Discriminant Analysis of Platform Parameters of Rhesus at Different Stages of SIV/SAIDS
10.13359/j.cnki.gzxbtcm.2015.05.029
- VernacularTitle:艾滋病不同进展类型猴模型平台期指标的判别分析
- Author:
Miaomiao ZHANG
;
Boqiang ZHU
;
Ye CHENG
;
Jiantao CHEN
;
Hongyan ZHOU
;
Linchun FU
- Publication Type:Journal Article
- Keywords:
Rapid progressor;
Normal progressor;
Long-term nonprogressor;
Simian immunodefieiency virus;
Discriminative analysis;
Disease models,animal;
Rhesus
- From:
Journal of Guangzhou University of Traditional Chinese Medicine
2015;(5):914-918,922
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict the disease progression risks of healthy rhesus ( normal) and rhesus infected with simian immunodeficiency virus ( SIV) in the stages of long-term nonprogressor ( LTNP) , normal progressor ( NP) , rapid progressor ( RP) by discriminant analysis. Methods Five-year observation was carried out in SIV infected rhesus model without any intervention. The SIV infected rhesus model at the stages of LTNP, NP, RP were selected, 10 in each group, and T lymphocyte subsets and serum parameters for spleen-deficiency syndrome and kidney-deficiency syndrome in SIV infected rhesus were compared with 5 healthy monkey having the same survival time. The influence factors of different types of disease progression were screened from T cell subsets and Chinese medical syndrome indexes, and then the discriminant equation was established to predict the risks. Results White blood cell ( WBC) count and lymphocyte ( LYM) ratio were enrolled into the discriminant equation before infection, and T4 level and Log10RNA of set point were enrolled into the discriminant equation in the platform period. The test results for the uniform rate of the established discriminant function showed that the total coincidence rate of theoretic distinguish to the actual data was 57.1% , 91.2%respectively before infection and in the platform period. Conclusion The pre-infection WBC count and LYM ratio can be used as a reference for the evaluation of different types of disease progresson, and Log10RNA and T4 level at platform phase can be used as the predicting factors of different types of disease progression risk prediction.
