Occult Hepatitis C Virus Infection in Patients With Autoimmune Hepatitis

Background:

Occult hepatitis C virus infection (OCI) is recognized by finding hepatitis C virus (HCV) RNA in hepatocytes without detectable anti-HCV antibodies and viral RNA in plasma. Autoimmune hepatitis (AIH) is a chronic and generally progressive disease without exactly-identified etiology.

Objectives:

This study aimed to determine the prevalence of OCI among patients with AIH and to evaluate the tests used to rule out HCV infection in diagnosing AIH.

Patients and Methods:

Between July 2012 to February 2013, 35 Iranian patients with AIH who attended Tehran Hepatitis Center were investigated. For identifying OCI, detection of HCV RNA in both ultracentrifuged serum samples and peripheral blood mononuclear cells (PBMCs) was used. Data analysis was performed using SPSS.

Results:

Six males and 29 females with mean disease duration of 77.1 ± 39.5 month and mean age of 43.62 ± 12.67 years were investigated. All cases were negative for anti-HCV antibody and we could not find any HCV RNA in ultracentrifuged serum samples and PBMCs.

Conclusions:

With our laboratory diagnostic method, it seems that there are no cases of OCI in patients with AIH. However, we recommend further studies with more samples and more precise laboratory method.     

Effect of transcranial direct current stimulation on short-term and long-term treatment of chronic tinnitus

Objective

This study was conducted to investigate the effectiveness of anodal and cathodal methods in reducing the intensity of tinnitus and to compare them with the control.

Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients

The main aim of this study was to propose a new statistical method for evaluation of spatial malignancy distribution within Magnetic Resonance Spectroscopy (MRS) grid in Glioblastoma Multiforme patients. Voxels with different malignancy probabilities were presented as a novel MRS-based Malignancy Probability Map (MPM). For this purpose, a predictive probability-based clustering approach was developed, including the two following steps: (1) Gaussian Mixture Model, (2) Quadratic Discriminate Analysis coupled with Genetic Algorithm. Clustered probability values from two methods were then integrated to exploit the MPM. Results show that the suggested method is able to estimate the malignancy distribution with over 90% sensitivity and specificity. The proposed MRS-based MPM has an acceptable accuracy for providing useful complementary information about regional diffuse glioma malignancy, with the potential to lead to better detection of tumoral regions with high probability of malignancy. So, it also may encourage the use of additional information of this map as a tool for dose painting.