Occult hepatitis C virus (HCV) infection (OCI) is described as the presence of viral genome in both hepatocytes and peripheral blood mononuclear cells (PBMCs) despite constant negative results on serum HCV RNA tests. Betathalassemia major (BTM) describes a group of inherited blood diseases. Patients with BTM require repeated blood transfusions, increasing the risk of exposure to infectious agents. We aimed to assess the prevalence of OCI in Iranian BTM patients and to identify the role of host factors in OCI positivity. A total of 181 BTM patients with HCV negative markers were selected. HCV RNA was tested in PBMCs using nested polymerase chain reaction assay. The positive samples were then genotyped via restriction fragment-length polymorphism (RFLP) and 5′-untranslated region sequencing. Six (3.3%) out of 181 BTM patients had viral HCV genomes in PBMC samples. Three (50.0%), two (33.3%), and one (16.7%) out of these six patients were infected with HCV-1b, HCV-1a, and HCV-3a, respectively. OCI positivity was significantly associated with the serum level of uric acid (P = 0.045) and ABO blood group (P = 0.032). Also, OCI patients had unfavorable IFNL3 rs12979860 TT, IFNL3 rs8099917 GG, IFNL3 rs12980275 GG, and IFNL4 ss469415590 ΔG/ΔG genotypes. In conclusion, we indicated the low frequency of OCI in BTM patients. Nevertheless, more attention is warranted considering the importance of this infection. Also, further studies are necessary to determine the actual prevalence of OCI among BTM patients in Iran.

Purpose The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran. Design/methodology/approach For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output. Findings After modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R2 of 0.939 was the most precise model for predicting the PPV in the present study. Originality/value In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

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