Nanoparticle or conventional adjuvants: which one improves immune response against Brucellosis?

Objective(s): Brucellosis is a common infectious disease among animals and humans. While subunit vaccines could be used as an efficient strategy against pathogens, they usually seem to be less immunogenic than live or killed vaccines. However, the use of a suitable adjuvant accompanied by subunit vaccines can be a good alternative to enhance the immune response.Materials and Methods: To find a proper adjuvant against Brucellosis, the immune response of induced mice by Aluminum Hydroxide (AH), Incomplete Freund (IFA), and Chitosan Nanoparticle (CS) adjuvants in individuals and in combination with CS were assessed.Results: Immunization with CS stimulated higher interferon gamma (IFN-γ) immunity, while there were no significant differences between rOMP25 (IFA), rOMP25 (AH), rOMP25 (AH-CS) and rOMP25 (IFA-CS) recombinant proteins. Tumor necrosis factor alpha (TNF-α) analysis revealed there were no significant differences between immunized groups and the positive control group, except for the treatment formulated in single IFA. Furthermore, unlike IFN-γ, there was a reverse interleukin-4 (IL-4) immune response trend for treatments, as rOMP25 (CS) displayed the lowest response. rOMP25 (CS) induced higher titer of total antibody than the other ones. Although the recombinant proteins emulsified in different adjuvants induced similar titer of IgG1 antibody, the ones that were formulated in CS, IFA and IFA-CS showed a higher titer of IgG2a. The cell proliferation assay demonstrating the antigen-specific cell proliferative response could be promoted after immunization with CS. Conclusion: CS whether single or in combination with IF adjuvants has potential to improve Th1-Th2 responses

The FAIR Cookbook - the essential resource for and by FAIR doers

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.We thank all book dash participants and recipe authors, as well as the FAIRplus fellows, all partners, and the members of the FAIRplus Scientific Advisory Board, and the management team. In particular we acknowledge a number of colleagues for their role in the FAIRplus project, in particular: Ebitsam Alharbi (0000-0002-3887-3857), Oya Deniz Beyan (0000-0001-7611-3501), Ola Engkvist (0000-0003-4970-6461), Laura Furlong (0000-0002-9383-528X), Carole Goble (0000-0003-1219-2137), Mark Ibberson (0000-0003-3152-5670), Manfred Kohler, Nick Lynch (0000-0002-8997-5298), Scott Lusher (0000-0003-2401-4223), Jean-Marc Neefs, George Papadotas, Manuela Pruess (0000-0002-6857-5543), Ratnesh Sahay, Rudi Verbeeck (0000-0001-5445-6095), Bryn Williams-Jones, and Gesa Witt (0000-0003-2344-706X). This work and the authors were primarily funded by FAIRplus (IMI 802750). PRS and SAS also acknowledge contributions from the following grants (the FAIR Cookbook is also embedded in or connected to): ELIXIR Interoperability Platform, EOSC-Life (H2020-EU 824087), FAIRsharing (Wellcome 212930/Z/18/Z), NIH CFDE Coordinating Center (NIH Common Fund OT3OD025459-01), Precision Toxicology (H2020-EU 965406), UKRI DASH grant (MR/V038966/1), BY-COVID (Horizon-EU 101046203), AgroServ (Horizon-EU 101058020).Peer Reviewed"Article signat per 33 autors/es: Philippe Rocca-Serra, Wei Gu, Vassilios Ioannidis, Tooba Abbassi-Daloii, Salvador Capella-Gutierrez, Ishwar Chandramouliswaran, Andrea Splendiani, Tony Burdett, Robert T. Giessmann, David Henderson, Dominique Batista, Ibrahim Emam, Yojana Gadiya, Lucas Giovanni, Egon Willighagen, Chris Evelo, Alasdair J. G. Gray, Philip Gribbon, Nick Juty, Danielle Welter, Karsten Quast, Paul Peeters, Tom Plasterer, Colin Wood, Eelke van der Horst, Dorothy Reilly, Herman van Vlijmen, Serena Scollen, Allyson Lister, Milo Thurston, Ramon Granell, the FAIR Cookbook Contributors & Susanna-Assunta Sansone"Postprint (published version

Secondary to primary data

Secondary to primary data from different datasource

Recommendations for the analysis of gene expression data to identify intrinsic differences between similar tissues

Contains fulltext : 221565.pdf (Publisher’s version ) (Open Access

BioDataFuse: Enhancing Data Interoperability through Modular Queries and Knowledge Graph Construction

In biological research, integrating experimental data with publicly available resources is pivotal for understanding complex biological mechanisms. However, this process is often intricate and time-consuming due to the complexity and diversity of data. Furthermore, the lack of consistent harmonization across different data types complicates the management of disparate data formats and sources. Addressing this, we introduce BioDataFuse, a query-based Python tool for seamless integration of biomedical data resources. BioDataFuse establishes a modular framework for efficient data wrangling, enabling context-specific knowledge graph creation and supporting graph-based analyses. With a user-friendly interface, it enables users to dynamically create knowledge graphs from their input experimental data. Supported by a robust Python package, pyBiodatafuse, this tool excels in data harmonization, aggregating diverse sources through modular queries. Moreover, BioDataFuse provides plugin capabilities for Cytoscape and Neo4j, allowing local graph hosting. Ongoing refinements enhance the graph utility through tasks like link prediction, making BioDataFuse a versatile solution for efficient and effective biological data integration

bridgedb/datasources: Release 20231102

<p>Two new species, two new datasources, an updated InterPro URL pattern, and updated SwissLipids Bioregistry.io prefix.</p&gt

From spreadsheet lab data templates to knowledge graphs:A FAIR data journey in the domain of AMR research

While awareness of FAIR (Findable, Accessible, Interoperable, and Reusable) principles has expanded across diverse domains, there remains a notable absence of impactful narratives regarding the practical application of FAIR data. This gap is particularly evident in the context of in-vitro and in-vivo experimental studies associated with the drug discovery and development process. Despite the structured nature of these data, reliance on classic methods such as spreadsheet-based visualization and analysis has limited the long-term reuse opportunities for such datasets. In response to this challenge, our work presents a representative journey towards FAIR data, characterized by structured, conventional spreadsheet-based lab data templates and the adoption of a knowledge graph framework for breaking data silos in the field of early antimicrobial resistance research. Here, we illustrate a tailored application of a "FAIRification framework" facilitating the practical implementation of FAIR principles. By showcasing the feasibility and benefits of transitioning to FAIR data practices, our work aims to encourage broader adoption and integration of FAIR principles within a research lab setting

Quantitative analysis of myofiber type composition in human and mouse skeletal muscles

Skeletal muscles are composed of different myofiber types characterized by the expression of myosin heavy chain isoforms, which can be affected by physical activity, aging, and pathological conditions. Here, we present a step-by-step high-throughput semi-automated approach for performing myofiber type quantification of entire human or mouse muscle tissue sections, including immunofluorescence staining, image acquisition, processing, and quantification. For complete details on the use and execution of this protocol, please refer to Abbassi-Daloii et al. (2022).1</p

Quantitative analysis of myofiber type composition in human and mouse skeletal muscles

Skeletal muscles are composed of different myofiber types characterized by the expression of myosin heavy chain isoforms, which can be affected by physical activity, aging, and pathological conditions. Here, we present a step-by-step high-throughput semi-automated approach for performing myofiber type quantification of entire human or mouse muscle tissue sections, including immunofluorescence staining, image acquisition, processing, and quantification. For complete details on the use and execution of this protocol, please refer to Abbassi-Daloii et al. (2022).1Pattern Recognition and Bioinformatic