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Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research
Within the GEN-COVID Multicenter Study, biospecimens from more than 1000 SARS-CoV-2 positive individuals have thus far been collected in the GEN-COVID Biobank (GCB). Sample types include whole blood, plasma, serum, leukocytes, and DNA. The GCB links samples to detailed clinical data available in the...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811682/ https://www.ncbi.nlm.nih.gov/pubmed/33456056 http://dx.doi.org/10.1038/s41431-020-00793-7 |
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author | Daga, Sergio Fallerini, Chiara Baldassarri, Margherita Fava, Francesca Valentino, Floriana Doddato, Gabriella Benetti, Elisa Furini, Simone Giliberti, Annarita Tita, Rossella Amitrano, Sara Bruttini, Mirella Meloni, Ilaria Pinto, Anna Maria Raimondi, Francesco Stella, Alessandra Biscarini, Filippo Picchiotti, Nicola Gori, Marco Pinoli, Pietro Ceri, Stefano Sanarico, Maurizio Crawley, Francis P. Birolo, Giovanni Renieri, Alessandra Mari, Francesca Frullanti, Elisa |
author_facet | Daga, Sergio Fallerini, Chiara Baldassarri, Margherita Fava, Francesca Valentino, Floriana Doddato, Gabriella Benetti, Elisa Furini, Simone Giliberti, Annarita Tita, Rossella Amitrano, Sara Bruttini, Mirella Meloni, Ilaria Pinto, Anna Maria Raimondi, Francesco Stella, Alessandra Biscarini, Filippo Picchiotti, Nicola Gori, Marco Pinoli, Pietro Ceri, Stefano Sanarico, Maurizio Crawley, Francis P. Birolo, Giovanni Renieri, Alessandra Mari, Francesca Frullanti, Elisa |
author_sort | Daga, Sergio |
collection | PubMed |
description | Within the GEN-COVID Multicenter Study, biospecimens from more than 1000 SARS-CoV-2 positive individuals have thus far been collected in the GEN-COVID Biobank (GCB). Sample types include whole blood, plasma, serum, leukocytes, and DNA. The GCB links samples to detailed clinical data available in the GEN-COVID Patient Registry (GCPR). It includes hospitalized patients (74.25%), broken down into intubated, treated by CPAP-biPAP, treated with O(2) supplementation, and without respiratory support (9.5%, 18.4%, 31.55% and 14.8, respectively); and non-hospitalized subjects (25.75%), either pauci- or asymptomatic. More than 150 clinical patient-level data fields have been collected and binarized for further statistics according to the organs/systems primarily affected by COVID-19: heart, liver, pancreas, kidney, chemosensors, innate or adaptive immunity, and clotting system. Hierarchical clustering analysis identified five main clinical categories: (1) severe multisystemic failure with either thromboembolic or pancreatic variant; (2) cytokine storm type, either severe with liver involvement or moderate; (3) moderate heart type, either with or without liver damage; (4) moderate multisystemic involvement, either with or without liver damage; (5) mild, either with or without hyposmia. GCB and GCPR are further linked to the GCGDR, which includes data from whole-exome sequencing and high-density SNP genotyping. The data are available for sharing through the Network for Italian Genomes, found within the COVID-19 dedicated section. The study objective is to systematize this comprehensive data collection and begin identifying multi-organ involvement in COVID-19, defining genetic parameters for infection susceptibility within the population, and mapping genetically COVID-19 severity and clinical complexity among patients. |
format | Online Article Text |
id | pubmed-7811682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-78116822021-01-18 Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research Daga, Sergio Fallerini, Chiara Baldassarri, Margherita Fava, Francesca Valentino, Floriana Doddato, Gabriella Benetti, Elisa Furini, Simone Giliberti, Annarita Tita, Rossella Amitrano, Sara Bruttini, Mirella Meloni, Ilaria Pinto, Anna Maria Raimondi, Francesco Stella, Alessandra Biscarini, Filippo Picchiotti, Nicola Gori, Marco Pinoli, Pietro Ceri, Stefano Sanarico, Maurizio Crawley, Francis P. Birolo, Giovanni Renieri, Alessandra Mari, Francesca Frullanti, Elisa Eur J Hum Genet Article Within the GEN-COVID Multicenter Study, biospecimens from more than 1000 SARS-CoV-2 positive individuals have thus far been collected in the GEN-COVID Biobank (GCB). Sample types include whole blood, plasma, serum, leukocytes, and DNA. The GCB links samples to detailed clinical data available in the GEN-COVID Patient Registry (GCPR). It includes hospitalized patients (74.25%), broken down into intubated, treated by CPAP-biPAP, treated with O(2) supplementation, and without respiratory support (9.5%, 18.4%, 31.55% and 14.8, respectively); and non-hospitalized subjects (25.75%), either pauci- or asymptomatic. More than 150 clinical patient-level data fields have been collected and binarized for further statistics according to the organs/systems primarily affected by COVID-19: heart, liver, pancreas, kidney, chemosensors, innate or adaptive immunity, and clotting system. Hierarchical clustering analysis identified five main clinical categories: (1) severe multisystemic failure with either thromboembolic or pancreatic variant; (2) cytokine storm type, either severe with liver involvement or moderate; (3) moderate heart type, either with or without liver damage; (4) moderate multisystemic involvement, either with or without liver damage; (5) mild, either with or without hyposmia. GCB and GCPR are further linked to the GCGDR, which includes data from whole-exome sequencing and high-density SNP genotyping. The data are available for sharing through the Network for Italian Genomes, found within the COVID-19 dedicated section. The study objective is to systematize this comprehensive data collection and begin identifying multi-organ involvement in COVID-19, defining genetic parameters for infection susceptibility within the population, and mapping genetically COVID-19 severity and clinical complexity among patients. Springer International Publishing 2021-01-17 2021-05 /pmc/articles/PMC7811682/ /pubmed/33456056 http://dx.doi.org/10.1038/s41431-020-00793-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Daga, Sergio Fallerini, Chiara Baldassarri, Margherita Fava, Francesca Valentino, Floriana Doddato, Gabriella Benetti, Elisa Furini, Simone Giliberti, Annarita Tita, Rossella Amitrano, Sara Bruttini, Mirella Meloni, Ilaria Pinto, Anna Maria Raimondi, Francesco Stella, Alessandra Biscarini, Filippo Picchiotti, Nicola Gori, Marco Pinoli, Pietro Ceri, Stefano Sanarico, Maurizio Crawley, Francis P. Birolo, Giovanni Renieri, Alessandra Mari, Francesca Frullanti, Elisa Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title | Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title_full | Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title_fullStr | Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title_full_unstemmed | Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title_short | Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research |
title_sort | employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing covid-19 research |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811682/ https://www.ncbi.nlm.nih.gov/pubmed/33456056 http://dx.doi.org/10.1038/s41431-020-00793-7 |
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