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See detailAltered infective competence of the human gut microbiome in COVID-19
de Nies, Laura; Galata, Valentina; Martin-Gallausiaux, Camille et al

in Microbiome (2023), 11(1), 46

BACKGROUND: Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have ... [more ▼]

BACKGROUND: Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. We used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. RESULTS: We found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19-positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19-positive individuals compared to healthy controls. CONCLUSIONS: Our analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients. Video Abstract. [less ▲]

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See detailSMASCH: Facilitating multi-appointment scheduling in longitudinal clinical research studies and care programs
Vega Moreno, Carlos Gonzalo UL; Gawron, Piotr UL; Lebioda, Jacek et al

Poster (2022, September 20)

Longitudinal clinical research studies require conducting various assessments over long periods of time. Such assessments comprise numerous stages, requiring different resources defined by ... [more ▼]

Longitudinal clinical research studies require conducting various assessments over long periods of time. Such assessments comprise numerous stages, requiring different resources defined by multidisciplinary research staff and aligned with available infrastructure and equipment, altogether constrained by time. While it is possible to manage the allocation of resources manually, it is complex and error-prone. Efficient multi-appointment scheduling is essential to assist clinical teams, ensuring high participant retention and producing successful clinical studies, directly impacting patient throughput and satisfaction. We present Smart Scheduling (SMASCH) system [1], a web application for multi-appointment scheduling management aiming to reduce times, optimise resources and secure personal identifiable information. SMASCH facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies and speed up management tasks. It is present in multiple clinical research and integrated care programs in Luxembourg since 2017, including Dementia Prevention Program, the study for Mild Cognitive Impairment and gut microbiome, and the National Centre of Excellence in Research on Parkinson’s disease [2] which encompasses the study for REM sleep behaviour disorder and the Luxembourg Parkinson’s Study. SMASCH is a free and open-source solution available both as a Linux package and Docker image. [less ▲]

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See detailIndividual factors and beliefs determining COVID-19 vaccination willingness
Pauly, Laure UL; Paccoud, Ivana UL; Satagopam, Venkata UL et al

Poster (2022, April)

Background: High vaccination coverage rates are necessary to reduce infections and transmissions of the SARS-CoV-2 virus causing COVID-19 and to allow successful mitigation of the current pandemic. To ... [more ▼]

Background: High vaccination coverage rates are necessary to reduce infections and transmissions of the SARS-CoV-2 virus causing COVID-19 and to allow successful mitigation of the current pandemic. To date, we are still lacking information to explain the hesitancy in Luxembourg towards uptake of the available COVID-19 vaccines. The present study explored motivations for and against vaccination in a population-representative sample of residents across Luxembourg to identify hesitant groups and develop strategies to increase population immunity against SARS-CoV-2. Methods: In the framework of the nationwide, representative longitudinal CON-VINCE study, a sample of 1589 respondents (49.6% women, 84.3% Luxembourg nationality) ranging from 18-84 years, participated in the survey in spring 2021. The protocol of the CON-VINCE study has been described in detail elsewhere (Snoeck et al. 2020). Results: 52% of the respondents had at least partial vaccination at time of assessment between April to June 2021. The most common reasons for vaccination of those willing to be vaccinated (81.2%) were altruistic motivations. Prevalent reasons against vaccination for those undecided (8.7%) or reluctant (10.2%) to be vaccinated were that the vaccine had not been tested sufficiently and the fear of long-term vaccine side effects. Only very few of the vaccination-hesitant or -reluctant respondents reported that they did not believe in vaccination in general. Conclusion: The present study identified motivations for and against COVID-19 vaccination and determined demographic and socio-economic factors associated with vaccination willingness. To increase vaccination rates, public health communication needs to target those unsure or unwilling to be vaccinated. We will continue to study the vaccination uptake in the Luxembourg population, as CON-VINCE is now part of the H2020-funded international ORCHESTRA project (https://orchestra-cohort.eu), research into comparing these results on a Pan-European level. [less ▲]

