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The present survey, promoted by the European Reference Network on rare respiratory diseases (ERN-Lung, Core Networks Sarcoidosis and ILD), aims to assess the existing sarcoidosis registries and biobanks across Europe and to compare the various types of biospecimen collected, the different procedures performed, and the sample storage conditions applied. This survey was initiated by the European Reference Network on rare respiratory diseases (ERN-Lung) Core Network "Sarcoidosis" in April 2023. The survey was launched by ERN-Lung Core Network "Sarcoidosis" in August 2023 and remained active until end of February 2024. It was disseminated to all ERN-Lung Core Network "Sarcoidosis" members (first round) and to "ILD" CN members (second round) via mail. Consent to completing the questionnaire was expressly requested before starting the online procedure. This survey raises important questions about future developments. The creation of registries and biobanks specific for sarcoidosis is mandatory to improve the knowledge of sarcoidosis pathogenesis and its management. This survey shows that the map of registries and biobanks specific for sarcoidosis in Europe is fragmented and needs merging, funding and coordination to allow high quality and structured research in sarcoidosis.
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http://dx.doi.org/10.36141/svdld.v41i4.16074 | DOI Listing |
mSystems
September 2025
Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia.
Medication usage is a known contributor to the inter-individual variability of the gut microbiome. However, medications are often used repeatedly and for long periods, a notion yet unaccounted for in microbiome studies. Recently, we and others showed that not only the usage of antibiotics and antidepressants at sampling, but also past consumption, is associated with the gut microbiome.
View Article and Find Full Text PDFIntractable Rare Dis Res
August 2025
Center for Clinical Sciences, Japan Institute for Health Security, Tokyo, Japan.
Rare skin diseases in China, recognized through the 2018 National Rare Disease List (121 conditions), pose substantial epidemiological and systemic challenges. The National Rare Diseases Registry System (NRDRS) documented 62,590 cases (2016-2020) of 166 diseases, and yet data remain fragmented: only 53.1% of rare diseases are prevalent and they are found in 94.
View Article and Find Full Text PDFBJC Rep
September 2025
Department of General Practice, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.
Background: In Ireland, cancer is a leading cause of mortality. Optimising primary care cancer research is crucial for better patient outcomes and healthcare policies. This study identifies Irish health data resources relevant to primary care cancer research, addressing a gap in understanding data availability and utility to enhance cancer care.
View Article and Find Full Text PDFEnviron Int
August 2025
Department of Growth and Reproduction, Copenhagen University Hospital - Rigshospitalet, University of Copenhagen, Denmark; International Centre for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital - Rigshospitalet,
Background: Testicular germ cell cancer (TGCC) originates during fetal life. Fetal exposure to environmental chemicals may contribute to its development, but epidemiological data are lacking. We investigated per- and polyfluoroalkyl substances (PFAS), which can act as endocrine disruptors during fetal development, and TGCC risk in adulthood.
View Article and Find Full Text PDFObjective: Our objective was to build classifiers for multiple phenotypes that categorize a cohort of adults with congenital heart disease (ACHD), that can be used to populate variables in a biobank.
Materials And Methods: A dataset of 1492 ACHD patients, with expert-created labels for eight phenotypes, was created and used to train classifiers with three different architectures. A larger unlabeled dataset containing 15869 patients was used to pre-train the classifiers, and a 20% subset of the unlabeled dataset was used to validate the classifier predictions.