Transcribed ultraconserved noncoding RNAs (T-UCR) are involved in Barrett's esophagus carcinogenesis.

Barretts esophagus (BE) involves a metaplastic replacement of native esophageal squamous epithelium (Sq) by columnar-intestinalized mucosa, and it is the main risk factor for Barrett-related adenocarcinoma (BAc). Ultra-conserved regions (UCRs) are a class non-coding sequences that are conserved in humans, mice and rats. More than 90% of UCRs are transcribed (T-UCRs) in normal tissues, and are altered at transcriptional level in tumorigenesis. To identify the T-UCR profiles that are dysregulated in Barretts mucosa transformation, microarray analysis was performed on a discovery set of 51 macro-dissected samples obtained from 14 long-segment BE patients. Results were validated in an independent series of esophageal biopsy/surgery specimens and in two murine models of Barretts esophagus (i.e. esophagogastric-duodenal anastomosis). Progression from normal to BE to adenocarcinoma was each associated with specific and mutually exclusive T-UCR signatures that included up-regulation of uc.58-, uc.202-, uc.207-, and uc.223- and down-regulation of uc.214+. A 9 T-UCR signature characterized BE versus Sq (with the down-regulation of uc.161-, uc.165-, and uc.327-, and the up-regulation of uc.153-, uc.158-, uc.206-, uc.274-, uc.472-, and uc.473-). Analogous BE-specific T-UCR profiles were shared by human and murine lesions. This study is the first demonstration of a role for T-UCRs in the transformation of Barretts mucosa

the isolpharm project a new isol production method of high specific activity beta emitting radionuclides as radiopharmaceutical precursors

The ISOLPHARM project explores the feasibility of exploiting an innovative technology to produce extremely high specific activity beta-emitting radionuclides as radiopharmaceutical precursors. This technique is expected to produce radiopharmaceuticals that are virtually mainly impossible to obtain in standard production facilities, at lower cost and with less environmental impact than traditional techniques. The groundbreaking ISOLPHARM method investigated in this project has been granted an international patent (INFN). As a component of the SPES (Selective Production of Exotic Species) project at the Istituto Nazionale di Fisica Nucleare–Laboratori Nazionali di Legnaro (INFN–LNL), a new facility will produce radioactive ion beams of neutron-rich nuclei with high purity and a mass range of 80–160 amu. The radioactive isotopes will result from nuclear reactions induced by accelerating 40 MeV protons in a cyclotron to collide on a target of UC[Formula: see text]. The uranium in the target material will be [Formula: see text]U, yielding radioactive isotopes that belong to elements with an atomic number between 28 and 57. Isotope separation on line (ISOL) is adopted in the ISOLPHARM project to obtain pure isobaric beams for radiopharmaceutical applications, with no isotopic contaminations in the beam or subsequent trapping substrate. Isobaric contaminations may potentially affect radiochemical and radionuclide purity, but proper methods to separate chemically different elements can be developed

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

A multi-centre polyp detection and segmentation dataset for generalisability assessment

Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation

Modulation of RANTES expression by HCV core protein in liver derived cell lines

<p>Abstract</p> <p>Background</p> <p>Hepatitis C virus (HCV) infection is associated with high percentage of chronicity which implies the ability of the virus to evade or modulate host cell immune system. Modulation of chemokines, such as RANTES may be part of the virus induced pathogenicity. We examined the effect of core and structural proteins of HCV on RANTES expression in two liver derived cell lines, HepG2 and Chang Liver (CHL).</p> <p>Methods</p> <p>HepG2 and Chang Liver (CHL) cell lines were established and selected for constitutive expression of HCV core and structural genes. Flow cytometry and quantitative RT-PCR analysis were performed to examine the effect of HCV core protein on RANTES expression. Luciferase analysis after RANTES-Luc-promoter transfection of established cell lines was assayed by luminometer measurements (RLU) of RANTES promoter activity. IRF-1 and IRF-7 expression was then examined by immunoblotting analysis.</p> <p>Results</p> <p>Results of flow cytometry and RT-PCR analysis indicated that RANTES is differentially regulated by HCV core protein in the two cell lines examined as its expression was inhibited in HepG2 cells, by a reduction of RANTES promoter activity. Conversely, RANTES protein and mRNA were induced by the core protein in CHL cells, through the induction of the promoter.</p> <p>Since HCV genome modulates IRF-1 and IRF-7 in replicon system and IRF-1, IRF-3 and IRF-7 have been reported to regulate RANTES promoter in various cell systems, analysis of the mechanism underlying RANTES modulation by the core protein revealed that IRF-1 expression was induced in HepG2 cells by the core protein, whereas in CHL cells it was expressed at a very low level that was not influenced by transfection with the core protein construct. This suggested that IRF-1 level may mediate the expression of RANTES in cell lines of liver origin. The effect of the core protein on RANTES promoter was countered by co-transfection with NF90, a double-stranded-RNA binding protein that activates some interferon response genes and acts as a component of cell defense against viral infection.</p> <p>Conclusion</p> <p>HCV core protein have opposite effects on the expression of RANTES in different cell types <it>in vitro</it>, possibly reflecting a similar scenario in different microenvironments <it>in vivo</it>.</p

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods