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CJAP ›› 2017, Vol. 33 ›› Issue (3): 282-286.doi: 10.12047/j.cjap.5460.2017.068

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Application of area percent of positive neuron and relative intensity of staining grey level in the image quantitative analysis of rat brain tissues immunohistochemistry

ZENG Zhi-gang1, QIU Hong1, ZHU Mei-ju1, ZHU Hong-zhu1, XIAO Jian-hua2   

  1. 1. School of Physical Education, Jinggangshan University, Jiangangshan 343009, China;
    2. Department of Pediatrics of the Affiliated Hospital, Jinggangshan University, Jiangangshan 343009, China
  • Received:2017-06-06 Revised:2018-02-17 Online:2017-05-28 Published:2018-06-20
  • Supported by:
    国家自然科学基金资助项目(31160217,31360255);江西省教育厅科技项目(GJJ10545)

Abstract: Objective: The acetylcholine expression in hypothalamus arcuate nucleus is detected and then the images are processed and analyzed. The features of the image quantitative analysis of immunohistochemistry (IHC) with the method combining two parameters of area percent of positive neuron (APPN) and relative intensity of staining grey level (RISGL) were investigated. Methods: Samples were the im-munohistochemical slices of acetylcholine(ACh)expression of hypothalamic arcuate nucleus cholinergic neurons in the process of exercise in-duced immunosuppression, which included twelve groups of "0 w, 2 w, 4 w, 6 w" and three groups of "control, immediately after exercise, 3 hours after exercise" in every week. IHC technology was used to detect the ACh expression. The image quantitative analysis of IHC was con-ducted in accordance with the parameters of ACh total area of positive neuron (TAPN), average intensity of staining grey level (AISGL), APPN, RISGL, APPN/RISGL. Then the differences among APPN, RISGL and traditional parameters in the quantitative analysis were com-pared and the advantages were found. Results: The changes of TAPN and APPN showed almost the same variation. Namely the corresponding significant differences could be found through these two parameters(P < 0.05), but the sensitivity and anti-interference of APPN was higher. The results of AISGL and RISGL were not coincident completely. Furthermore, with the combination of APPN and RISGL, the positive expres-sion could be reflected better than any single parameter. Conclusion: The parameters of immunohistochemical image analysis, APPN and RIS-GL, can be reliable and accurate in image quantitative analysis of IHC. The combination of APPN and RISGL can not only reflect the expres-sion of positive neurons, but also help analyze its mechanism, which is better than traditional analysis parameters.

Key words: immunohistochemistry, computer-assisted image processing, small-area analysis, grey level, rat

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