Literature DB >> 19626883

[Study on malignant and normal rectum tissues using IR and 1H and 31P NMR spectroscopy].

Xiu-xiang Gao1, Hong-wei Yao, Jun-kai Du, Mei-xian Zhao, Jian Qi, Hui-zhen Li, Qing-hua Pan, Yi-zhuang Xu, Jin-guang Wu.   

Abstract

In the present paper, NMR spectroscopy, an effective tool to detect the variation in, molecular structure and changes in chemical composition of metabolites in tissues, was used to study the differences between malignant and normal tissues from rectum. 1H and 31P spectra of seven malignant rectum tissue samples and five normal control tissues were investigated by using a 300 M NMR spectrometers and compared with the results of the infrared spectra of normal and malignant rectum organ tissues. The results indicate that the 1H and 31P spectra of rectum cancer tissues are significantly different from those of the normal controls and most differences present in the form of variation in relative intensities of the characteristic peaks of various metabolites. Systematic differences in the NMR spectra between malignant tissues and normal controls are as follows: in the 1H NMR spectra, differences lie in fatty acids with the concentration of fatty acid decreasing significantly in malignant tissues. In the 31P NMR spectra, differences lie in phospholipid, with the chemical shift of phospholipid decreasing significantly in malignant tissues. This phenomenon may reflect the fact that the activity of protein synthesis is enhanced in cancerous tissues. The difference in the chemical shift of phospholipid between normal rectal tissue and malignant tissue may be considered as a detection criterion. Therefore, the above spectral variations in 31P NMR spectra may be utilized as a potential tool to diagnose rectum cancer.

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Year:  2009        PMID: 19626883

Source DB:  PubMed          Journal:  Guang Pu Xue Yu Guang Pu Fen Xi        ISSN: 1000-0593            Impact factor:   0.589


  1 in total

1.  Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier.

Authors:  Qingbo Li; Can Hao; Xue Kang; Jialin Zhang; Xuejun Sun; Wenbo Wang; Haishan Zeng
Journal:  Sensors (Basel)       Date:  2017-11-27       Impact factor: 3.576

  1 in total

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