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DETECTION OF CANCEROUS CELLS AND TISSUES BY FTIR MICROSPECTROSCOPY

DETECTION OF CANCEROUS CELLS AND TISSUES BY FTIR MICROSPECTROSCOPY
通过 FTIR 显微镜检测癌细胞和组织
批准号:
6450686
负责人:
MAX DIEM
金额:
$5.95万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2001
资助国家:
美国
项目状态:
已结题
起止时间:
2001-04-01 至 2002-03-31

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中文摘要
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英文摘要
Infrared (vibrational) absorption spectral patterns from normal cells and tissues, and from those diagnosed to be cancerous by standard pathological methods, have been reported recently by several research groups. The spectral differences between normal and abnormal samples can be described as gross spectral changes, such as glycogen depletion or increase in the spectral contributions of structural proteins in cancerous samples, and very subtle change sin the DNA/RNA vibrations that appear to be the true spectral signatures of cancer. The former changes are not specific for cancer but may occur in a variety of conditions; whereas the changes in DNA/RNA spectral features appear to be specific for dysplasia and neoplasia. The overall aim of the research described in the enclosed proposal is to establish the cause and develop the understanding of the spectral differences in the DNA/RNA spectral region between normal and abnormal samples. In order to achieve this goal, the understanding of spectral features of cells and tissues must be furthered, and the dependence of the spectra on the cell's state of maturation and differentiation, as well as other factors, such as apoptosis, necrosis and stage of the cell cycle, need to be established. Infrared spectroscopy promises to be an invaluable tool for the diagnosis of disease, since all biomolecules in cells and tissues exhibit inherent and specific infrared spectral patterns. As such, infrared spectroscopy is a diagnostic technique that does not require specific probes to detect changes in cellular composition. Thus, it is possible to distinguish states of maturation, differentiation and stages of the cell division cycle by infrared spectroscopy. A detailed understanding of these spectral results to the ability to detect disease in a completely objective, machine-based approach.
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