{"title":"Respiratory tract infections: an update on the complexity of bacterial diversity, therapeutic interventions and breakthroughs","authors":"Avani Panickar, Anand Manoharan, Anand Anbarasu, Sudha Ramaiah","doi":"10.1007/s00203-024-04107-z","DOIUrl":null,"url":null,"abstract":"<div><p>Respiratory tract infections (RTIs) have a significant impact on global health, especially among children and the elderly. The key bacterial pathogens <i>Streptococcus pneumoniae</i>,<i> Haemophilus influenzae</i>,<i> Klebsiella pneumoniae</i>,<i> Staphylococcus aureus</i> and non-fermenting Gram Negative bacteria such as <i>Acinetobacter baumannii and Pseudomonas aeruginosa</i> 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.</p></div>","PeriodicalId":8279,"journal":{"name":"Archives of Microbiology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Microbiology","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s00203-024-04107-z","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Research papers must make a significant and original contribution to
microbiology and be of interest to a broad readership. The results of any
experimental approach that meets these objectives are welcome, particularly
biochemical, molecular genetic, physiological, and/or physical investigations into
microbial cells and their interactions with their environments, including their eukaryotic hosts.
Mini-reviews in areas of special topical interest and papers on medical microbiology, ecology and systematics, including description of novel taxa, are also published.
Theoretical papers and those that report on the analysis or ''mining'' of data are
acceptable in principle if new information, interpretations, or hypotheses
emerge.