{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Wang Z"],"pubmed_abstract":["<h4>Background</h4>Predicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.<h4>Methods</h4>To optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2<sup>low</sup> and Th2<sup>high</sup>CRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2<sup>high<"],"journal":["Frontiers in immunology"],"pagination":["1054201"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9811186"],"repository":["biostudies-literature"],"pubmed_title":["A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion."],"pmcid":["PMC9811186"],"pubmed_authors":["Lan F","Hao L","Wang C","Zhang N","Zhang L","Zhang S","Li Y","Zhao L","Wang Q","Zhang Y","Duan S","Bachert C","Wang X","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion.","description":"<h4>Background</h4>Predicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.<h4>Methods</h4>To optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2<sup>low</sup> and Th2<sup>high</sup>CRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2<sup>high<","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T13:53:25.022Z","creation":"2024-11-21T07:53:34.296Z"},"accession":"S-EPMC9811186","cross_references":{"pubmed":["36618395"],"doi":["10.3389/fimmu.2022.1054201"]}}