Lessons learned from the COVID-19 pandemic: identifying hesitant groups and exploring reasons for vaccination hesitancy, from adolescence to late adulthood.

Introduction: The COVID-19 (COronaVIrus Disease-2019) pandemic highlighted the importance of assessing the rationales behind vaccine hesitancy for the containment of pandemics. In this nationwide study, representative of the Luxembourgish population, we identified hesitant groups from adolescence to late adulthood and explored motivations both for and against vaccination.

Methods: We combined data collected via online surveys for the CON-VINCE (COvid-19 National survey for assessing VIral spread by Non-affected CarriErs) study, 1865 respondents aged 18-84, and for the YAC (Young people And Covid-19) study, 3740 respondents aged 12-29.

The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training.

Motivation: Developing competency in the broad area of bioinformatics is challenging globally, owing to the breadth of the field and the diversity of its audiences for education and training. Course design can be facilitated by the use of a competency framework-a set of competency requirements that define the knowledge, skills and attitudes needed by individuals in (or aspiring to be in) a particular profession or role. These competency requirements can help to define curricula as they can inform both the content and level to which competency needs to be developed.

Using interactive platforms to encode, manage and explore immune-related adverse outcome pathways.

This work focuses on the need for modeling and predicting adverse outcomes in immunotoxicology to improve nonclinical assessments of the safety of immunomodulatory therapies. The integrated approach includes, first, the adverse outcome pathway concept established in the toxicology field, and, second, the systems medicine disease map approach for describing molecular mechanisms involved in a particular pathology. The proposed systems immunotoxicology workflow is illustrated with chimeric antigen receptor (CAR) T cell treatment as a use case.

Graph databases in systems biology: a systematic review.

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools.

Preferences regarding Disclosure of Risk for Parkinson's Disease in a Population-based Study.

Background: Preferences for risk disclosure in population-based studies assessing Parkinson's disease (PD) risk have not been assessed so far.

Objectives: To examine preferences for risk disclosure in a subset of the European Healthy Brain Aging (HeBA) multicenter study.

Methods: After a remote PD risk assessment, a structured pilot-questionnaire on risk disclosure was first presented to participants (≥50 years, without neurodegenerative diseases) during in-person visits at the Innsbruck study site.

Genetically stratified Parkinson's disease with freezing of gait is related to specific pattern of cognitive impairment and non-motor dominant endophenotype.

Background: Freezing of gait (FOG) is an important milestone in the individual disease trajectory of people with Parkinson's disease (PD). Based on the of FOG etiology, the mechanism behind FOG implies higher executive dysfunction in PD. To test this model, we investigated the FOG-related phenotype and cognitive subdomains in idiopathic PD (iPD) patients without genetic variants linked to PD from the Luxembourg Parkinson's study.

Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.

Parkinson's disease (PD) involves complex molecular interactions and diverse comorbidities. To better understand its molecular mechanisms, we employed systems medicine approaches using the PD map, a detailed repository of PD-related interactions and applied Probabilistic Boolean Networks (PBNs) to capture the stochastic nature of molecular dynamics. By integrating cohort-level and real-world patient data, we modeled PD's subtype-specific pathway deregulations, providing a refined representation of its molecular landscape.

Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data.

Objective: The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot.

Bridging therapy-induced phenotypes and genetic immune dysregulation to study interleukin-2-induced immunotoxicology.

Interleukin-2 (IL-2) holds promise for the treatment of cancer and autoimmune diseases, but its high-dose usage is associated with systemic immunotoxicity. Differential IL-2 receptor (IL-2R) regulation might impact function of cells upon IL-2 stimulation, possibly inducing cellular changes similar to patients with hypomorphic IL2RB mutations, presenting with multiorgan autoimmunity. Here, we show that sustained high-dose IL-2 stimulation of human lymphocytes drastically reduces IL-2Rβ surface expression especially on T cells, resulting in impaired IL-2R signaling which correlates with high IL-2Rα baseline expression.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing.

DS-PACK: Tool assembly for the end-to-end support of controlled access human data sharing.

The EU General Data Protection Regulation (GDPR) requirements have prompted a shift from centralised controlled access genome-phenome archives to federated models for sharing sensitive human data. In a data-sharing federation, a central node facilitates data discovery; meanwhile, distributed nodes are responsible for handling data access requests, concluding agreements with data users and providing secure access to the data. Research institutions that want to become part of such federations often lack the resources to set up the required controlled access processes.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.

Creation of a pandemic memory by tracing COVID-19 infections and immunity in Luxembourg (CON-VINCE).

Background: During the COVID-19 pandemic swift implementation of research cohorts was key. While many studies focused exclusively on infected individuals, population based cohorts are essential for the follow-up of SARS-CoV-2 impact on public health. Here we present the CON-VINCE cohort, estimate the point and period prevalence of the SARS-CoV-2 infection, reflect on the spread within the Luxembourgish population, examine immune responses to SARS-CoV-2 infection and vaccination, and ascertain the impact of the pandemic on population psychological wellbeing at a nationwide level.

Programme Dementia Prevention (pdp): A Nationwide Program for Personalized Prevention in Luxembourg.

Background: With continuously aging societies, an increase in the number of people with cognitive decline is to be expected. Aside from the development of causative treatments, the successful implementation of prevention strategies is of utmost importance to reduce the high societal burden caused by neurodegenerative diseases leading to dementia among which the most common cause is Alzheimer's disease.

Objective: The aim of the Luxembourgish "programme dementia prevention (pdp)" is to prevent or at least delay dementia in an at-risk population through personalized multi-domain lifestyle interventions.

Luxembourg Parkinson's study -comprehensive baseline analysis of Parkinson's disease and atypical parkinsonism.

Background: Deep phenotyping of Parkinson's disease (PD) is essential to investigate this fastest-growing neurodegenerative disorder. Since 2015, over 800 individuals with PD and atypical parkinsonism along with more than 800 control subjects have been recruited in the frame of the observational, monocentric, nation-wide, longitudinal-prospective Luxembourg Parkinson's study.

Objective: To profile the baseline dataset and to explore risk factors, comorbidities and clinical profiles associated with PD, atypical parkinsonism and controls.

The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease.

Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g.

Firalink: A bioinformatics pipeline for long non-coding RNA data analysis.

Summary: The Firalink bioinformatics pipeline has been developed to analyse long non-coding RNA (lncRNA) data generated by targeted sequencing. This pipeline has been first implemented for use with the FIMICS panel containing 2906 lncRNAs useful for investigations in cardiovascular disease. It has been subsequently tested and validated using a panel of lncRNAs targeting brain disease.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases.

Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower.

The Translational Data Catalog - discoverable biomedical datasets.

The discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission.

A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods.

FAIR in action - a flexible framework to guide FAIRification.

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts.

Analysis: Flawed Datasets of Monkeypox Skin Images.

The self-proclaimed first publicly available dataset of Monkeypox skin images consists of medically irrelevant images extracted from Google and photography repositories through a process denominated web-scrapping. Yet, this did not stop other researchers from employing it to build Machine Learning (ML) solutions aimed at computer-aided diagnosis of Monkeypox and other viral infections presenting skin lesions. Neither did it stop the reviewers or editors from publishing these subsequent works in peer-reviewed journals.