Unveiling the Secrets of Acinetobacter baumannii : Resistance, Current Treatments, and Future Innovations.
Andrea MarinoEgle AugelloStefano StracquadanioCarlo Maria BellancaFederica CosentinoSerena SpampinatoGiuseppina CantarellaRenato BernardiniStefania StefaniBruno CacopardoGiuseppe NunnariPublished in: International journal of molecular sciences (2024)
Acinetobacter baumannii represents a significant concern in nosocomial settings, particularly in critically ill patients who are forced to remain in hospital for extended periods. The challenge of managing and preventing this organism is further compounded by its increasing ability to develop resistance due to its extraordinary genomic plasticity, particularly in response to adverse environmental conditions. Its recognition as a significant public health risk has provided a significant impetus for the identification of new therapeutic approaches and infection control strategies. Indeed, currently used antimicrobial agents are gradually losing their efficacy, neutralized by newer and newer mechanisms of bacterial resistance, especially to carbapenem antibiotics. A deep understanding of the underlying molecular mechanisms is urgently needed to shed light on the properties that allow A. baumannii enormous resilience against standard therapies. Among the most promising alternatives under investigation are the combination sulbactam/durlobactam, cefepime/zidebactam, imipenem/funobactam, xeruborbactam, and the newest molecules such as novel polymyxins or zosurabalpin. Furthermore, the potential of phage therapy, as well as deep learning and artificial intelligence, offer a complementary approach that could be particularly useful in cases where traditional strategies fail. The fight against A. baumannii is not confined to the microcosm of microbiological research or hospital wards; instead, it is a broader public health dilemma that demands a coordinated, global response.
Keyphrases
- acinetobacter baumannii
- artificial intelligence
- pseudomonas aeruginosa
- multidrug resistant
- drug resistant
- deep learning
- health risk
- public health
- healthcare
- machine learning
- adverse drug
- big data
- gram negative
- cystic fibrosis
- heavy metals
- convolutional neural network
- mental health
- klebsiella pneumoniae
- risk assessment
- drinking water
- mesenchymal stem cells
- human health
- emergency department
- current status
- escherichia coli
- dna methylation
- global health
- cell therapy