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See detailSmart Scheduling (SMASCH): multi-appointment scheduling system for longitudinal clinical research studies.
Vega Moreno, Carlos Gonzalo UL; Gawron, Piotr UL; Lebioda, Jacek UL et al

in JAMIA open (2022), 5(2), 038

OBJECTIVE: Facilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical ... [more ▼]

OBJECTIVE: Facilitate the multi-appointment scheduling problems (MASPs) characteristic of longitudinal clinical research studies. Additional goals include: reducing management time, optimizing clinical resources, and securing personally identifiable information. MATERIALS AND METHODS: Following a model view controller architecture, we developed a web-based tool written in Python 3. RESULTS: Smart Scheduling (SMASCH) system facilitates clinical research and integrated care programs in Luxembourg, providing features to better manage MASPs and speed up management tasks. It is available both as a Linux package and Docker image (https://smasch.pages.uni.lu). DISCUSSION: The long-term requirements of longitudinal clinical research studies justify the employment of flexible and well-maintained frameworks and libraries through an iterative software life-cycle suited to respond to rapidly changing scenarios. CONCLUSIONS: SMASCH is a free and open-source scheduling system for clinical studies able to satisfy recent data regulations providing features for better data accountability. Better scheduling systems can help optimize several metrics that ultimately affect the success of clinical studies. [less ▲]

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See detailSelection of data sets for FAIRification in drug discovery and development: Which, why, and how?
Alharbi, Ebtisam; Gadiya, Yojana; Henderson, David et al

in Drug Discovery Today (2022)

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long ... [more ▼]

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation. [less ▲]

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See detailEditorial: Digital Innovation and Data-Driven Research in Neurodegenerative Diseases
Gu, Wei UL; Rong, Panying; Hofmann-Apitius, Martin et al

in Frontiers in Neurology (2022), 13

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See detailTranslational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings
Vega Moreno, Carlos Gonzalo UL; Kratochvil, Miroslav UL; Satagopam, Venkata UL et al

in Bioinformatics and Biomedical Engineering (2022)

The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to ... [more ▼]

The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to computer science researchers and vice-versa. We highlight two general areas where the use of AI-based solutions designed for clinical and laboratory settings has proven problematic. These are used to demonstrate common sources of translational challenges that often stem from the differences in data interpretation between the clinical and research view, and the unmatched expectations and requirements on the result quality metrics. We outline how explicit interpretable inference reporting might be used as a guide to overcome such translational challenges. We conclude with several recommendations for safer translation of machine learning solutions into real-world settings. [less ▲]

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See detailThe BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) Glossary: developing a lingua franca to facilitate data harmonisation and cross-cohort analyses
Broderick, Conor; Christian, Nils; Apfelbacher, Christian et al

in British Journal of Dermatology (2021)

Dear Editor, BIOMAP (BIOMarkers in Atopic dermatitis and Psoriasis) is a large European consortium aiming to advance personalised medicine for atopic dermatitis and psoriasis by identifying biomarkers ... [more ▼]

Dear Editor, BIOMAP (BIOMarkers in Atopic dermatitis and Psoriasis) is a large European consortium aiming to advance personalised medicine for atopic dermatitis and psoriasis by identifying biomarkers which predict therapeutic response and disease progression. BIOMAP brings together clinicians, researchers, patient organisations and pharmaceutical industry partners and encompasses data from over 60 individual studies, including randomised clinical trials, population-based cohorts and deeply-phenotyped disease registries. The curation and harmonisation of data and bio-samples from these established studies will facilitate cross-cohort clinical and molecular analyses, increasing the potential to identify small effect estimates and to better stratify disease subtypes. This letter serves to disseminate BIOMAP's pathway to data harmonisation and will inform future collaborative research endeavours. [less ▲]

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See detailCOBREXA.jl: constraint-based reconstruction and exascale analysis
Kratochvil, Miroslav UL; Heirendt, Laurent UL; Wilken, St Elmo et al

in Bioinformatics (2021)

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of ... [more ▼]

