Objective(s): To measure serum C-reactive protein (CRP) titer as a predictive diagnosis of acute hepatitis C virus (HCV)
infection.
Methodology: Two hundred and ten patients with acute HCV infection and 234 apparently healthy individuals as
control group were enrolled in this study in Baghdad medical city (Teaching Laboratories). The patents include
74(35.2%) females and 136 (64.8%) males with mean age (27±16.5) years. The control group includes 114 (48.7%)
females and 120 (51.3%) males with mean age (26±5.8) years. Blood samples were collected from out patients from
Alfadul in Baghdad city. Sera were separated and stored at 20 0
C. The diagnosis of acute HCV infection was based on
detection of HC Ag and anti- HCV IgM and standard liver function tests. Determination of CRP titer was assessed by
semi-quantitative tube agglutination test. All data were statistically analyzed.
Results: Based on 95% percentile, the baseline CRP titer in healthy individuals was 1:8 (16mg/l) and for patients 1:512
(1024mg/l). There was a statistically significant increase in the mean CRP titer in patients with acute HCV infection
compared to healthy individuals (P< 0.001) .The validly of CRP titer 1: 64 as a cut –off value to predict HCV infection
provide a sensitivity and specificity of 100 % and 96% respectively. Furthermore, there was a significant correlation
between CRP titer and liver function test values.
Recommendation:
Therefore, in further studies, we recommends the evaluation of C- reactive protein titer in patients with acute
hepatitis B Virus infection and patients with non–infectious diseases such as cardiovascular disease, diabetes mellitus
and hyperlipidemia infection, and compare between them.
Nonlinear time series analysis is one of the most complex problems ; especially the nonlinear autoregressive with exogenous variable (NARX) .Then ; the problem of model identification and the correct orders determination considered the most important problem in the analysis of time series . In this paper , we proposed splines estimation method for model identification , then we used three criterions for the correct orders determination. Where ; proposed method used to estimate the additive splines for model identification , And the rank determination depends on the additive property to avoid the problem of curse dimensionally . The proposed method is one of the nonparametric methods , and the simulation results give a
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