BIBBOX, a FAIR toolbox and App Store for life science research.

Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR.

Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning.

Background: Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors.

Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification.

Importance: Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning-derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists.

Objective: To evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading.

Background: Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.

Senescence markers in focal nodular hyperplasia of the liver: pathogenic considerations on the basis of immunohistochemical results.

Focal nodular hyperplasia (FNH) is a polyclonal tumour-like hepatic lesion characterised by parenchymal nodules, connective tissue septa without interlobular bile ducts, pronounced ductular reaction and inflammation. It may represent a response to local arterial hyperperfusion and hyperoxygenation resulting in oxidative stress. We aimed at obtaining closer insight into the pathogenesis of FNH with its characteristic morphologic features.

BBMRI-ERIC Negotiator: Implementing Efficient Access to Biobanks.

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized.

Interpretable survival prediction for colorectal cancer using deep learning.

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Pharmacologic IL-6Rα inhibition in cholangiocarcinoma promotes cancer cell growth and survival.

Biliary tract cancer (BTC) represents a malignant tumor of the biliary tract including cholangiocarcinoma (CCA) and the carcinoma of the gallbladder (GBC) with a 5-year survival rate between 5 and 18% due to late diagnosis and rapid disease progression. Chronic inflammation is one of the main risk factors for CCA and GBC in particular. IL-6, as a mediator of inflammation, can act through a membrane-bound receptor alpha-chain (mIL-6R, "IL-6 classic signaling") or via soluble forms (sIL-6R, "IL-6 trans-signaling").

The RD-Connect Registry & Biobank Finder: a tool for sharing aggregated data and metadata among rare disease researchers.

In rare disease (RD) research, there is a huge need to systematically collect biomaterials, phenotypic, and genomic data in a standardized way and to make them findable, accessible, interoperable and reusable (FAIR). RD-Connect is a 6 years global infrastructure project initiated in November 2012 that links genomic data with patient registries, biobanks, and clinical bioinformatics tools to create a central research resource for RDs. Here, we present RD-Connect Registry & Biobank Finder, a tool that helps RD researchers to find RD biobanks and registries and provide information on the availability and accessibility of content in each database.

IMP2/IGF2BP2 expression, but not IMP1 and IMP3, predicts poor outcome in patients and high tumor growth rate in xenograft models of gallbladder cancer.

Overexpression of the oncofetal insulin-like growth factor 2 mRNA-binding protein 2 (IMP2/IGF2BP2) has been described in different cancer types. Gallbladder carcinoma (GBC) is a rare but highly aggressive cancer entity with late clinical detection and poor prognosis. The aim of this study was to investigate the role of IMP2 in human GBC.

Conception and Implementation of an Austrian Biobank Directory Integration Framework.

Introduction: Sample collections and data are hosted within different biobanks at diverse institutions across Europe. Our data integration framework aims at incorporating data about sample collections from different biobanks into a common research infrastructure, facilitating researchers' abilities to obtain high-quality samples to conduct their research. The resulting information must be locally gathered and distributed to searchable higher level information biobank directories to maximize the visibility on the national and European levels.

From the evaluation of existing solutions to an all-inclusive package for biobanks.

The domain of biobanking has gone through many stages and as a result there are a wide range of commercial and open source software solutions available. The utilization of these software tools requires different levels of domain and technical skills for installation, configuration and ultimate us of these biobank software tools. To compound this complexity the biobanking community are required to work together in order to share knowledge and jointly build solutions to underpin the research infrastructure.

Automatic classification of histopathological diagnoses for building a large scale tissue catalogue.

In this paper an automatic classification system for pathological findings is presented. The starting point in our undertaking was a pathologic tissue collection with about 1.4 million tissue samples described by free text records over 23 years.

Analysis of biomedical data with multilevel glyphs.

Background: This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position.

Methods: In the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping.