{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(9)"],"submitter":["Garcia Lopez A"],"pubmed_abstract":["Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to "],"journal":["Cell reports. Medicine"],"pagination":["101712"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11528229"],"repository":["biostudies-literature"],"pubmed_title":["Risk assessment with gene expression markers in sepsis development."],"pmcid":["PMC11528229"],"pubmed_authors":["Lukaszewski R","Bauer M","Garcia Lopez A","Singer M","Seelbinder B","Sae-Ong T","Panagiotou G","Schauble S","Giamarellos-Bourboulis EJ"],"additional_accession":[]},"is_claimable":false,"name":"Risk assessment with gene expression markers in sepsis development.","description":"Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2026-07-16T00:26:03.781Z","creation":"2025-04-04T22:48:15.485Z"},"accession":"S-EPMC11528229","cross_references":{"pubmed":["39232497"],"doi":["10.1016/j.xcrm.2024.101712"]}}