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 pneumonia
  • Start & end year

    2023
    2027
  • Known Financial Commitments (USD)

    $0
  • Funder

    UK Research and Innovation (UKRI)
  • Principal Investigator

    N/A

  • Research Location

    United Kingdom
  • Lead Research Institution

    University of Bristol
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations 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.