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Research identifies molecular mechanisms of antibiotic resistance in bacteria and suggests alternative treatment strategies, highlighting AI's role in precision therapy.
Drug-resistant bacteria refer to microorganisms that possess resistance to one or multiple antibiotics, rendering these medications ineffective in treating infections and significantly increasing treatment difficulty. Between 1990 and 2021, over 1 million deaths annually worldwide were directly attributed to antibiotic resistance, with an average of 4.71 million deaths indirectly associated with it. Therefore, research into the molecular mechanisms of resistant bacteria and alternative treatment methods is urgently needed. Currently identified molecular mechanisms of bacterial resistance are diverse, primarily including: production of antibiotic-inactivating enzymes, modification of drug targets, reduced cell membrane permeability, and upregulation of active efflux pump systems. To address bacterial drug resistance, we have identified various alternative therapies targeting resistant bacteria, such as phage therapy, antimicrobial peptide therapy, gene editing therapy, and nanotechnology-based treatments. This paper will elaborate on the molecular mechanisms of resistant bacteria and alternative treatment strategies, while integrating artificial intelligence and machine learning data models in medical applications to provide more efficient and precise treatment approaches.
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Xincheng Li (2025) studied this question.
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