Food Safety Risks Associated with Hepatitis E Virus Detection in Wild Boar Liver

Hepatitis E virus (HEV) has significantly impacted humans due to its potential to cause acute viral hepatitis. Discovery of hepatitis E virus in domestic pigs and wild boars worldwide and the realization that it is highly prevalent, raised concerns of the implications for food-borne transmission of HEV in Europe. Present work focusses on molecular detection of hepatitis E virus in wild boar liver samples, underlining the possible role of wildlife as a source of HEV transmission to humans. During hunting season 2016-2017, liver samples were collected from 37 wild boars in Iași and Suceava County. All tissues samples were submitted for RNA isolation followed by nested RT-PCR. Genetic characterization of wild boar HEV targeted the structural gene in the ORF2 region of hepatitis E virus genome. After specific amplification by nested RT-PCR of a 348 nt fragment from HEV ORF2, five liver samples positive for hepatitis E virus genotype 3 RNA were identified. In the present study HEV detection in Romanian fresh liver from wild boars highlights the importance of swine as a possible source of foodborne transmission. Moreover, our results along with the reviewed literature data emphasize the necessity of efficient food safety control measures implementation

Diabetes and Cancer: Is there a Link?

Cancer and diabetes are two major health problems worldwide, and incidence is increasing globally for both diseases. Type 2 diabetes is characterized by hyperinsulinemia and insulin resistance and the effect of insulin and insulin growth factor I on cancer development and progression have been demonstrated in animal and human studies. The relationship between diabetes and cancer was reported for more than 60 years. Many epidemiological studies conducted over time suggested the association between diabetes and cancer. Epidemiological studies show an increased risk in type 2 diabetic patients for colon, breast, liver, pancreas, bladder cancers and non-Hodgkin’s lymphoma, and a decrease risk for prostate cancer. Lung cancer does not appear to be related to diabetes and for renal cancer data are inconclusive. Diabetes, beside the fact that it is an independent risk factor for different type of cancer, can also have an impact on prognosis of cancer, and studies shown an increased cancer mortality in patients with diabetes

Metabolic Risk Factors in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most frequent primary malignancy of the liver and it is one of the leading causes of cancer-related deaths worldwide. The global burden of hepatocellular carcinoma is growing nowadays. Most cases of hepatocellular carcinoma develop in the background of chronic hepatitis C and B and liver cirrhosis‑well-known risk factor. But despite the reducing incidence of chronic hepatitis infections, an increase in the incidence of hepatocellular carcinoma was observed in the last decades. This could be explained by the increasing prevalence of obesity, type 2 diabetes mellitus, nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), which are becoming important risk factors in hepatocellular carcinoma. Regular surveillance, as performed for patients with viral hepatitis, is required for patients with metabolic risk factors

Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn’s Disease, Based on Confocal Laser Endomicroscopy Images

Background and Aims: Mucosal healing (MH) is associated with a stable course of Crohn’s disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator’s errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. Methods: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. Results: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. Conclusions: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time

Research and Science Today No.2

Research and Science Today Journal is a publication founded in 2011 and it is dedicated to the students of all levels (license, master and doctoral) of faculties in the country and abroad. We want to offer the participants the opportunity to present their scientific works in the following areas: Social Sciences, Economic Sciences, Legal Sciences, Humanities, Education Sciences, Engineering, Medicine and Sport. This journal provides students the opportunity to create and / or to improve their abilities to write scientific papers. So each appearance (two appearances per year at which we can add supplements) contains a number of papers written by students, masters and doctoral from the faculties from the country or / and abroad. The journal promotes original studies contributing to the progress of knowledge and it is motivated by the need to address issues of theory and practice in the areas mentioned above. The Journal is a training means of the factors involved in the conceptualization, development, implementation and evaluation , aiming the formation of creative personalities who could be able to adapt through the changing conditions of life. Journal wants to be a forum for debates disciplinaries and interdisciplinaries theoretical topics, to become a research support, to leverage this work at regional, national and international levels. We believe that this gathering will enjoy the support from both parts of the researchers and of the practitioners, and will provide appropriate training sources held professional through the current problems