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.https://doi.org/10.17881/ZKCR-BT30.Supplementary data are available at Bioinformatics online. [less ▲]

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See detailCOVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
Ostaszewski, Marek UL; Niarakis, Anna; Mazein, Alexander UL et al

in Molecular systems biology (2021), 17(10), 10387

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets ... [more ▼]

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective. [less ▲]

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See detailCardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129
Badimon, Lina; Robinson, Emma L.; Jusic, Amela et al

in Cardiovascular Research (2021)

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths ... [more ▼]

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [less ▲]

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See detailPredictProtein - Predicting Protein Structure and Function for 29 Years
Bernhofer, Michael; Dallago, Christian; Karl, Tim et al

in Nucleic Acids Research (2021)

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼]

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲]

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See detailProceedings of the AI4Health Lecture Series (2021)
Schommer, Christoph UL; Sauter, Thomas UL; Pang, Jun UL et al

Scientific Conference (2021)

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one ... [more ▼]

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange. [less ▲]

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See detailRoad to effective data curation for translational research
Gu, Wei UL; Hasan, Samiul; Rocca-Serra, Philippe et al

in Drug Discovery Today (2020)

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of ... [more ▼]

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the eTRIKS program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry. [less ▲]

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See detailSupporting findability of COVID-19 research with large-scale text mining of scientific publications
Welter, Danielle UL; Vega Moreno, Carlos Gonzalo UL; Biryukov, Maria UL et al

Poster (2020, November 27)

When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the ... [more ▼]

When the COVID-19 pandemic hit in early 2020, a lot of research efforts were quickly redirected towards studies on SARS-CoV2 and COVID-19 disease, from the sequencing and assembly of viral genomes to the elaboration of robust testing methodologies and the development of treatment and vaccination strategies. At the same time, a flurry of scientific publications around SARS-CoV-2 and COVID-19 began to appear, making it increasingly difficult for researchers to stay up-to-date with latest trends and developments in this rapidly evolving field. The BioKB platform is a pipeline which, by exploiting text mining and semantic technologies, helps researchers easily access semantic content of thousands of abstracts and full text articles. The content of the articles is analysed and concepts from a range of contexts, including proteins, species, chemicals, diseases and biological processes are tagged based on existing dictionaries of controlled terms. Co-occurring concepts are classified based on their asserted relationship and the resulting subject-relation-object triples are stored in a publicly accessible human- and machine-readable knowledge base. All concepts in the BioKB dictionaries are linked to stable, persistent identifiers, either a resource accession such as an Ensembl, Uniprot or PubChem ID for genes, proteins and chemicals, or an ontology term ID for diseases, phenotypes and other ontology terms. In order to improve COVID-19 related text mining, we extended the underlying dictionaries to include many additional viral species (via NCBI Taxonomy identifiers), phenotypes from the Human Phenotype Ontology (HPO), COVID-related concepts including clinical and laboratory tests from the COVID-19 ontology, as well as additional diseases (DO) and biological processes (GO). We also added all viral proteins found in UniProt and gene entries from EntrezGene to increase the sensitivity of the text mining pipeline to viral data. To date, BioKB has indexed over 270’000 sentences from 21’935 publications relating to coronavirus infections, with publications dating from 1963 to 2021, 3’863 of which were published this year. We are currently working to further refine the text mining pipeline by training it on the extraction of increasingly complex relations such as protein-phenotype relationships. We are also regularly adding new terms to our dictionaries for areas where coverage is currently low, such as clinical and laboratory tests and procedures and novel drug treatments. [less ▲]

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See detailGigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets
Kratochvil, Miroslav UL; Hunewald, Oliver; Heirendt, Laurent UL et al

in GigaScience (2020), 9(11),

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data ... [more ▼]

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. [less ▲]

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See detailBioKC: a platform for quality controlled curation and annotation of systems biology models
Vega Moreno, Carlos Gonzalo UL; Groues, Valentin UL; Ostaszewski, Marek UL et al

Scientific Conference (2020, September 04)

Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated ... [more ▼]

