NONINVASIVE MEASUREMENT OF BLOOD ANALYTES

血液分析物的无创测量

• 批准号: 8364132
• 负责人: ISHAN BARMAN
• 金额: $ 2.97万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364132
• 关键词: Algorithms; Biomedical Research; Blood; Calibration; Detection; Development; Forearm; Funding; Glucose; Goals; Grant; Healthcare; Human; Individual; Intercellular Fluid; Lasers; Measurement; Methods; National Center for Research Resources; Photobleaching; Photons; Physiological; Population; Principal Investigator; Procedures; Prospective Studies; Raman Spectrum Analysis; Randomized; Research; Research Infrastructure; Resources; Sampling; Source; Technology; Tissue Model; Tissues; United States National Institutes of Health; clinically relevant; cost; diabetic patient; human subject; in vivo; migration; tissue phantom

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.

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级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测量受试者血液组织基质中与临床相关的分析物。这种技术可能会对整个人群的医疗保健实践产生重大影响，短期研究将针对糖尿病患者的血糖测量。我们最近已经证明了通过对前臂进行个体化校准，通过经皮测量检测体内葡萄糖浓度的可行性。从这些测量中，我们确定了当校准算法应用于更大的人群时，预测误差的以下来源：(A)样品浊度的可变性，(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。我们之前已经完成了一种方法(称为浑浊校正拉曼光谱，TCRS)的开发，用于利用光子迁移方法校正由于浑浊引起的拉曼光谱失真。我们已经证明，在应用TCRS时，从一组具有相同浓度的拉曼散射体但不同混浊度的组织模体观察到的差异很大的拉曼光谱往往会显示出近乎完美的融合到一个单一的光谱轮廓上。此外，在一项使用具有不同浊度和随机浓度的拉曼散射剂和干扰剂的物理组织模型的前瞻性研究中，通过对获取的拉曼光谱应用混浊校正过程，预测误差减少了20%。