{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen A"],"funding":["Research Corporation for Science Advancement","NIDDK NIH HHS","NHLBI NIH HHS","NIH","David and Lucile Packard Foundation","National Science Foundation","Cystic Fibrosis Research Foundation"],"pagination":["1619-1631"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8975607"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["121(9)"],"pubmed_abstract":["Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate o"],"journal":["Biophysical journal"],"pubmed_title":["Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection."],"pmcid":["PMC8975607"],"funding_grant_id":["P30 DK065988","P01 HL108808","UH3 HL123645","R01 HL136961"],"pubmed_authors":["Daftari K","Chen A","Lai SK","Pickles R","Freeman R","Wessler T","Boucher RC","Hinton K","Forest MG"],"additional_accession":[]},"is_claimable":false,"name":"Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection.","description":"Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate o","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-03T21:30:37.735Z","creation":"2025-04-03T21:30:37.735Z"},"accession":"S-EPMC8975607","cross_references":{"pubmed":["35378080"],"doi":["10.1016/j.bpj.2022.04.003"]}}