The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.

The aim of this study was to explore the phenomenon of identity change and subsequent post-traumatic growth (PTG) in individuals with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Ten participants (average illness duration 7.4 years) were interviewed (average length, 79 minutes) via a semi-structured interview schedule and verbatim transcriptions were analysed with interpretative phenomenological analysis. The four superordinate themes revealed were ‘comparisons of past to present self: “you have to be someone else, and you have to live with that’’’, ‘the effect of social isolation on identity and subsequent insights into others’ behaviours’, ‘contemplation of future and identity: ‘‘where do I go from here?”’, and ‘PTG: “the letting go, the building up, [and] the gradual process of rebuilding”’. These themes outlined the experiences of those with ME/CFS as they underwent changes in identity due to the limitations the condition imposed on activities and roles, understanding others’ behaviours after a period of isolation, the comparison of the past self with the present self and finally, the positive growth that was noted by two of the interviewees with regards to a new ‘true’ self. Despite the distressing and unpredictable nature of ME/CFS, it appears that individuals with this disorder can experience personal growth.

Resistance to piperacillin/tazobactam (TZP) in Escherichia coli has predominantly been associated with mechanisms that confer resistance to third-generation cephalosporins. Recent reports have identified E. coli strains with phenotypic resistance to piperacillin/tazobactam but susceptibility to third-generation cephalosporins (TZP-R/3GC-S). In this study we sought to determine the genetic diversity of this phenotype in E. coli (n=58) isolated between 2014–2017 at a single tertiary hospital in Liverpool, UK, as well as the associated resistance mechanisms. We compare our findings to a UK-wide collection of invasive E. coli isolates (n=1509) with publicly available phenotypic and genotypic data. These data sets included the TZP-R/3GC-S phenotype (n=68), and piperacillin/tazobactam and third-generation cephalosporin-susceptible (TZP-S/3GC-S, n=1271) phenotypes. The TZP-R/3GC-S phenotype was displayed in a broad range of sequence types, which was mirrored in the same phenotype from the UK-wide collection, and the overall diversity of invasive E. coli isolates. The TZP-R/3GC-S isolates contained a diverse range of plasmids, indicating multiple acquisition events of TZP resistance mechanisms rather than clonal expansion of a particular plasmid or sequence type. The putative resistance mechanisms were equally diverse, including hyperproduction of TEM-1, either via strong promoters or gene amplification, carriage of inhibitor-resistant β-lactamases, and an S133G bla _CTX-M-15 mutation detected for the first time in clinical isolates. Several of these mechanisms were present at a lower abundance in the TZP-S/3GC-S isolates from the UK-wide collection, but without the associated phenotypic resistance to TZP. Eleven (19%) of the isolates had no putative mechanism identified from the genomic data. Our findings highlight the complexity of this cryptic phenotype and the need for continued phenotypic monitoring, as well as further investigation to improve detection and prediction of the TZP-R/3GC-S phenotype from genomic data.