Prevalence and Characterization of Hepatitis B and Hepatitis C Infection among Blood Donors in Erbil

Blood transmitting infectious disease still remains a considerable global health problem. Hepatitis B virus (HBV) and hepatitis C virus (HCV) are two of the most commonly transmitted infectious agents. This prospective cross-sectional study was conducted between December, 2017, and February, 2018, at the Directorate of Blood Bank in Erbil Province, Northern Iraq. During that period, a total of 6173 blood donors donated blood; all blood donors were asked a series of questions through a structured questionnaire designed for such purpose. These patients were serologically examined for HBV and HCV. Positive blood samples were further analyzed serologically and confirmed by real-time polymerase chain reaction (RT-PCR). Among 6173 blood donors who were investigated for HBV, 7 (0.11%) and 98 (1.6%) were positive for hepatitis B urface antigen (HBs-Ag) and hepatitis B core Antibody (HBc-Ab), respectively, whereas during screening for HCV, 4 (0.06%) were positive for HCV-Ab. Coinfection (dual infection (HBV and HCV) was positive in 1 patient (0.01%). Among 98 reactive samples, 75.5% were positive for HBs antibody (HBs-Ab), the remaining 24 samples (24.5%) were regarded as occult hepatitis B infection (OBI), since they were positive for HBc-Ab, whereas negative both for HBs-Ag and HBs-Ab. The diagnosis of OBI could be confirmed by RT-PCR in 8 samples, 33% of samples. The overall incidence of HBV and HCV among examined blood donors was 0.5 %, and 0.06%, respectively. Amidst that incidence, 0.39 % were diagnosed as OBI. To prevent viral transmission through blood transfusion is needed to combine a different and sensitive method for HBV detection as well as volve tests that have high sensitivity and specificity for serological markers. Moreover, a molecular tool that is sensitive enough to detect very low copies of viral DNA must also be developed

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Computational models of liver fibrosis progression for hepatitis C virus chronic infection

Background: Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42). Methods: A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP. Results: Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients. Conclusions: The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings