Accelerated prediction of virulence and antibiotic susceptibility for bacteria causing bloodstream infections using MALDI clinical diagnostics
- Funded by UK Research and Innovation (UKRI)
- Total publications:0 publications
Grant number: 2897050
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Key facts
Disease
Bacterial infection caused by Klebsiella pneumoniaStart & end year
20232027Known Financial Commitments (USD)
$0Funder
UK Research and Innovation (UKRI)Research Location
United KingdomLead Research Institution
University of BristolResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnosticsResearch Subcategory
DiagnosticsSpecial Interest Tags
N/AStudy Type
Non-ClinicalClinical Trial Details
N/ABroad Policy Alignment
PendingAge Group
Not ApplicableVulnerable Population
Not applicableOccupations of Interest
Not applicable
Abstract
In life-threatening infections, timely and appropriate antibiotics improve outcomes but testng for antibiotic sensitivities is lsow. Our novel machine learning helps detect bacterial types associated with enhanced virulence and resistance from routine MALDI mass spectrometry data. You will develop and validate this approach for clinically-important Klebsiella pneumoniae and Escherichia coli additionally performing gene knockout experiments and bioinformatics.