{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(3)"],"submitter":["Hong Y"],"pubmed_abstract":["<h4>Background</h4>Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity.<h4>Methods</h4>A cohort of subjects diagnosed with severe COVID-19 pneumonia at 12 participating hospitals between December 2022 and March 2023 was prospectively screened for eligibility. Context-aware self-supervised representation learning (CSRL) was employed to extract intricate features from CT images. Quantification of plasma proteins was achieved using the Olink® inflammation panel. A deep learning model was meticulously trained, with CSRL features serving as input and the prote"],"journal":["Journal of intensive medicine"],"pagination":["252-261"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12417366"],"repository":["biostudies-literature"],"pubmed_title":["Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia."],"pmcid":["PMC12417366"],"pubmed_authors":["Zhang G","Fang H","Lin R","Fang J","Gong R","Huang J","Zhang J","Zhong C","Liu N","Yu J","Zhao Z","Cheng Y","Yu H","Liu Y","Zhan YX","Zheng YH","Zheng C","Wu R","Chen X","Fan B","Yu Y","Sun K","Zheng Y","Yang J","Xu P","Chen J","Ni H","Chen L","Ye B","Yuan L","Zheng S","Chen C","Zhou X","Qian X","Huang ZH","Ye L","Hong Y","Pan XM","Zhang Z","Chinese Multi-omics Advances In Sepsis (CMAISE) Consortium","Han C"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia.","description":"<h4>Background</h4>Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity.<h4>Methods</h4>A cohort of subjects diagnosed with severe COVID-19 pneumonia at 12 participating hospitals between December 2022 and March 2023 was prospectively screened for eligibility. Context-aware self-supervised representation learning (CSRL) was employed to extract intricate features from CT images. Quantification of plasma proteins was achieved using the Olink® inflammation panel. A deep learning model was meticulously trained, with CSRL features serving as input and the prote","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-06-02T17:46:09.226Z","creation":"2026-05-27T03:07:49.179Z"},"accession":"S-EPMC12417366","cross_references":{"pubmed":["40933743"],"doi":["10.1016/j.jointm.2024.11.001"]}}