Research and Science Today No.2

Research and Science Today Journal is a publication founded in 2011 and it is dedicated to the students of all levels (license, master and doctoral) of faculties in the country and abroad. We want to offer the participants the opportunity to present their scientific works in the following areas: Social Sciences, Economic Sciences, Legal Sciences, Humanities, Education Sciences, Engineering, Medicine and Sport. This journal provides students the opportunity to create and / or to improve their abilities to write scientific papers. So each appearance (two appearances per year at which we can add supplements) contains a number of papers written by students, masters and doctoral from the faculties from the country or / and abroad. The journal promotes original studies contributing to the progress of knowledge and it is motivated by the need to address issues of theory and practice in the areas mentioned above. The Journal is a training means of the factors involved in the conceptualization, development, implementation and evaluation , aiming the formation of creative personalities who could be able to adapt through the changing conditions of life. Journal wants to be a forum for debates disciplinaries and interdisciplinaries theoretical topics, to become a research support, to leverage this work at regional, national and international levels. We believe that this gathering will enjoy the support from both parts of the researchers and of the practitioners, and will provide appropriate training sources held professional through the current problems

Shear Wave Elastography in Patients with Primary and Secondary Hyperparathyroidism

Objectives: In this study, we aim to determine the elastographic characteristics of both primary and secondary hyperparathyroidism using shear wave elastography. We also aim to evaluate the elastographic differences between them, as well as the differences between the parathyroid, thyroid, and muscle tissue, in order to better identify a cutoff value for the parathyroid tissue. Methods: In this prospective study, we examined a total of 68 patients with hyperparathyroidism, divided into two groups; one group consisted of 27 patients with primary hyperparathyroidism and the other group consisted of 41 selected patients with confirmed secondary hyperparathyroidism. The elasticity index (EI) was determined in the parathyroid, thyroid, and muscle tissue. The determined values were compared to better identify the parathyroid tissue. Results: The median value of mean SWE values measured for parathyroid adenomas from primary hyperparathyroidism was 4.86 kPa. For secondary hyperparathyroidism, the median value of mean SWE was 6.96 KPa. The median (range) presurgical values for parathormone (PTH) and calcium were 762.80 pg/mL (190, 1243) and 9.40 mg/dL (8.825, 10.20), respectively. We identified significant elastographic differences between the two groups (p < 0.001), which remained significant after adjusting elastographic measures to the nonparametric parameters, such as the parathormone value and vitamin D (p < 0.001). The cutoff values found for parathyroid adenoma were 5.96 kPa and for parathyroid tissue 9.58 kPa. Conclusions: Shear wave elastography is a helpful tool for identifying the parathyroid tissue, in both cases of primary and secondary hyperparathyroidism, as there are significant differences between the parathyroid, thyroid, and muscle tissue. We found a global cutoff value for the parathyroid tissue of 9.58 kPa, but we must keep in mind that there are significant elastographic differences between cutoffs for primary and secondary hyperparathyroidism

Shear Wave Elastography in Patients with Primary and Secondary Hyperparathyroidism

Objectives: In this study, we aim to determine the elastographic characteristics of both primary and secondary hyperparathyroidism using shear wave elastography. We also aim to evaluate the elastographic differences between them, as well as the differences between the parathyroid, thyroid, and muscle tissue, in order to better identify a cutoff value for the parathyroid tissue. Methods: In this prospective study, we examined a total of 68 patients with hyperparathyroidism, divided into two groups; one group consisted of 27 patients with primary hyperparathyroidism and the other group consisted of 41 selected patients with confirmed secondary hyperparathyroidism. The elasticity index (EI) was determined in the parathyroid, thyroid, and muscle tissue. The determined values were compared to better identify the parathyroid tissue. Results: The median value of mean SWE values measured for parathyroid adenomas from primary hyperparathyroidism was 4.86 kPa. For secondary hyperparathyroidism, the median value of mean SWE was 6.96 KPa. The median (range) presurgical values for parathormone (PTH) and calcium were 762.80 pg/mL (190, 1243) and 9.40 mg/dL (8.825, 10.20), respectively. We identified significant elastographic differences between the two groups (p p < 0.001). The cutoff values found for parathyroid adenoma were 5.96 kPa and for parathyroid tissue 9.58 kPa. Conclusions: Shear wave elastography is a helpful tool for identifying the parathyroid tissue, in both cases of primary and secondary hyperparathyroidism, as there are significant differences between the parathyroid, thyroid, and muscle tissue. We found a global cutoff value for the parathyroid tissue of 9.58 kPa, but we must keep in mind that there are significant elastographic differences between cutoffs for primary and secondary hyperparathyroidism