谱倾向于显示出接近完美的合并到单个光谱轮廓上。此外，在采用具有不同浊度和随机浓度的拉曼散射体和干扰剂的物理组织模型的前瞻性研究中，通过将浊度校正程序应用于所获取的拉曼光谱，获得了20%的预测误差减少。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The goal of this project is the measurement of clinically-relevant analytes in the blood tissue matrix of human subjects using near-infrared Raman spectroscopy. Such a technology could have great impact on the healthcare practices for the entire population, with the shorter term research directed towards glucose measurements for diabetic patients. We have recently demonstrated detection feasibility of glucose concentrations in vivo from transcutaneous measurements of the forearm by performing individual calibration. From these measurements we have identified the following sources of prediction error when a calibration algorithm is applied on a larger human population: (A) variability in sample turbidity, (B) presence of tissue autofluorescence and photobleaching and (C) physiological lag between blood and interstitial fluid (ISF) glucose. We have previously completed the development of a method (called turbidity corrected Raman spectroscopy, TCRS) for the correction of turbidity-induced distortions in Raman spectra utilizing the photon migration approach. We have demonstrated that, upon application of TCRS, the widely varying Raman spectra observed from a set of tissue phantoms having the same concentration of Raman scatterers but different turbidities tend to show near perfect coalescence onto a single spectral profile. Furthermore, in a prospective study that employs physical tissue models with varying turbidities and randomized concentrations of Raman scatterers and interfering agents, a 20% reduction in prediction error is obtained by applying the turbidity correction procedure to the acquired Raman spectra.