Quantifying patient- and hospital-level antimicrobial resistance dynamics in Staphylococcus aureus from routinely collected data.

Introduction. Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus. The reported prevalence of these resistances varies, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures.Gap Statement. The value and limitations of routinely collected hospital data to gain insight into AMR dynamics at the hospital and individual levels simultaneously are unclear.Methodology. We explored S. aureus AMR diversity in 70 000 isolates from a UK paediatric hospital between 2000-2021, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms and information on hospitalization and antibiotic consumption.Results. At the hospital level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25-50 %, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible S. aureus. Ciprofloxacin resistance in MRSA decreased from 70-40 % of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4 % of patients ever positive for S. aureus simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3 % of patients ever positive for S. aureus. These changes equally represented gain and loss of resistance.Conclusion. Within this routinely collected dataset, we found that 65 % of changes in resistance within a patient's S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.

Quantifying patient- and hospital-level antimicrobial resistance dynamics in Staphylococcus aureus from routinely collected data.

Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. We explored S. aureus AMR diversity in 70,000 isolates from a UK paediatric hospital between 2000-2020, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms, hospitalisation information, and antibiotic consumption. At the hospital-level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25 to 50%, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible S. aureus . Ciprofloxacin resistance in MRSA decreased from 70% to 40% of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4% of patients ever positive for S. aureus simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3% of patients ever positive for S. aureus . These changes equally represented gain and loss of resistance. Within this routinely collected dataset, we found that 65% of changes in resistance within a patient’s S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.

Assessment of ethanol autoxidation as a drop-in kerosene and surrogates blend with a new modelling approach.

Bioethanol has been considered as a more sustainable alternative for fossil fuels, and it has been used as a drop-in fuel mixture. In this paper, the autoxidation properties of real kerosene as well as single, binary and ternary surrogates with the presence of ethanol are investigated for the first time. A simplified python code is proposed to predict the pressure drop of the PetroOXY method that was used for assessing the fuel autoxidation properties. The experimental results show that the addition of an ethanol concentration reduces the induction period of real kerosene while increasing that of surrogate mixtures. Also, the maximum pressure during the PetroOXY test increases with the increase of ethanol concentration. The model is able to predict the induction period of ethanol accurately by employing an automated reaction mechanism generator. A strategy to increase the autoxidation stability of ethanol by adding 1 g/L antioxidant has been evaluated. The efficiency of the antioxidants for ethanol is in the following order: PY > Decalin > DTBP > Tetralin > BHT > MTBP > BHA > TBHQ > PG.