Standardisation of biomedical knowledge into systems biology models is essential for the study of the biological function. However, biomedical knowledge curation is a laborious manual process aggravated by the ever increasing growth of biomedical literature. High quality curation currently relies on pathway databases where outsider participation is minimal. The increasing demand of systems biology knowledge presents new challenges regarding curation, calling for new collaborative functionalities to improve quality control of the review process. These features are missing in the current systems biology environment, whose tools are not well suited for an open community-based model curation workflow. On one hand, diagram editors such as CellDesigner or Newt provide limited annotation features. On the other hand, most popular text annotations tools are not aimed for biomedical text annotation or model curation. Detaching the model curation and annotation tasks from diagram editing improves model iteration and centralizes the annotation of such models with supporting evidence. In this vain, we present BioKC, a web-based platform for systematic quality-controlled collaborative curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). [less ▲]

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See detailGenetic Architecture of Parkinson's Disease in the Indian Population: Harnessing Genetic Diversity to Address Critical Gaps in Parkinson's Disease Research.
Rajan, Roopa; Divya, K. P.; Kandadai, Rukmini Mridula et al

in Frontiers in neurology (2020), 11

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability ... [more ▼]

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our "hub-spoke" model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD. [less ▲]

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See detailPrevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study.
Snoeck, Chantal J.; Vaillant, Michel; Abdelrahman, Tamir et al

E-print/Working paper (2020)

BACKGROUND: After the World Health Organization declared the outbreak of coronavirus disease to be a public health emergency of international concern on January 30, 2020, the first SARS-CoV-2 infection ... [more ▼]

BACKGROUND: After the World Health Organization declared the outbreak of coronavirus disease to be a public health emergency of international concern on January 30, 2020, the first SARS-CoV-2 infection was detected in Luxembourg on February 29, 2020. Representative population-based data, including asymptomatic individuals for assessing the viral spread and immune response were, however, lacking worldwide. METHODS: Using a panel-based method, we implemented a representative sample of the Luxembourgish population based on age, gender and residency for testing for SARS-CoV-2 infection and antibody status in order to define prevalence irrespective of clinical symptoms. Participants were contacted via email to fill an online questionnaire before biosampling at local laboratories. All participants provided information related to clinical symptoms, epidemiology, socioeconomic and psychological assessments and underwent biosampling, rRT-PCR testing and serology for SARS-CoV-2. RESULTS: We included a total of 1862 individuals in our representative sample of the general Luxembourgish population. Of these, 5 individuals had a current positive result for infection with SARS-CoV-2 based on rRT-PCR. Four of these individuals were oligosymptomatic and one was asymptomatic. Overall we found a positive IgG antibody status in 35 individuals (1.97%), of which 11 reported to be tested positive by rRT-PCR for SARS-CoV-2 previously and showed in addition their IgG positive status also a positive status for IgA. Our data indicate a prevalence of 0.3% for active SARS-CoV-2 infection and an infection rate of 2.15% in the Luxembourgish population between 18 and 79 years of age. CONCLUSIONS: Luxembourgish residents show a low rate of acute infections after 7 weeks of confinement and present with an antibody profile indicative of a more recent immune response to SARS-CoV-2. All infected individuals were oligo- or asymptomatic. Bi-weekly follow-up visits over the next 2 months will inform about the viral spread by a- and oligosymptomatic carriers and the individual changes in the immune profile.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialNCT04379297Funding StatementThe CON-VINCE Study is funded by the Research Fund Luxembourg (FNR; CON-VINCE) and the André Losch Foundation (Luxembourg).Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesDue to ethical concerns, supporting data cannot be made openly available. [less ▲]

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See detailBioKC: a collaborative platform for systems biology model curation and annotation
Vega Moreno, Carlos Gonzalo UL; Groues, Valentin UL; Ostaszewski, Marek UL et al

in bioRxiv (2020)

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual ... [more ▼]

Curation of biomedical knowledge into standardised and inter-operable systems biology 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. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).Competing Interest StatementThe authors have declared no competing interest. [less ▲]

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