Correlates of Pancreatic Enzyme Replacement Therapy Intake in Adults with Cystic Fibrosis: Results of a Cross-Sectional Study.

Most people with cystic fibrosis (pwCF) develop pancreatic insufficiency and are treated with pancreatic enzyme replacement therapy (PERT). We aimed to describe the use of PERT and assess the correlates of PERT dose in adult pwCF. In a cross-sectional study at the Copenhagen CF Centre, the participants reported PERT intake, gastrointestinal (GI) symptoms and the use of concomitant treatments. Demographic and clinical characteristics were extracted from the Danish CF Registry. We used linear regression to assess the correlates of PERT dose per kg bodyweight (U-lipase/kg). We included 120 pwCF with a median age of 32.9 years, 46% women and 72% F508delta homozygote. The PERT dose ranged from 0 to 6160 U-lipase/kg per main meal (mean 1828; SD 1115). The PERT dose was associated with participants' sex (men vs. women: 661; 95% CI: 302; 1020 U-lipase/kg), age (-16; 95% CI: -31; -1 U-lipase/kg per year) and weight (-45; 95% CI: -58; -31 U-lipase/kg per kg). Having less frequent constipation and being lung transplanted were also associated with a higher PERT dose. A third of participants did not take PERT for snacks, and this was associated with the frequency of diarrhoea. These findings indicate that PERT intake may be improved to reduce GI symptoms.

Pseudomonas aeruginosa transcriptome during human infection.

Laboratory experiments have uncovered many basic aspects of bacterial physiology and behavior. After the past century of mostly in vitro experiments, we now have detailed knowledge of bacterial behavior in standard laboratory conditions, but only a superficial understanding of bacterial functions and behaviors during human infection. It is well-known that the growth and behavior of bacteria are largely dictated by their environment, but how bacterial physiology differs in laboratory models compared with human infections is not known. To address this question, we compared the transcriptome of Pseudomonas aeruginosa during human infection to that of P. aeruginosa in a variety of laboratory conditions. Several pathways, including the bacterium's primary quorum sensing system, had significantly lower expression in human infections than in many laboratory conditions. On the other hand, multiple genes known to confer antibiotic resistance had substantially higher expression in human infection than in laboratory conditions, potentially explaining why antibiotic resistance assays in the clinical laboratory frequently underestimate resistance in patients. Using a standard machine learning technique known as support vector machines, we identified a set of genes whose expression reliably distinguished in vitro conditions from human infections. Finally, we used these support vector machines with binary classification to force P. aeruginosa mouse infection transcriptomes to be classified as human or in vitro. Determining what differentiates our current models from clinical infections is important to better understand bacterial infections and will be necessary to create model systems that more accurately capture the biology of infection.