ABSTRACT
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
ABSTRACT
OBJECTIVES: Antibiotic therapy for Pseudomonas infections is becoming increasingly difficult. In this study, the transposons from two multidrug-resistant (MDR) clinical Pseudomonas strains containing related transposons responsible for giving rise to resistance determinants were characterised. METHODS: Two MDR clinical Pseudomonas isolates were obtained from a medical facility in Cyprus. The strains were identified as Pseudomonas putida C54 and Pseudomonas aeruginosa C69. DNA was extracted from both strains and was sequenced. Transposons were identified, annotated and compared with DNA sequences in GenBank. RESULTS: Two related nested transposons, here named Tn6608 (from P. putida C54) and Tn6609 (from P. aeruginosa C69), were characterised. The transposons are built on an ancestral Tn1403 base element (here named Tn1403A) that contains only the transposition module (tnpA and tnpR) and the associated cargo gene module (orfA, orfB, orfC and orfD) flanked by a 38-bp inverted repeat. The nested transposons identified in this study have evolved via acquisition of multiple transposons, adding multiple resistance genes to an ancestral transposon that originally lacked any resistance determinants. CONCLUSION: Transposons related to Tn6608 and Tn6609 have evolved and are globally disseminated. Of particular interest is that most of these nested transposons are located within the same site in a genomic island, providing alternative avenues for dissemination.