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AMR-Diag: A Novel Diagnostic Tool for Sequence Based Prediction of Antimicrobial Resistance


Description:

The emergence and spread of antimicrobial resistant (AMR) bacteria is defined as a global health problem by WHO. The situation is at its gravest in low- and middle-income countries, where antibiotic consumption is high and largely unregulated. Due to the lack of real-time diagnostics, prescription of the right antimicrobial at the right time is not always achieved. Time required for culture based identification of pathogen and phenotype-based identification of susceptibility to antimicrobials often necessitates unessential use of broad spectrum antimicrobials, which contributes to increase in resistance among pathogens. Accurate and rapid diagnostics that both identify the pathogen and provide drug susceptibility data in real-time would transform patient management and the current AMR crisis. Their application would reach broadly from primary health care centres to tertiary care hospitals, providing immediate guidance for therapeutic intervention thereby resulting in more prudent and appropriate use of antimicrobials. Taking advantage of the advances in whole genome sequencing (WGS), bioinformatics, proteomics and machine learning methods we plan to develop a decision-making tool AMR-Diag, for the detection of bacterial infection, including its resistance profile. The radical improvement comes from adopting Neural Network based learning for solving WGS data analysis problems. We will cover Extended Spectrum Beta-Lactamases (ESBLs) in Gram negative bacteria with focus on Escherichia coli, Klebsiella, and Acinetobacter. The developed method will be validated using characterized clinical isolates and finally will be evaluated on clinical samples for culture and culture-free identification of pathogens with ESBL resistance profiles. This tool will be easy to run and interpret results from, and will work on standard devices. The proposed project collaboration will strengthen already on-going research activities in each involved research group in India and Norway.


Project leader: Rafi Ahmad

Started: 2018

Ends: 2021

Category: Statlige høyskoler

Sector: UoH-sektor

Budget: 5300000

Institution: HØGSKOLEN I INNLANDET

Address: Elverum