MBZUAI researchers developed a machine-learning method to predict antimicrobial resistance (AMR) by analyzing electronic health records. The system predicts if a patient will experience AMR when prescribed an antibiotic or if infected with a bacterium. Published in Scientific Reports, the innovation helps physicians identify patients at risk for AMR by using patient demographics, lab results, and physician notes. Why it matters: This approach can help combat the rise of drug-resistant bacteria by providing timely predictions and supporting more informed prescription decisions.
KAUST's Women to Impact (WTI) initiative announced the winners of its Resilience Challenge, a global competition seeking tech-based solutions for building resilience in local ecosystems. The challenge, sponsored by SEDCO Holding, was part of KAUST's Winter Enrichment Program. First place went to AI-AMRS for their AI-based solution to antimicrobial resistance, while second and third place went to SandX/BiocharX for aridland agriculture and takeAbreath for stress management respectively. Why it matters: The challenge highlights KAUST's commitment to fostering innovation and supporting women in STEM, while addressing pressing global issues like climate change, food security, and health.
KAUST researchers have discovered that combining ultraviolet sunlight with phages increases the susceptibility of antibiotic-resistant bacteria to sunlight disinfection. This breakthrough addresses the growing threat of antimicrobial resistance, as the rate of discovering new antibiotics has slowed. The team demonstrated this method's effectiveness against a pathogenic E. coli strain found in Saudi wastewater. Why it matters: This research offers a promising alternative to traditional antibiotics, particularly relevant in regions like Singapore and the GCC where treated wastewater is a crucial water supply source.