{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Myall AC"],"funding":["Defense Threat Reduction Agency","Chem-Bio Diagnostics program","Natural Environment Research Council"],"pagination":["2347-2355"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8388022"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["37(16)"],"pubmed_abstract":["<h4>Motivation</h4>A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human bloo"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["An OMICs-based meta-analysis to support infection state stratification."],"pmcid":["PMC8388022"],"funding_grant_id":["HDTRA1-12-D-0003-0023","NE/M01939X/1"],"pubmed_authors":["Antczak P","Perkins S","David J","Jones AR","Spencer P","Myall AC","Rushton D"],"additional_accession":[]},"is_claimable":false,"name":"An OMICs-based meta-analysis to support infection state stratification.","description":"<h4>Motivation</h4>A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human bloo","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2026-04-08T09:02:56.926Z","creation":"2026-04-08T00:38:36.409Z"},"accession":"S-EPMC8388022","cross_references":{"pubmed":["33560295"],"doi":["10.1093/bioinformatics/btab089"]}}