Antimicrobial resistance

Pallett et al. discuss the impact of human conflict on development of antimicrobial resistance. They overview approaches to limit the spread of antimicrobial resistance, using the ongoing conflict in Ukraine as an example of the challenges and opportunities.

van Dijk et al. discuss the potential for antimicrobial resistance as a consequence of disinfectant use. The authors advocate for the prudent use of disinfectants in all sectors of society.

Krockow et al present data from two surveys exploring public perception of antimicrobial resistance-related terminology from a linguistic perspective. They highlight the failure of current terminology to support public awareness and understanding of the global antimicrobial resistance challenge.

Hamilton et al. model the impact of a malaria vaccine on malaria cases, drug resistance, and deaths if administered yearly to infants in 42 African countries. They find that even a moderately effective vaccine could substantially reduce malaria burden, with a vaccine that maintains its efficacy over time being most impactful.

Schouls et al. characterize 43,321 methicillin-resistant Staphylococcus aureus (MRSA) isolates obtained between 2008 and 2019 in the Netherlands. Genomic changes occur in the MRSA population, with increases in the proportion of PVL-positive MRSA.

Tchesnokova et al. investigate Escherichia coli that are not susceptible to fluoroquinolones within fecal samples collected in 2015 and 2021 from women aged over 50. Despite reduced fluoroquinolone prescriptions during that time, increases are seen in gut carriage of fluoroquinolone-resistant uropathogenic E. coli.

Bah, Kujabi et al. characterise multi-drug resistant-Gram negative bacilli (MDR-GNB) carriage in small vulnerable newborns and their mothers at a low-resource African hospital. Many neonates acquire MDR and ESBL-GNB between birth and 7 days of age, but there is only limited evidence these are transmitted from the mothers.

Cherny et al. apply additive Bayesian network modelling to examine antibiotic cross-resistance in bacterial species obtained from urine, wounds, blood, and sputum. Patterns of cross-resistance differed across sample sources highlighting the importance of considering the sample source when assessing likelihood of antibiotic cross-resistance.

Mintz et al. develop machine learning models to predict the probability of ciprofloxacin resistance in hospitalized patients. Resistance of previous infections, prior location of patients, sex and recent resistance frequencies in the hospital impact the probability of ciprofloxacin resistance in each patient.

Schouls et al. characterize antimicrobial resistance genes in MRSA isolates from humans and livestock in the Netherlands. The multidrug resistance gene cfr and the phenicol resistance gene fexA are identified in both types of samples, including in samples taken from persons having professional contact with livestock.

Sukhum, Newcomer et al. evaluate reservoirs of antibiotic-resistant organisms within the built environment and patient samples from an established and a newly-built intensive care unit. The authors demonstrate colonization of sink drains and other sites and show relatedness between environmental reservoirs and patient infections.

Vendrik et al performed a prospective matched case-control study on the genomic epidemiology of colistin-resistant Escherichia coli and Klebsiella pneumoniae from Dutch patients. Colistin resistance is present, but uncommon in the Netherlands and caused by the mcr gene in a minority of colistin-resistant isolates.

Corbin et al. train machine learning models on electronic health record data to predict susceptibility of infections to particular antibiotics (personalized antibiograms). Antibiotic selection driven by personalized antibiograms achieves similar coverage rates to those seen in actual clinical practice using fewer broad spectrum antibiotics.

Liu et al. analyze phenotypic and genomic characteristics of clinical IMP-producing Klebsiella spp. Isolates in China. Multi-clonal transmission, multiple genetic environments and differing plasmid types all play a role in disseminating blaIMP genes among Klebsiella spp.