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Background & Aim. Wilson's disease (WD) is a rare autosomal recessive disease. More than 500 mutations have been described so far, out of which 29 in exon 14. H1069Q mutation in the exon 14 of ATP7B gene is the most frequently encountered in Europe. The aim of the present study was to evaluate the incidence of mutations occurring in exon 14 of ATP7B gene in Romanian patients referred to a tertiary gastroenterology center, with known or suspected WD and in asymptomatic first degree relatives of index cases. methods: 93 patients were included in the study. Exon 14 of ATP7B gene has been amplified by PCR from genomic DNA and mutations identified by sequencing. Results: Only H1069Q missense mutation was detected in our study group. In patients with an established diagnosis of WD (38 cases), 34.2% were heterozygous for H1069Q and 21.1% were homozygous, with an allelic frequency of 38.1%. In paediatric WD patients (12 cases) 25% were heterozygous and 16.7% were homozygous (not significant versus adult population). Among asymptomatic first degree relatives of patients with WD (12 siblings, 25 parents) there were 40.5% cases heterozygous for H1069Q. In patients with suspected WD (17 cases), only 5.9% were heterozygous and no homozygous patient was identified. In our study group, H1069Q screening alone could not raise the Leipzig score to confirm diagnosis in patients with suspected WD or in asymptomatic first degree relatives. Conclusion: H1069Q mutation is highly prevalent in Romanian WD patients and first degree relatives, similar to other central and continental western European populations. [ABSTRACT FROM AUTHOR]

Background & Aims: By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis., Methods: We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns., Results: The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85., Conclusions: Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses., (Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.)