Medical and Biological Computing Laboratory, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
Department of Bio-Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
Arch Microbiol. 2024 Aug 17;206(9):382. doi: 10.1007/s00203-024-04107-z.
Respiratory tract infections (RTIs) have a significant impact on global health, especially among children and the elderly. The key bacterial pathogens Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, Staphylococcus aureus and non-fermenting Gram Negative bacteria such as Acinetobacter baumannii and Pseudomonas aeruginosa are most commonly associated with RTIs. These bacterial pathogens have evolved a diverse array of resistance mechanisms through horizontal gene transfer, often mediated by mobile genetic elements and environmental acquisition. Treatment failures are primarily due to antimicrobial resistance and inadequate bacterial engagement, which necessitates the development of alternative treatment strategies. To overcome this, our review mainly focuses on different virulence mechanisms and their resulting pathogenicity, highlighting different therapeutic interventions to combat resistance. To prevent the antimicrobial resistance crisis, we also focused on leveraging the application of artificial intelligence and machine learning to manage RTIs. Integrative approaches combining mechanistic insights are crucial for addressing the global challenge of antimicrobial resistance in respiratory infections.
呼吸道感染（RTIs）对全球健康有重大影响，尤其是在儿童和老年人中。主要的细菌性病原体肺炎链球菌、流感嗜血杆菌、肺炎克雷伯菌、金黄色葡萄球菌和非发酵革兰氏阴性菌，如鲍曼不动杆菌和铜绿假单胞菌，与 RTIs 最常相关。这些细菌病原体通过水平基因转移进化出了多种多样的耐药机制，通常由移动遗传元件和环境获得介导。治疗失败主要是由于抗菌药物耐药和细菌结合不足，这需要开发替代治疗策略。为了克服这一问题，我们的综述主要侧重于不同的毒力机制及其导致的致病性，强调了不同的治疗干预措施来对抗耐药性。为了防止抗菌药物耐药性危机，我们还专注于利用人工智能和机器学习的应用来管理 RTIs。将机制见解结合起来的综合方法对于应对呼吸道感染中的抗菌药物耐药性这一全球性挑战至关重要。
