Researchers at IIT Delhi are working on a technology to devise diagnostic solutions for combating the problem of antimicrobial resistance to enable rapid diagnosis of bacterial infection and guide clinical decision making.
According to the team at IIT, the research will greatly reduce the unnecessary use of antimicrobials in diagnostic tests and minimise the development of resistance as currently there is a big knowledge gap in microbial resistance biology and the availability of biomarkers and technology for rapid diagnostics.
"Antibacterial resistance is now widely recognised as the biggest healthcare problem of this century. Due to limitations in the current microbiological methods, it is estimated that more than two-thirds of antibiotic prescriptions are unnecessary and are empirical in nature. This practice is a major cause of the emergence of AMR and its rapid spread in the last decade," said IIT professor Vivekanandan Perumal, who is the Principal Investigator (PI) of the project.
"Although the requirement of rapid pathogen identification and methods for antimicrobial susceptibility testing (AST) are well recognized, major limitations include the knowledge gaps in understanding the genomic signatures and their correlation with Antimicrobial Resistance (AMR)," he added.
The research team will focus on 4 major pathogens (Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter baumanii, Pseudomonas aeruginosa) that are often resistant to antibiotics in Indian clinical settings.
The main objectives of the research project include -- characterization of AMR among Indian isolates using whole-genome sequencing of clinical isolates and optical genome mapping for pathogen identification using a unique genome-based signature for microbial typing with the optical mapping of DNA fragments.
"The research will also look into development of methods for rapid antimicrobial susceptibility testing by carrying out the pH measurements inside spherical microgels microreactors with embedded pH-sensitive carbon dot nanosensors and identification of bacteria species and spread of AMR using clothes worn by HCWs (healthcare workers) with a rapid culture-independent method based on bacterial 16s RNA," Perumal said.
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