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Red blood cells raman spectroscopy comparison of type two diabetes patients and rats

  • Lei Wang
  • , Gui Dong Liu*
  • , Xin Mu
  • , Hong Bin Xiao
  • , Chao Qi
  • , Si Qi Zhang
  • , Wen Ying Niu
  • , Guang Kun Jiang
  • , Yue Nan Feng
  • , Jing Qi Bian
  • *Corresponding author for this work
  • Heilongjiang University of Traditional Chinese Medicine
  • School of Electrical Engineering and Automation, Harbin Institute of Technology
  • Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

By using confocal Raman spectroscopy, Raman spectra were measured in normal rat red blood cells, normal human red blood cells, STZ induced diabetetic rats red blood cells, Alloxan induced diabetetic rats red blood cells and human type 2 diabetes red blood cells. Then principal component analysis (PCA) with support vector machine (SVM) classifier was used for data analysis, and then the distance between classes was used to judge the degree of close to two kinds of rat model with type 2 diabetes. The results found significant differences in the Raman spectra of red blood cell in diabetic and normal red blood cells. To diabetic red blood cells, the peak in the amide VI C=O deformation vibration band is obvious, and amide VN-H deformation vibration band spectral lines appear deviation. Belong to phospholipid fatty acyl C-C skeleton, the 1 130 cm-1 spectral line is enhanced and the 1 088 cm-1 spectral line is abated, which show diabetes red cell membrane permeability increased. Raman spectra of PCA combined with SVM can well separate 5 types of red blood cells. Classifier test results show that the classification accuracy is up to 100%. Through the class distance between the two induced method and human type 2 diabetes, it is found that STZ induced model is more close to human type 2 diabetes. In conclusion, Raman spectroscopy can be used for diagnosis of diabetes and rats STZ induced diabetes method is closer to human type 2 diabetes.

Original languageEnglish
Pages (from-to)2776-2780
Number of pages5
JournalGuang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
Volume35
Issue number10
DOIs
StatePublished - 1 Oct 2015
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Principal component analysis
  • Raman spectroscopy
  • Red blood cell
  • Support vector machine

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